Real Estate Investments, Product Market

Competition, and Stock Returns

Moussa DiopUniversity of Wisconsin-Madison

April 16, 2017

Abstract

By limiting operating flexibility, real estate investments are found to increase firm risk, thus

expected returns. This study introduces product market competition as a critical determinant

of the relation between real estate investments and stock returns. As part of capacity strate-

gies, these investments are generally associated with increased market power and lower cash

flow volatility in oligopolistic industries. I present a simple model of oligopolistic competition

showing a negative relation between real estate holdings and firm beta, and empirically con-

firm this prediction. Controlling for product market competition enhances identification of the

endogenous relation between real estate investments and stock returns.

JEL Classification: G11, G31, L11, L13

Keywords: Real Estate Investments, Stock Returns, Product Market Competition, Return Volatil-

ity, Oligopolistic Competition

Introduction

“The company generally owns the land and building or secures long-term leasesfor restaurant sites, which ensures long-term occupancy rights and helps controlrelated costs... The company identifies and develops sites that offer convenience tocustomers and long-term sales and profit potential.” McDonald’s Corporation

As this quote from McDonald Corporation’s 2011 annual report shows, securing long-term rights

to real estate may be of strategic importance for some firms, particularly those operating in real

estate intensive industries.1 The amount and type of real estate a firm owns reflect industry and

firm characteristics as well as location and general market conditions. This study explores the

impact of product market competition on the relation between real estate investments and stock

returns. More specifically, I show that pricing power derived from capital (real estate) investments

in oligopolistic industries reduces cash flow volatility, resulting in a negative relation between real

estate and stock returns.2 This study complements and extends Tuzel (2010), who does not consider

industry structure, by highlighting a potential benefit from real estate investments in oligopolistic

industries. This study assumes industry structure as exogenous. In contrast, Ambrose et al. (2016)

examine the endogenous interaction between capital investments and industry structure under

demand uncertainty, and incidentally note the possible effect of demand volatility on the volatility

of firm value as industry concentation increases.

Capital investments generally involve a sizable real estate component. By altering financial and

operating leverage, growth options, and a firm’s ability to benefit from positive economic shocks,

these investments certainly affect stock returns (Berk et al. (1999); Carlson et al. (2004); Cooper

(2006); Kogan and Papanikolaou (2013)). As shown by Tuzel (2010), frictions in capital adjustment

mechanisms, such as limited investment reversibility, may prevent firms from maintaining optimal

levels of capital stocks in the event of adverse market shocks, hence leading to a positive relation

between capital assets (real estate) and stock returns.3 Though silent about firm risk, the industrial

1Firm specificity of capital investments tied to the real estate is also a key determinant of the length and type oftenure that management decides to maintain on that real estate (Fisher (2004)).

2In this study, competitive industries refer to industries where firms are price takers. In contrast, firms operatingin oligopolistic industries enjoy some pricing power. The terms oligopolistic industries (oligopolies) and concentratedindustries have the sane meaning and are used interchangeably in this paper.

3The real estate literature reviewed later has also explored with various degrees of success the relation betweenreal estate ownership and stock returns (Deng and Gyourko (1999)), systematic risk (Brounen and Eichholtz (2005)),production uncertainty (Zhao and Sing (2015)), or demand uncertainty ( Ambrose et al. (2016)).

1

organization literature has extensively studied and identified benefits from capital investments for

oligopolies in terms of pricing power, market protection, and cash flow stability (e.g., Wenders

(1971); Spence (1977); Eaton and Lipsey (1980)). In summary, the net effect of capital investments,

including real estate, on firm risk likely depends on market structure. I argue that the relation

between real estate (capital) investments and stock returns is positive in competitive industries, but

non-positive in oligopolistic industries due to stronger pricing power and lower cash flow volatility

derived from these investments.

The real estate owned by non-real estate firms generally consists of real property assets used in

their production activities.4 Production and marketing strategies normally drive these investments,

which affect operational and financial risks. Despite its bulkiness, illiquidity, limited reversibility

in the short term, and large asymmetric adjustment costs, real estate represents roughly 28% of

corporate balance sheets in 2010 (Ambrose et al. (2016)), consisting largely of production facilities,

warehouses, office buildings, land, and retail outlets. Figure 1 shows that the real estate intensity

of corporations from 1970 to 2010. Given these undesirable attributes of real estate that increase

firm risk, why do firms still own a considerable amount of real estate?

This study approaches real estate as a factor of production, but a factor different from most

inputs since its short-term inflexibility makes it hard for firms to maintain an optimal level in

response to demand shocks.5 I argue that product market competition is a determining factor in

the type and amount of real estate that firms own. Capacity and output decisions are important

strategic tools available to firms operating in oligopolistic markets. Moreover, capacity expansions

generally involve a substantial real estate component. Dixit and Pindyck (1994) note that real

estate investments provide firms with options to grow production. But as noted by Grenadier

(2002) and Novy-Marx (2007), competition erodes and may even eliminate the value of growth

options. In competitive industries, capacity-increasing investments are likely to yield minimal

economic benefits since firms have little to no market power. Capacity investments will generally

increase cash flow, unless the additional capacity is left idle for strategic reasons. However, it is

4Non-real estate firms are firms whose primary business is not directly related to real estate development, invest-ment, management, or financing. The vast majority of these firms acquire or lease real estate to secure an inputresource (similar to labor or equipment) to meet future production objectives.

5It is recognized that some firms may approach real estate investments from a wealth maximization perspective.This dimension of corporate real estate investments is not addressed in this study.

2

unlikely that capacity investments lower cash flow volatility in competitive markets since all firms

are price takers. Consequently, firms undertaking real estate investments in competitive industries

will be burdened by the undesirable attributes of real estate and their inability to affect output

prices will make them more vulnerable to adverse market shocks. As a result, investors will view

these firms as riskier and will therefore require higher returns.6

In contrast, the effect of real estate investments on firm risk in concentrated (oligopolistic) indus-

tries is somewhat more favorable. In this operating environment, capacity investments aimed at

creating, consolidating, or protecting economic rents (i.e., profit margins) may stabilize cash flows.

