The Dynamics of Capital Flows and Property Returns:
A Disaggregated Analysis of Metropolitan Areas and Property Types
by
Jeffrey Fisher, David C. Ling, and Andy Naranjo*
February 2005
*Department of Finance, Insurance and Real Estate
Graduate School of Business Administration
Warrington College of Business
University of Florida
Gainesville, FL 32611-7168
Phone: (352) 392-0153
Fax: (352) 392-0301
E-mail: ling@ufl.edu
The Dynamics of Capital Flows and Property Returns:
A Disaggregated Analysis of Metropolitan Areas and Property Types
by Jeffrey Fisher, David C. Ling, and Andy Naranjo
Paper Summary
We propose to examine the effects of institutional capital flows into private commercial real
estate markets on property returns disaggregated by metropolitan area and property type.
Simultaneously, we will examine the effects of property returns on subsequent investor capital flows into
these metropolitan areas and property types. The dynamic relation between disaggregated, private
market capital flows and property returns will be estimated using state-of-the-art regression techniques.
The influence of interest rates and other national and MSA specific economic variables, such as
population growth and employment, will also be incorporated into the dynamic analysis. Unlike static
regression techniques, our dynamic model will produce estimates of both the short- and long-run
relationships among investor capital flows and returns. For example, if capital flows do impact property
returns, we will be able to determine if the effects are concentrated in the subsequent quarter or, instead,
incorporated into returns over multiple quarters. In addition, our disaggregated analysis will permit us to
examine the extent to which cross-sectional differences among MSAs in, for example, space market
supply elasticities affect the responsiveness of returns to increased capital flows.
Research Objective
Despite an emerging general finance literature on capital flows, asset values, and the role of
investor sentiment,1 the role of capital flows in real estate pricing dynamics has received relatively little
attention. Nevertheless, the seemingly pervasive view among real estate practitioners is that capital
flows, at least in part, predict returns. For example, in March 2004, the Morgan Stanley research team
stated that “fund flows are the only thing driving (REIT) returns higher.”2 Motivated by such practitioner
claims and a lack of rigorous research to support them, Ling and Naranjo (2003, 2005) produced the first
two rigorous analyses of the dynamics of real estate capital flows and returns. In their first paper (Ling
and Naranjo, 2003), the authors examine whether total equity flows into the REIT sector affect aggregate
REIT prices and returns and, simultaneously, whether past industry-level returns influence subsequent
1
See for example, Cha and Lee (2001), Choe, Kho and Stulz (1999), Edelen and Warner (1999), Edwards and Zhang
(1998), Fortune (1998), Froot, O’Connell and Seasholes (2001), Karceski (2003), Remolona et al (1997), and
Warther (1995, 1998).
2
“Will Fund Flows Buoy Valuations,” Real Estate Investment Trusts Industry Report, Morgan Stanley REIT Team,
March 2004.
1
capital flows into the REIT sector. In their second paper (Ling and Naranjo, 2005), the authors examine
the interrelationships and short- and long-run dynamics between capital flows to dedicated REIT mutual
funds and industry-level REIT returns. In summary, their work to date has produced strong 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.
The work of Ling and Naranjo has contributed to our understanding of the dynamics of capital
flows and returns in public real estate markets. However, a rigorous analysis of the dynamics of capital
flows and returns in private real estate markets has not yet been undertaken. This is a serious void in the
literature given that approximately 90 percent of investible commercial real estate in the U.S. is owned
and traded in private markets.3
The objective of the proposed research is to examine the short- and long-run dynamics among
institutional capital flows and property returns in the largest U.S. metropolitan areas. In particular, we
will examine whether capital invested in major metropolitan areas by members of the National Council
of Real Estate Investment Fiduciaries (NCREIF) is associated with higher, or lower, property returns in
subsequent periods. Simultaneously, we will examine whether quarterly MSA level returns, as measured
by NCREIF, are predictive of future capital investments by NCREIF members in these MSAs. For each
metropolitan area, this analysis will first be performed by aggregating MSA-level capital flows and
returns across all commercial property types. We will then examine the short- and long-run dynamics
among MSA level capital flows and returns disaggregated by the four major property types: office,
industrial, retail, and apartment.
The main tool of analysis we propose to employ is a vector autoregressive (VAR) regression
model. This regression technique allows estimation of standard regression coefficients as well as
impulse response functions for the variables of interest. These impulse response functions trace out the
effects over time that result from changes in capital flows and returns. The application of VAR
regression techniques will permit us to consider a number of interesting questions:
3
1.
What is the effect of a change in private capital flows on current and future property
returns, and what are the time series dynamics of this effect? That is, is the effect of a
shock to capital flows on returns temporary or is it permanent? If temporary, how
quickly does the return effect dissipate?
2.
How does the supply elasticity of the MSA affect both the short- and long-run effects of
capital flows on returns? Some recent evidence documents that private real estate returns
have been significantly higher in supply constrained markets than in markets with more
elastic supply responses (i.e., “commodity” markets). However, despite the belief among
many practitioners that investments in “constrained” markets are preferred to
See Ling and Archer (2005), Chapter 15, page 387.