Capacity investments may also lead to stronger product market presence and enhanced bargaining

power relative to suppliers. Peress (2010) shows that firms can use their market power to pass on

shocks to customers, thereby insulating profits. Thus, capacity investments may act as a hedge

against negative supply shocks by allowing firms to transfer these shocks to customers to the extent

permitted by the elasticity of demand or to negotiate more favorable pricing terms from suppliers

than smaller competitors can. Again, these strategies would only succeed in oligopolistic industries

since competition erodes market power. Consequently, it is possible that these economic benefits

from real estate investments in oligopolistic markets can temper or even overwhelm the associated

undesirable effect on firm risk discussed earlier.

To formally explore this theory and motivate the empirical analysis I propose a simple partial

equilibrium model of competition in an oligopolistic output market characterized by heterogeneous

firms. Firms use capital assets (real estate) and a fully depreciating input to produce a homoge-

neous final consumption good. They face asymmetric adjustment costs for capital investments and

use a Cobb-Douglas production technology. A detailed description of the model follows later. Since

the model cannot be solved algebraically, I carefully choose the parameters following the litera-

ture, simulate the firms’ investment and production decisions, and compute their average capital

investment characteristics and betas. These simulations reveal a negative relation between firm

beta and the ratio of capital assets (real estate) to the other input (Figure 2). Furthermore, the

graphs in Figure 2 show that the slope of the relation between real estate and firm risk flattens

as the number of firms increases, but remains non-positive. The fewer the number of competing

6Higher firm risk prompts investors to require higher returns, which ex post translate into a positive relationbetween real estate and observed returns.

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firms (i.e., the higher industry concentration), the greater a firm’s pricing power and the steeper

the negative slope of the relation between firm beta and real estate investments.

Next, I empirically test this prediction by examining the return characteristics of a large sample of

public non-real estate corporations from 1973 to 2010 using a portfolio approach. I show that the

positive relation between real estate investments and stock returns documented in the literature is

specific to competitive industries. As predicted by the model, the relation between real estate and

stock returns turns negative in concentrated industries, as confirmed by large negative returns on

a synthetic long real estate position capturing the difference in returns between the high and low

real estate firms. In contrast, a similar synthetic long real estate position in competitive industries

produces positive returns, confirming the positive effect of real estate on firm risk documented by

Tuzel (2010) since most firms operate in competitive industries. Furthermore, I show that the

positive (negative) relation between real estate and stock returns in competitive (concentrated)

industries persists after controlling for conventional risk factors. These findings are also robust

to various measures of real estate and industry concentration. Since market efficiency normally

precludes consistent abnormal returns, these results imply the pricing of some risk factor(s) not

captured in the pricing model used to estimate abnormal returns. Finally, real estate appears to

alter exposure to the conventional risk factors included in the model in a similar manner in both

market structures.

Obviously, intense competition requires nimbleness at all levels of the firm. At the extreme, firms

facing cut-throat competition may be better off contracting out production in some cases. But this

cannot be true in all competitive industries, especially for oligopolies, since a firm’s competitive

advantages may be tied to firm-specific assets tied to real estate. These findings do not mean that

real estate investments are good or bad for shareholders in competitive or concentrated industries.

Real estate assets affect firm risk, thus ultimately altering the category of investors attracted to

these firms.

Possible endogeneity between real estate investments and returns makes identification of the true

relation difficult. Some business activities require more real estate than others (e.g., car manufac-

turing vs. computer production, or retail vs. business services). At the firm level, capital structure

may affect real estate decisions, which in turn feed back into capital structure and returns. Con-

4

trolling for product market competition permits a partial disentangling of the endogenous relation

between real estate investments and stock returns.7 I show that the findings persist in the firms’

industry-adjusted returns. In summary, corporate real estate strategies should consider indus-

try structure. Renting may be preferable from the perspective of firm risk for firms operating in

competitive markets. But the lower cash flow volatility associated with real estate ownership in

oligopolistic industries may yield significant benefits.

This paper proceeds as follows. Next, I review the relevant literature. The third section presents a

model of oligopolistic competitive equilibrium to motivate the study. The fourth section describes

the research methodology and data, followed by a discussion of the main empirical results and

various robustness checks. Finally, the last section concludes.

Related Literature

Several studies have examined the equilibrium relation between real estate investments and stock

returns. Deng and Gyourko (1999) document a weak negative relation between abnormal stock

returns and real estate investments for industrial firms. In contrast, Michael et al. (2001) and

Brounen et al. (2005) find no significant link between real estate and abnormal returns. But

both studies show that real estate has a significant negative effect on systematic risk, which is

not surprising since real estate generally has a low beta. The inconclusiveness of these studies

regarding the relation between abnormal stock returns and real estate may be due to endogeneity

issues. Also, the use of property, plant, and equipment (PPE) to total assets (TA) as a proxy for

real estate may be problematic. Deng and Gyourko (1999) find this ratio has a low cross-industry

variance. To avoid potential identification problems, several authors focus on retail firms, given the

importance of real estate in that industry, and generally document a positive relation between real

estate investments and stock returns (Brounen and Eichholtz (2005); Brounen et al. (2005); Yu and

Liow (2009)). Unfortunately, this finding is not necessarily generalizable to other industries.

7A simpler approach to dealing with this endogeneity issue is to restrict the analysis to one industry (Brounenand Eichholtz (2005); Yu and Liow (2009); Ling et al. (2012)). However, it is difficult to make general statementsabout the relation between real estate and stock returns from these studies.

5

Tuzel (2010) takes a broader approach in examining the relation between real estate investments

and stock returns. Using a portfolio formation methodology, which I adopt in this study, that

study finds that real estate increases firm risk resulting in a positive relation with returns. Tuzel

argues that the positive effect of real estate on firm risk stems from the operating inflexibility,

low depreciation rate, and significant asymmetric adjustment costs associated with real estate as

a factor of production. Somewhat similarly, Zhao and Sing (2015) examine corporate real estate

ownership decisions in the presence of productivity uncertainty and show that high-risk firms are

expected to hold less real estate assets in order to reduce potential operating losses in the event

of negative productivity shocks. This documented positive relation between real estate and stock

returns normally implies the pricing of some risk directly or indirectly associated with real estate.

Ling et al. (2012) empirically examine the pricing of real estate risk for retail firms by examining

the sensitivity of firm market and real estate betas to real estate investments. They document

a positive relation between firm real estate betas and real estate investments, but unlike other

studies (e.g., Deng and Gyourko (1999)) they find that real estate has no effect on market beta.