2
investments in commodity markets, the proposed research will produce the first careful
analysis of this issue.
3.
Do past MSA-level returns to institutional investors influence their subsequent capital
flows into these markets after controlling for the potential effects of other variables?
Institutional real estate investors have been labeled by some observers as “herd”
investors. The proposed research will provide extensive documentation on the extent to
which institutional capital flows in quarter t are predictive of flows in subsequent
quarters.
4.
Are there other variables that affect the dynamic relation between institutional capital
flows and property returns at the MSA level? In addition to past returns, the general
literature on capital flows suggests other variables such as interest rates and
capitalization rates may affect capital flows. We will also experiment with other
macroeconomic variables such as MSA level changes in population and employment.
Knowledge of the factors that influence private capital flows into MSAs and various property
types, and the effects these flows have on both short- and long-run returns, should prove to be extremely
valuable to commercial real estate practitioners in making investment and portfolio allocation decisions.
Research Methodology
The more recent research which investigates the dynamics of capital flows and returns employs
structural vector autoregressive (VAR) and vector error-correction (VEC) frameworks that also include
additional exogenous variables. In our analysis, we propose to use VAR and VEC models to examine the
relationships among institutional investor real estate capital flows and property returns at the aggregate,
MSA, and property type levels. The models will also include various national and MSA level control
variables such as equity market returns, interest rates, unemployment, and other macroeconomic state
variables. By separately examining the aggregate, MSA, and property types, we will be able to
disentangle the influences that location and property type have in the conditional dynamics of
institutional capital flows and returns.
Several of the variables that we propose to investigate (e.g., institutional real estate capital flows)
are likely to be non-stationary.4 If so, single-equation time-series techniques may result in misleading
(i.e., spurious) values of inferential statistics (i.e., t-statistics, R2, and DW) that may erroneously cause
one to conclude that a meaningful relation exists among the regression variables. To address these
4
In general, a series is non-stationary if its mean, autocovariances, or other higher moments are time dependent.
For example, if the mean of a series varies with respect to time, it is likely to be non-stationary. Simply stated, the
test for a unit root (i.e., non-stationarity) in a time-series is the test that a regression of a series on itself lagged one
period yields a coefficient of one. This test is complicated by several features arising from the non-stationarity of the
series under the null hypothesis. In a well-known paper, Dickey and Fuller (1979) suggest a method for computing a
test for a unit root in a time-series.
3
potential non-stationarity problems, we will also examine vector error-correction (VEC) models in our
investigation, which are designed for use with non-stationary time series (see, for example, Hamilton,
1994).
To better understand the VEC framework, it is useful to first briefly describe a vector
autoregression (VAR) model. In its simplest form, a VAR model is composed of a system of regressions
where a set of dependent variables are expressed as linear functions of their own and each other’s lagged
values, and possibly some other independent variables. For example, consider the following twovariable, one-period lag VAR model:
Yt = a1 + b1Yt-1 + c1Zt-1 + e1t
Zt = a2 + b2Zt-1 + c2Yt-1 + e2t.
Simultaneous VAR models have proven successful for forecasting systems of interrelated time series
variables (see Sims, 1980). Using more technical terminology, a vector autoregression model is the
unconstrained reduced form of a dynamic simultaneous equations model. A problem, however, arises
with the VAR framework if the variables in the system are non-stationary.
The VEC model is a more appropriate model than the VAR if the variables in the VAR system
are integrated of order one or more (i.e., are non-stationary). A group of non-stationary time series is
cointegrated if there is a linear combination of them that is stationary. These cointegrating relations are
incorporated into the VEC. For example, consider the following two-variable VEC with non-stationary
time series:
Yt = a1 + b1Yt-1 + c1Zt-1 + 1(Yt-1 - Zt-1) + e1t
Zt = a2 + b2Zt-1 + c2Yt-1 + 2(Yt-1 - Zt-1) + e2t,
where all terms involving  (i.e., first differences) are stationary. This two-variable error correction
model is a bivariate VAR in first differences augmented by the error-correction terms 1(Yt-1 - Zt-1) and
2(Yt-1 - Zt-1) from the cointegrating relation. In general, the kth order vector error-correction model can
be represented by the following system:
Xt    1Xt  1   2Xt  2  ...  k  1Xt  k  1  Xt  k  et ,
where
Xt = vector of p I(1) variables,
 = p x 1 vector of intercepts,
1, 2, k,  = p x p matrices of parameters,
et = error term [ NID(0,)],
 = difference operator,
I(1) = integrated of order one (i.e., first-difference stationary).
4
In the above system, the coefficient matrix  provides information about the long-run relations among
the variables, while the ’s provide information on short-run relations. Using Johansen’s (1988) method,
we can obtain estimates of the long-run relationships, short-run relationships, impulse response functions,
and forecast variance decompositions. The impulse response functions provide the time path of the
short-run dynamic relationships from a shock to the variables in the system, while the forecast variance
decompositions provide the forecast error variance explained by variations in the variables.