In summary, this literature generally documents a positive relation between real estate and stock

returns, although real estate appears to reduce firm systematic risk.

The above literature does not consider the possible role of product market competition. Relying on

the industrial organization literature on barriers to entry, Ambrose et al. (2016) explores the effect

of demand uncertainty on the endogenous interaction of strategic capital investment, industry con-

centration, and incidentally volatility of firm value. As predicted by Spence (1977), Dixit (1980),

and Tirole (1988), Ambrose et al. (2016) show that capital investments affect industry concentra-

tion by deterring entry. Furthermore, they document that demand volatility decreases capacity

investments and increases the volatility of firm value as concentration increases. But capital invest-

ments also provide other benefits in oligopolistic industries in addition to entry deterrence. Gaspar

and Massa (2006) note that market power reduces information uncertainty for investors and thus

dampens return volatility. Peress (2010) shows that firms can use their monopoly power to pass

shocks on to customers, thereby protecting profits and reducing cash flow volatility. Aguerrevere

(2009) shows that the effect of competition on systematic risk is conditional on demand at the

industry level, but systematic risk generally increases with competition even if installed capacity is

6

not sufficient to accommodate current demand, for which Hou and Robinson (2006) present sup-

portive empirical evidence. Finally, Irvine and Pontiff (2009) identify product market competition

as a driver of the increase in firm-level volatility documented by Campbell et al. (2001).

Undoubtedly, product market competition affects strategic operating decisions, which in turn im-

pact the riskiness of operating cash flows. The complicated interaction between capacity decisions

(real estate investments), product market competition, and stock returns is the primary focus of

this study. In contrast to Ambrose et al. (2016), I assume that industry structure is exogenous,

do not model demand volatility, and focus on oligopoly pricing power and its effect on cash flow

volatility, rather than on entry deterrence. But similar to Tuzel (2010) and Ambrose et al. (2016)

I view real estate investment from a technological perspective.8

Model

The following model describes an oligopolistic output market in which firms elect whether to invest

in capital assets (real estate) every period and use a fully depreciating input resource (e.g., raw

materials, labor, or another form of fully depreciating capital) to produce a homogeneous final

consumption good. The model of production and asset pricing borrows somewhat from Tuzel

(2010) while the equilibrium concept known as extended oblivious equilibrium fully detailed in

the equilibrium concept and compution section that follows is taken from Weintraub et al. (2010).

Whereas the model in Tuzel (2010) is a general equilibrium model that assumes perfect competition

in the output market, this model is a partial market equilibrium model in which the consumer’s

decision problem is not explicitly modeled. But as a important differentiating characteristic of

this model, firms are allowed to have pricing power based on capacity installed in an oligopolistic

competition setting. I allow heterogeneity in production capacity, which leads to heterogeneity in

market power.

8Ambrose et al. (2016) classify real estate into strategic and general-purpose real estate. It must be stressed thatthe argument advanced in this paper applies more to strategic than general-purpose real estate. Figure 1 shows thereal estate owned by corporations is most likely firm-specific in nature. While there may be valid economic reasonsfor a firm to own strategic real estate assets, leasing may be the optimal solution for general-purpose real estate,particularly with the growth of the equity real estate investment trust (REIT) industry.

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Demand

The focus of this paper is the real estate investment decisions and stock returns for firms in an

oligopolistic industry of N firms that produce a homogeneous final good Qi,t with i ∈ {1, 2, . . . N}

and t ∈ {1, 2, . . .∞}. The aggregate demand for the final good in period t, Qt =∑N

i=1Qi,t, is given

by

Qt(Pt) = γ0ZtP−γ1t ,

where Pt is the good’s equilibrium market price, Zt is a market-wide demand shock, γ1 is the

elasticity of demand, and γ0 is a positive demand shifter. The inverse aggregate demand function

is then given by

Pt(Qt) =

(γ0ZtQt

) 1γ1

.

I assume that Zt, the market-wide demand shock, follows the AR(1) process in logs of the form

lnZt = ρZ lnZt−1 + ut, ut ∼ N(0, σ2

Z

),

with ρZ representing the persistence of the shock and ut representing the stochastic component of

the shock that is distributed IID normal with variance σ2Z .

Supply

I assume that the N firms are equity-financed. Firm i produces output Qi,t at the beginning of

period t using real estate capital Ki,t and the fully depreciating input Li,t. I assume that firms

must use the entirety of installed capacity to produce the final good. The output is given by the

Cobb-Douglas production function

Qi,t = F (Ai,t,Ki,t, Li,t) = Ai,tKαi,tL

1−αi,t .

Ai,t is a Hicks-neutral idiosyncratic productivity shock which also follows the AR(1) process in

logs

lnAi,t = ρA lnAi,t−1 + vi,t, vi,t ∼ N(0, σ2

A

)

8

with ρA representing the persistence of the shock. vi,t is the stochastic component of the shock

that is distributed IID normal with variance σ2A. I assume vi,t and vj,t are uncorrelated for any pair

(i, j) with i 6= j and that ut and vi,t are independent.

Real estate capital Ki,t is purchased in a perfectly competitive market and depreciates over time

at rate δ. The law of motion for capital is therefore

Ki,t+1 = (1− δ)Ki,t + Ii,t

where Ii,t is firm i’s capital investment at time t, which will be available for production at the

beginning of period t+ 1. This capital investment is subject to asymmetric quadratic adjustment

costs given by

g (Ii,t,Ki,t) =1

2η

(Ii,t)2

Ki,t

where

η =

ηlow if Iit > 0

ηhigh otherwise, with ηhigh ≥ ηlow.

The asymmetric adjustment costs capture the fact that it is more costly to decrease real estate

holdings than to expand them. Firms incur no adjustment costs during periods when investment

is zero.

Firms seek to maximize their expected present discounted value of profits by making a sequence of

investment and input purchasing decisions. Formally, firm i’s sequence problem is

max{Li,t,Ii,t}∞t=0

E0

∞∑t=0

ψtΠ

Qi,t,∑j 6=i

Qj,t, Zt

where ψ = 1

1+r and r is the real interest rate, which is exogenously determined since the consumer

problem is not modeled. Assuming that input markets are perfectly competitive so that firms

take input prices as given, firm i’s profit function at time t, which also represents the amount of

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dividends to shareholders since firms are equity financed, is

Π

Qi,t,∑j 6=i

Qj,t, Zt

= Pt(Qt) ·Qi,t − wLi,t − Ii,t − g (Ii,t,Ki,t) (1)

=

( γ0Zt

Ai,tKαi,tL

1−αi,t +

∑j 6=iQj,t

) 1γ1

Ai,tKαi,tL

1−αi,t − wLi,t

− (Ki,t+1 − (1− δ)Ki,t)−1

2η

(Ki,t+1 − (1− δ)Ki,t)2

Ki,t

where∑

j 6=iQj,t =∑

j 6=iAj,tKαj,tL

1−αj,t . Thus, firm i’s Bellman equation can be written as

V

Ki,t, Ai,t, Zt|∑j 6=i

Qj,t

= maxLi,t,Ki,t+1

ΠQi,t,∑

j 6=iQj,t, Zt

(2)

+ψE

VKi,t+1, Ai,t+1, Zt+1|

∑j 6=i

Qj,t+1

|Ai,t, Zt .

Asset Pricing

Firm i ’s consumption cost of capital (Tobin’s q) at time t from Equation 1 can be expresssed as

follows

qi,t = 1 + ηIi,tKi,t

.

Assuming the firm can liquidate its capital stock at this price, the value of firms equity at time t

(Ei,t) is then equal to the market value of its assets in place, namely

Ei,t = qi,tKi,t+1,

and the dividend firm i pays to shareholders is

Di,t = Π

Qi,t,∑j 6=i

Qj,t, Zt

.

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The rate of return for a firm’s equity shareholders is therefore

rei,t+1 = ln

(Ei,t+1 +Di,t+1

Ei,t

).

In this paper, the main objects of interest are the firm betas and their relation with firm real estate

capital holdings Ki,t. Let rvw,t+1 be the value-weighted average firm return (i.e., the aggregate

stock market return). Then firm i’s beta at time t is

βi,t =Covt

(rvw,t+1, r

ei,t+1

)Vart (rvw,t+1)

where the variance and covariance are calculated using the previous 100 periods.

Equilibrium Concept and Computation

The firms’ investment and input decisions cannot be solved algebraically. In order to determine the

relation between market structure, capital holdings, and firm betas, an equilibrium concept must

be chosen that is both computationally feasible and allows independent variation of the number of

firms N . To do so, the study adopts a slightly modified version of extended oblivious equilibrium

from Weintraub et al. (2010) that disallows firm entry. Conditional on today’s value of the aggregate

industry shock, each firm tracks the long-run average industry state. That is, rather than tracking

every other firm’s capital holdings and productivity and responding appropriately, each firm follows

a strategy that is “oblivious” in the sense that it responds to a hypothetical average firm and is

oblivious to the actions of any particular competitor. This equilibrium concept is explained in

greater detail in the appendix section. The equilibrium is computed using an algorithm described

in the appendix. After an equilibrium is found, a panel dataset of variables from the model is

simulated using the equilibrium strategies.

Parameterization and Results

The parameters of the model are not calibrated to match any particular industry. They are chosen

to be plausible as a real data generating process and closely match the parameter values in previous

11

empirical studies (Kydland and Prescott (1982); Cooley and Prescott (1995); Tuzel (2010)). Table

1 presents the chosen parameter values for the model. The persistence and standard deviation of

systematic demand shocks (ρZ and σZ) are set to 0.8 and 0.025, respectively. I set the demand

elasticity (γ1) to 2 and the demand shifter (γ0) to 25 in order to generate variation in capital

holding across firms. The capital share (α), persistence of idiosyncratic productivity shocks (ρA),

and standard deviation of idiosyncratic productivity shocks (σA) are 0.36, 0.8, and 0.05, respectively.

I assume a reasonable capital depreciation rate (δ) of 0.02 and, and set the low and high capital

adjustment costs (ηLow and ηhigh) to 0.8 and 1.6, respectively – I set the value of the high capital

adjustment cost equal to twice that of the low adjustment cost.9 Finally, I use a real interest rate

of 0.04, which is adequate for the period covered by the study, and normalize the price of the fully

depreciating input to 1.

Figure 2 displays results from simulations of the model. In particular, it shows scatter plots of

average firm betas and average capital-to-other-input ratios for firms sorted on their capital-to-

other-input ratio in each period for various industry sizes. In contrast to the results of the perfectly

competitive model presented in Table 3 of Tuzel (2010), this model of an oligopolistic industry

produces a slightly negative relation between relative capital holdings and firm risk as measured by

firm betas. Furthermore, this relation appears to weaken as the industry becomes more competitive;

the trend is very flat when there are 100 firms in the industry compared to when there are just 8

or 10 firms. Since firms with market power may face incentives to hold relatively illiquid capital

assets such as real estate capital in order to maintain their competitive position, investments in

such firms may be less risky than investments in firms with less productive capacity.

Empirical Analysis

Methodology

The design of the empirical analysis is as follows. First, I assign firms to industries every year

based on their SIC codes10 and estimate the level of competition in the industries. I compute the

9The results are unchanged when the ratio of high to low adjustment costs is set even higher10I check the robustness of the main findings to alternative SIC classifications and the allocation of firms to

industries according to Fama and French (1992) industries.

12

Herfindahl-Hirschman Index (HHI) of the industries as

HHIj =N∑i=1

s2ij (3)

where sij is the market share of firm i in industry j, which contains N firms. HHI is a measure

of market concentration. As is common in the literature, I estimate market share as the ratio of

firm sales to total industry sales. Therefore, a high HHI is indicative of a concentrated industry

dominated by few large firms, whereas a low HHI is indicative of a competitive industry. Since

changes in industry concentrations are likely to be gradual, following Hou and Robinson (2006)

I assign to each industry its three-year average HHI and classify the industries according to that

measure into concentration quintiles. The industries in the first quintile represent competitive

industries and those in the fifth quintile represent concentrated (oligopolistic) industries. Again,

I perform this classification annually. Next, I separately mix the firms in the competitive and

concentrated industry groups and sort each group of firms into decile portfolios according to the

amount of real estate on the firms’ balance sheets. I then track the performance of the portfolios

over the next 12 months before starting over the double sorting along industry concentration and

then real estate assets.