In our analysis, we will estimate the above constrained system or an unconstrained VAR version
if the variables in the system are non-stationary and cointegrated. We will estimate the models over the
1978-2004 sample period using quarterly data at the aggregate level (pooled cross-sectional times series)
and at the disaggregate level by MSA and property type.
Data
Our sample period will begin in the first quarter of 1978 and end in the fourth quarter of 2004.
We are currently gathering aggregate, MSA, and property type (industrial, office, retail, and multifamily) quarterly data from NCREIF. For each of the data types, we are collecting the total return,
appreciation return, market value, and implied capital flow. The implied capital flow is based on the
extent to which NCREIF members have shifted the relative amount of capital they have invested in
different MSAs and property types over time. Each quarter the aggregate market value of properties in
the NCREIF Property Index (NPI) in a particular property type and MSA changes for several reasons
including: 1) capital appreciation return in the property sector and MSA, 2) net flow of funds into the
MSA from acquisitions less dispositions, and 3) properties added to the database from new members.
We remove the effect of capital appreciation from the net change in market value so that we only capture
the change due to a flow of funds into the property sector and MSA from net acquisitions or properties
added by new members. We than calculate the net capital flow on a relative basis by dividing by the
total amount of capital in the NPI during the same quarter to adjust for the change in market value over
time due to adding new members.
What we are interested in is the changes in the relative amount of capital in each property sector
and MSA. For example, properties may be sold in Dallas and properties purchased in San Francisco
during a particular quarter. This changes the relative amount of capital in San Francisco versus Dallas.
It is this relative change in the propensity to invest in different property sectors and MSAs that we use as
a proxy for capital flows from institutional investors. To address the concern that NCREIF’s NPI return
data suffer from appraisal smoothing and lagging, we will test the robustness of our results by using
5
NCREIF’s Current Value Index (CVI) in place of the NPI and by experimenting with several
unsmoothing techniques.
At the MSA level, we are also gathering available economic variables, including unemployment
and other variables. These additional MSA level state variables will allow us to also control for relative
regional effects. National level data including stock returns, interest rates, and other fundamental
macroeconomic data are available from CRSP and the DRI/Citibank databank, both of which have been
purchased by the University of Florida.
References
Cha, H.S. and B. Lee, 2001, “The Market Demand Curve for Common Stocks: Evidence from
Equity Mutual Fund Flows,” Journal of Financial and Quantitative Analysis 36(2): 195-220.
Choe, H., B.C. Kho, and R. Stulz, 1999, “Do Foreign Investors Destabilize Stock Markets? The
Korean Experience in 1997,” Journal of Financial Economics 54(2): 227-264.
Dickey, D. and W. Fuller, 1979, “Distribution of the Estimates for Autoregressive Time
Series with a Unit Root,” Journal of the American Statistical Association, pp. 427-431.
Edelen, R. M. and J. B. Warner, 2001, “Aggregate Price Effects of Institutional Trading: A Study
of Mutual Fund Flow Data and Market Returns,” Journal of Financial Economics 59(2):
195-220.
Edwards, F. R., and X. Zhang, 1998, “Mutual Fund Flows and Stock and Bond Market Stability,”
Journal of Financial Services Research 13(3): 257-282.
Fortune, P, 1998, “Mutual Funds, Part II: Fund Flows and Security Returns,” New England
Economic Review, Federal Reserve Bank of Boston, Jan/Feb: 3-22.
Froot, K.A., P.G.J. O’Connell, and M.S Seasholes, 2001, “The Portfolio Flows of International
Investors,” Journal of Financial Economics 59(2): 151-194.
Hamilton, J. D., 1994, Time-series Analysis, Princeton Press.
Johansen, S., 1988, “Statistical Analysis of Cointegrating Vectors,” Journal of Economic
Dynamics and Control, pp. 231-254.
Karceski, J., 2002, “Returns-Chasing Behavior, Mutual Funds, and Beta’s Death,” Journal of Financial
and Quantitative Analysis 37(3): 559-599.
Ling, D. C. and W. Archer, Real Estate Principles: A Value Approach, McGraw-Hill Irwin, Burr Ridge
Illinois, 2005.
Ling, D.C. and A. Naranjo, 2003, “The Dynamics of REIT Capital Flows and Returns,” Real Estate
Economics 31(3): 405-436.
6
Ling, D.C. and A. Naranjo, 2005, “Dedicated REIT Mutual Fund Flows and REIT Performance,”
Journal of Real Estate Finance and Economics, forthcoming.
Remolona, E.M., P. Kleiman, and D. Gruenstein, 1997, “Market Returns and Mutual Fund Flows,”
Federal Reserve Bank of New York Policy Review 3(2): 33-52.
Sims, C., 1980, “Macroeconomics and Reality,” Econometrica, pp. 1-49.
Warther, V.A., 1995, “Aggregate Mutual Fund Flows and Security Returns,” Journal of Financial Economics
39, pp. 209-235.
Warther, V.A., 1998, “Has the Rise of Mutual Funds Increased Market Instability?” Brookings Papers on
Financial Services, The Brookings Institution.
7