If product market competition has no effect on the relation between real estate investments and firm

risk, the behavior of portfolio returns relative to the firms’ real estate assets should be similar in

the competitive and concentrated industry groups. However, the model predicts that the relation

between returns and real estate will be non-positive in concentrated industries. First, I compare

the performance of the portfolios using average excess returns over the risk-free rate and average

industry-adjusted returns (i.e., average of the firms’ returns over their respective industry’s average

returns).11 If industry concentration and real estate are not determining return factors, and absent

any serious sampling problems, there should be no significant differences between the portfolio

returns in competitive and concentrated industries. Based on the findings of the existing literature,

I expect average portfolio returns to be positively correlated with real estate assets in competitive

industries. As argued, the story is more complicated in concentrated industries. Even though

11By controlling for average industry returns, the latter measure lead to a more accurate comparison of firms acrossdifferent industries.

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real estate investments still reduce operating flexibility in those industries due to the associated

asymmetric adjustment costs, the resulting increase in pricing power will weaken or even cancel

out that negative effect as shown by the model’s predictions.

Even though the finding of significant differences in the portfolios’ excess and industry-adjusted

returns suffices to conclude that real estate affects stock returns, it cannot be automatically inter-

preted as evidence that the market prices some risk associated with real estate investments. To

explore this further, the next step of the analysis considers whether the portfolios generate signif-

icant differences in abnormal returns after controlling for conventional risk factors and the extent

to which these abnormal returns are related to the amount of real estate owned by the portfolios’

component firms. The portfolios’ abnormal returns are estimated from monthly return series using

the following three-factor market model:

ri,t − rf,t = αi + βi(rm,t − rf,t) + γismbt + δihmlt + εi,t, (4)

where the dependent variable is the monthly returns of portfolio i (ri,t) minus the risk-free rate

(rf,t) and the pricing factors are monthly excess market returns (rm,t − rf,t) and monthly returns

on the Fama and French (1992) benchmark size and book-to-market portfolios, smbt and hmlt,

respectively. The scalar αi represents portfolio i ’s abnormal return (alpha) during the period. The

coefficients βi, γi, and δi represent portfolio i ’s systematic risk and loadings on the Fama-French

size and book-to-market risk factors, respectively. Finally, the vector εi contains the portfolio’s

return error terms.

If equation (4) properly accounts for all risk factors priced by the market, the estimated portfolio

abnormal returns should not be statistically different from zero. Otherwise, I cannot categori-

cally reject that product market competition, real estate ownership, or both affect stock returns.

However, the finding of insignificant abnormal returns does not contradict the main argument of

this paper either. Therefore, it is difficult to make definitive predictions about the relation be-

tween abnormal portfolio returns and real estate investments. However, I expect this relation to

be non-negative in competitive industries and non-positive in concentrated industries.

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Sample Selection

I use the Center for Research in Security Prices (CRSP) public company stock return data and the

corresponding Compustat accounting data for this empirical analysis. The initial sample consists

of U.S. firms with SIC numbers between 2000 and 5999 (i.e., industrial firms) with CRSP ordinary

share code 10 and 11 listed on the NYSE, AMEX, and Nasdaq between January 1970 and December

2010 that are contained at the intersection of the CRSP monthly stock return data series and the

merged Compustat annual industrial accounting database.12 Therefore, the sample excludes the

following industries: real estate investment trusts, construction, financial services, mining and oil,

agriculture, services, and healthcare.

I match the stock return and accounting data following Fama and French (1992) to ensure that

the accounting information used to create the portfolios is available prior to the stock return data

it is meant to explain and already impounded into stock prices. This is done by matching CRSP

monthly stock returns from July of year t to June of year t + 1 with accounting information for the

fiscal year t – 1, which results in stock returns lagging the release of the accounting data by at least

six months. Then, I assign firms to industries at the end of June every year according to the first

three digits of their Compustat SIC codes.13 Next, I compute the industries’ HHI using net sales as

explained earlier. I then drop industries with fewer than three firms, which eliminates monopolies,

and classify the remaining industries into concentration quintiles according to the industries’ three-

year average HHI. This three-year smoothing and the lagging of stock returns relative to accounting

data reduce the final sample to 37 years, spanning July 1973 through June 2010. Again, I perform

the industry classification at the end of June every year and keep the resulting industry quintiles

for the next 12 months from July through June.

Next, I separately classify the firms in the competitive and concentration industry quintiles into

ten portfolios each according to the amount of real estate assets in PPE at the end of June and

track the performance of the portfolios over the next 12 months.14

12I use 1970 as a starting date to reduce any potential bias toward large firms since Nasdaq firms were added toCRSP in 1973. Also, the Compustat PPE component accounts required for this analysis were sparsely populatedprior to 1970.

13Even though Compustat and CRSP SIC codes do not perfectly match (Kahle and Walkling, 1996), the outcomeof the analysis is unchanged when I use CRSP SIC codes for the industry classification. I also show that the resultsobtain when I use two-digit SIC codes or the Fama-French industries.

14Compustat breaks down PPE accounts into buildings, machinery and equipment, capitalized leases, land and

15

Descriptive Statistics

The final sample contains 7,736 firms classified into 171 industries according to their three-digit

SIC codes and consists of 71,885 firm-years, representing on average 1,943 firms per year. Table

2 summarizes the distributional characteristics of the industries’ annual and three-year moving

average HHI. I also report in Table 2 concentration figures based on total assets for comparison

purposes. Despite the exclusion of industries with fewer than three firms, the industries’ HHI vary

considerably, from 0.04 (indicative of a highly competitive industry) to 0.997 (reflective of a highly

concentrated industry). Table 2 also shows that the distributional characteristics of the four mea-

sures of industry concentration are very similar. Additionally, Figure 3 reveals that the distribution

of industry HHI is not constant over the sample period. The first moments of the distributions

vary considerably over time but remain interlocked, moving together over time. Hence, any of these

concentration measures should adequately capture the variation in industry concentration present

in the data and can be used for the classification of industries into quintile concentration groups.

As noted previously, I use three-year moving average sales HHI (HHI MA (sales)) in Table 2 and

throughout the rest of the paper as the main industry concentration measure.

The sample period witnessed two distinct waves of industry consolidation. The first and strongest

wave started in the mid-1970s and crested in the early 1990s. It resulted in average HHI increasing

by roughly 45% from 0.29 in 1976 to 0.42 in 1990. Also, the last decade witnessed a milder

surge in industry consolidation. The first wave coincided with a surge in hostile takeovers, partly

facilitated by an expansion of the junk bond market that collapsed as the economy went into

recession in the early 1990s (Carney (2009); Lipton (2006)). The latter wave was driven by a

number of factors, including globalization, a rise in commodity prices, low interest rates, shareholder

activism, hedge funds, and growth in private equity funds (Lipton (2006)). These exogenous shocks

to industry concentration should facilitate identification of the role of product market competition

in the relation between real estate investments and stock returns.

Industry Concentration Quintiles

improvements, construction in progress, natural resources, and other assets. Following Tuzel (2010), I use buildingsand capitalized leases, the largest components of PPE and the closest to production capacity, as the primary measureof real estate investments. Since Compustat only reports PPE accounts net of depreciations prior to 1985, I use netfigures for those years and gross figures after 1985 to compute real estate variables.

16

Table 3 summarizes the composition of the HHI-derived industry concentration quintiles, which

include 26 or 27 industries each. The average HHI of the concentrated industries group (the fifth

quintile) is roughly four times that of the competitive industries group (the first quintile): 0.66

versus 0.14.15 The difference between the competitive and concentrated industries is even more

pronounced at the firm level. The average HHI contribution16 of firms in the concentrated industries

is 30 times that of firms in the competitive industries. The difference in mean tests reported in the

last row of Table 3 unequivocally reject the null hypothesis of equal means between the two groups.

Figure 4 further highlights the difference in competitiveness between the two groups of industries.

The gap between the two curves tracing average HHI of the high and low concentration groups

never narrows over the study period. Furthermore, each series displays the two waves of industry

consolidation noted earlier, indicating that they were not limited to specific industries.17 Given the

pronounced structural differences between these two groups of industries, a comparison the effects

of real estate investments on stock returns in these industries should yield valuable insight into the

role of industry structure, if any.

Table 4 lists the average characteristics of firms in the competitive and concentrated industry

quintiles. As the number of firms drops with industry concentration, average firm size (measured

by sales, total assets, or market value of equity) predictably increases. Firms operating in con-

centrated industries are roughly twice the size of those in competitive industries. In addition,

book-to-market value increases with industry concentration; firms operating in competitive indus-

tries are more growth-oriented due to their constant need to undertake value-enhancing innovations

(Hou and Robinson (2006)). Brander and Lewis (1986, 1988) note that product market compe-

tition affects capital structure decisions. Both leverage and long-term debt ratios are positively

correlated with industry concentration. Since firms operating in concentrated industries are larger

and experience less cash flow volatility, they are able to sustain higher levels of debt (Jensen and

Meckling (1976); Myers (1984); Myers and Majluf (1984); Jensen (1986)). Table 4 clearly reveals

significant differences in size and capital structure between firms in competitive and concentrated

15For example, two representative industries of the concentrated and competitive industry groups are rubberproducts and restaurants, respectively.

16Firm i ’s contribution to industry j ’s HHI is s2ij in Equation (3).17Figure 4 shows that an alternative industry classification technique consisting of setting fixed HHI bands through-

out the sample period, rather than annually resetting these thresholds, would not materially alter the findings.

17

industries, as evidenced by the strong rejection of the null of equal mean values for all variables.

These differences certainly affect cash flows and should therefore be reflected in stock returns.

There is also a clear difference between the two groups of industries with respect to the amount

of real estate and other productive assets they own. The last four columns of Table 4 reveal an

interesting fact about the nature of the firms’ real estate investments. Columns 9 and 10 show that

firms operating in concentrated industries invest significantly more in buildings, land, and fixed

assets in general than those operating in competitive industries. Relatively, firms in competitive

industries are more likely to use leases (columns 7 and 8). Firms operating in concentrated industries

may be more reluctant to use leasing due to a higher proportion of firm-specific investments, stronger

cash flows, or both. Their higher capital intensity may also be a factor.

Real Estate Decile Portfolios

After classifying the sample into quintile industry concentration groups, I separately sort the firms in

the low and high quintile groups into decile portfolios according to the firms’ real estate investments.

Table 5 summarizes the characteristics of the firms in the first (low) and the tenth (high) real

estate decile portfolios of the competitive and concentrated quintile industries. As in Tuzel (2010),

I sort the firms into decile portfolios according to the ratio of buildings and capitalized leases

to PPE.18 Whether measured by sales, assets, or market value, the average size of the firms in

the tenth real estate decile is two to three times that of the firms in the first real estate decile

in competitive industries. The difference in average firm size between the first and tenth decile

portfolios is even more pronounced in concentrated industries where firms in the top decile portfolio

are on average more than 8 times larger in terms of sales. As noted previously, firms operating

in concentrated industries are generally larger, have higher book-to-market values, and are more

levered, as evidenced by the significant cross-industry difference in mean tests reported in Table

5.

A close comparison of the first three columns of tables 4 and 5 reveals an interesting fact about

the relation between firm size and real estate investments. These columns show that real estate

is positively correlated with firm size in concentrated industries. For example, the average firm

18This study’s main findings remain unchanged when I use alternative real estate measures as discussed later.

18

sales of $2.90 billion in concentrated industries (column 1 in Table 4) lies between the first decile

portfolio’s average firm sales of $900 million and the tenth decile portfolio’s average firm sale $3.06

billion (column 1 of Table 5). In contrast, firms in competitive industries do not display the same

monotonic pattern: the average firm sales of $1.28 billion for the industry group in Table 4 is outside

those of the first and tenth decile portfolios ($331 and $703 million) in Table 5. This non-monotonic

relation between real estate investments and firm size may be due to greater heterogeneity within

that group of industries. Table 5 also shows a striking difference in the portfolios’ real estate and

fixed assets (columns 7 to 10). Depending on the measure of real estate used, firms in the high

real estate decile have on average 8 to 22 times more real estate than those in the low real estate

decile in the competitive and concentrated industry groups, despite a much smaller difference is

fixed assets.

In summary, Table 5 highlights two important points. First, average characteristics of firms in

competitive and concentrated industries are significantly different; competitive industries largely

include growth firms. Second, the low real estate decile firms in both industry groups are smaller,

more growth-oriented, and less levered. These significant differences in firm characteristics between

the low and high decile portfolios should normally affect the portfolios’ performance.

Main Results

In this section, I present the performance of the real estate sorted portfolios in competitive and

concentrated industries. Without controlling for industry structure, Tuzel (2010) documents a pos-

itive relation between real estate and returns from 1971 to 2005. I argue that this positive relation

applies to competitive industries only. As shown in the theory section, real estate investments

may lead to greater pricing power and lower firm risk in oligopolistic industries. Consequently, I

expect a non-positive relation between real estate and returns in those industries. These predicted

relations should materialize in the portfolios’ returns.

Average Portfolio Returns:

Table 6 presents the portfolios’ average excess and industry-adjusted returns during the 37-year

period from July 1973 through June 2010. The portfolios’ average excess returns in competitive

19

industries in Panel A are positive and statistically significant. As predicted, they increase with

the portfolios’ real estate rankings from 4.7% for the low real estate decile to 10.6% for the high

real estate decile, representing a 5.9% average annual return difference between the two. Also,

the portfolios’ average industry adjusted returns trend positively with their real estate rankings,

resulting in a 4.8% return premium for the top decile portfolio. These results shown in the top-

left corner of Figure 5 corroborate Tuzel’s findings. Managers of firms operating in competitive

industries should be aware that significant real estate ownership is likely to increase firm risk due

to the associated decrease in operating flexibility.19 Yet the argument that all firms should avoid

real estate ownership regardless of the competitive environment may be shortsighted. Panel B of

Table 6 repeats the analysis with the portfolios of firms operating in concentrated industries. In

contrast to the previous results, both portfolio return series decrease with the portfolios’ real estate

rankings. The portfolios’ average excess returns drop from 11.3% for the low decile to 4.8% for the

top decile (shown in the top-right quadrant of Figure 5). Basically, high real estate firms appear

to be less risky in concentrated industries compared to competitive industries. The above portfolio

returns are value-weighted averages, but the findings are unchanged with equally-weighted average

returns (Table .1 of the appendix).

As noted, real estate investments affect capital structure and may therefore also alter stock re-

turns through that channel. Even though it is unclear how leverage may differently affect stock

returns in competitive and concentrated industry, it is important that I adjust portfolio returns for

leverage since the model assumes that firms are equity financed.20 Table 7 reports value-weighted

average unlevered excess and industry-adjusted returns of the decile portfolios in competitive and

concentrated industries. As expected, average unlevered portfolio returns are lower than the cor-

responding levered returns presented in Table 6, reflecting the positive effect of leverage on equity

returns (in bottom-left quadrant of Figure 5). For example, the average unlevered excess returns

19The fact that real estate ownership is associated with higher firm risk is not necessarily bad. As firms undertakesuch investments, they may become more attractive to different investor clienteles.

20I compute unlevered stock returns (rU ) using the following formula that assumes no taxes:

rU = rLD + E

E− rD

D

E, (5)

where rL and rD are levered stock return (return from CRSP in this case) and average cost of debt, respectively. Dand E stand for market value of debt and equity. I assume an average cost of debt of 7% across the board – theresults are robust to changes in the average cost of debt.

20

of the low and high decile portfolios in competitive industries are 2.6% and 6.7%, compared to

levered returns of 4.7% and 10.6%, respectively. Furthermore, the relation between average unlev-

ered returns and real estate investments remains positive and generally monotonic in competitive

industries (Panel A of Table 7). Despite controlling for leverage, the difference between the high

and low decile portfolios’ returns remain large and statistically significant. For example, the differ-

ence between the high and low portfolios’ average unlevered excess returns is 4.1%, though lower

than the 5.9% difference in levered returns. More importantly, the behavior of unlevered portfolio

excess and industry-adjusted returns in concentrated markets (Panel B Table 7 and bottom-right

quadrant of Figure 5) is similar to that of levered returns discussed earlier. The portfolios’ average

unlevered excess returns decrease from 8.2% to 3.0%, giving a difference unlevered excess returns

of -5.2% between the high and low decile portfolios, compared to a difference in levered returns of

-6.5%.

The unlevered portfolio returns generally confirm the conclusions derived from levered returns. The

portfolios’ returns increase with their real estate rankings in competitive industries and decline in

concentrated industries. Again, the results are unchanged when equally weighted returns are used.

However, controlling for leverage may improve identification of the potential role of product market

competition on the relation between real estate investments and stock returns. But it is important

to control for industry fixed-effects as well since some industries are more real estate intensive than

others. Consequently, it is comforting that unlevered industry adjusted returns reported in Panel

B of Table 7 support this study’s predictions.

Abnormal Portfolio Returns:

So far, I have largely abstracted from risk considerations. In this section, I explore whether the

documented difference in portfolio performance in competitive and concentrated industries also

materializes in abnormal portfolio returns. The finding of significant abnormal portfolio returns

after controlling for conventional pricing factors may reveal the channel through which product

market competition alters the relation between real estate and stock returns.

Table 8 reports abnormal portfolio returns from the estimation of model (4) using the portfolios’

monthly value-weighted unlevered excess returns. The abnormal returns of competitive industry

21

portfolios are generally significant (both statistically and economically) and increase from -3.1% for

the low real estate decile portfolio to 1.9% for the high real estate decile portfolio, a difference in

abnormal return of 4.9%. Table 8 also shows estimated abnormal portfolio returns in concentrated

industries, which are negatively related to the portfolios’ real estate rankings. The estimated ab-

normal returns of the low and high decile portfolios are 2.5% and -2.5%, respectively, resulting in

a significant difference in abnormal return of -5.1%. This significant negative relation between real

estate and stock returns in concentrated industries is an important finding, for it goes beyond the

weak prediction of a non-positive relation set out initially. Untabulated estimations using equally

weighted unlevered portfolio returns produce similar results in competitive and concentrated in-

dustries. These findings also obtain when I use levered excess returns (Table .2 of the appendix).

Therefore, these documented relations between abnormal portfolio returns and real estate invest-

ments are not driven by either leverage or exposure to the conventional risk factors included in

model (4).21

Finding significant abnormal returns does not necessary imply that the market prices some real

estate risk. It is possible that real estate ownership is correlated with an omitted risk factor. The

challenge is that any search for abnormal return is conditional on the pricing model and the data.

Assuming that the model is correctly specified and that there are no significant data issues, the

portfolios should normally produce insignificant average abnormal returns. I follow the literature

in terms of model selection, even running the risk of sacrificing efficiency by adopting a multi-

factor market model rather than the more parsimonious capital asset pricing model. And even

though the estimated abnormal returns could be data-driven, the long sample period of 37 years

and the relatively low risk of selection bias, given the sample size, provide some reassurance. Also

the conditioning of the portfolios on real estate seems reasonable since it is unlikely that investors

would disregard such information when pricing stocks while generally reacting to corporate real

estate acquisitions and disposals as documented by event studies (Hite et al. (1984); Slovin et al.

(1990); Glascock et al. (1991); Myer et al. (1992); McIntosh et al. (1995); Rodriguez and Sirmans

(1996)).

In summary, the evidence presented shows that real estate investments and stock returns are

21The results remain materially unchanged when I use a four-factor model adding a market momentum factor tomodel (4).

22

positively related in competitive industries and negatively related in concentrated industries. Since

the positive return effect in competitive industries appears to be statistically stronger and most

industries are relatively competitive, it is likely that this effect dominates in studies that do not

control for industry concentration.

Real Estate and Portfolio Risk-Factor Loadings:

The effects of real estate on stock returns is not restricted to the evidence from abnormal returns

discussed in the previous section. Real estate investments also affect exposure to conventional risk

factors. To capture these effects I estimate equation (4) using monthly returns on the high real

estate decile portfolio minus monthly returns on the low real estate decile portfolio, which can be

interpreted as a synthetic long real estate position. The regression coefficient estimates from this

high-minus-low real estate position in Table 9 provides valuable insights. Regardless of industry

structure, real estate investments appear to lower market beta and sensitivity to the size risk factor,

and to increase exposure to the book-to-market risk factor. These findings are quite intuitive. It has

been documented that real estate investment lowers market beta (Deng and Gyourko (1999)). Also,

the positive correlation between real estate and size may explain the negative size effect and positive

book-to-market effect of real estate. The fact that the directions of these effects transcend industry

considerations means that the opposing effects of product market competition on the interaction

between real estate investments and stock returns are captured in the portfolios’ abnormal returns.

It is thus conceivable that factors beyond those included in the model might explain the observed

return reversal.

Furthermore, a comparison of the portfolios’ estimated factor loadings (unreported) confirms that

firms operating in competitive industries have on average higher market betas, as predicted by

Aguerrevere (2009), and are more exposed to the size risk factor, which should normally lead to

higher returns overall as documented by Hou and Robinson (2006).

Robustness Checks

The previous portfolio returns apply to the 37-year period from July 1973 to June 2010. I assigned

firms to industries according to the first three digit of their SIC codes and I classified industries into

23

HHI quintiles and sort firms in the low and high quintile groups into decile portfolios according the

ratio of buildings and capitalized leases in PPE. In this section, I check that the study’s findings

are not specific to the selected sample period and are robust to alternative industry classification

methods and real estate measures.

The U.S. economy is still recovering from the Great Recession brought about by the collapse of the

housing market and resulting financial crisis. Although it is not clear how such a tail event toward

the end of the sample period might have affected the results, it is important to ensure that the

findings are robust to the exclusion of that period of relatively high uncertainty. Table 10 reports

the abnormal portfolio returns from July 1973 to June 2005, which almost coincides with the period

covered by Tuzel (2010). To save space, I only report the returns on the High–Low decile portfolios

which capture the effects of real estate as discussed earlier. As Table 10 shows, the results are

unaffected by the exclusion of the Great Recession. The difference in abnormal returns between

the high and low real estate decile portfolios is positive and significant in competitive industries and

negative and significant in concentrated industries. In fact, these results are even slightly stronger

than the base-case results, indicating that the inclusion of the real estate driven recession might

have biased the main findings downward.

The evidence presented so far results from the performance of real estate decile portfolios. But if the

argument advanced in this paper is correct, quintile portfolios should also provide some supporting,

albeit weaker, evidence since the difference in real estate holdings between the high and low quintile

portfolios is smaller relative to that of corresponding decile portfolios. The outcome of such an

analysis presented in column 2 of Table 11 confirms this prediction – column 1 reports High-Low

returns from Table 7. The differences in average excess and industry-adjusted returns between

the high and low real estate quintile portfolios are lower in absolute terms than the corresponding

differences in decile returns reproduced in column 1. Also, a comparison of the average returns on

the low and high quintile portfolios (not shown in Table 11) and the corresponding decile portfolio

returns further corroborates this study’s predictions. In competitive industries, the average return

on the low quintile portfolio is higher than on the low decile portfolio, while the corresponding

return on the high quintile portfolio is lower than on the high decile portfolio. This interesting

finding further confirms the positive relation between real estate and stock returns in competitive

24

industries. In contrast, the average return on the low quintile portfolio in concentrated industries

is lower than on the low decile portfolio, as expected, but the corresponding average returns on the

high quintile and decile portfolios are roughly similar, hence confirming the predicted non-positive

relation between real estate and stock returns in concentrated industries.

Table 11 also reports the difference in excess and industry-adjusted returns between the high and

low decile portfolios when firms are classified into industries according to their two-digit SIC codes

(Column 3) and the 48 Fama-French industries (Column 4), a widely used industry classifica-

tion method in finance studies.22 Overall, these results confirm the previous findings, However,

the Fama-French industry classification method produces stronger results than the two-digit SIC

industry classification method since it involves more industries. Surprisingly, the Fama-French clas-

sification method performs as well as the three-digit SIC classification used in the base case, even

though it results in a much smaller number of industry groups (48 versus 171). It is possible that

the Fama-French classification technique results in more homogeneous industry groups, which may

help facilitate identification. The last column of Table 11 gives differences in average returns when

I use total assets in lieu of net sales to compute industry concentrations. Since total assets and net

sales are highly correlated and share similar distributional characteristics as Table 2 and Figure 3

show, the results in column 5 are almost identical to the base-case results in column 1.

Table 12 reports excess and industry-adjusted returns under alternative measures of real estate

ownership. In the base case (column 1), firms are classified into portfolios according to RER1, the

ratio of buildings and capitalized leases to PPE. Using RER2, a broader measure of real estate that

adds construction in progress and land to RER1, does not significantly alter the results (column 2).

RER3, the ratio of buildings and land to PPE, also leads to similar results (untabulated). PPER,

the ratio of PPE to TA, however, appears to be a poor proxy for real estate, as this measure

produces negative but insignificant results in concentrated industries (column 3). Intuitively, the

more broadly that real estate is defined, the more serious the identification problem becomes,

leading to insignificant results. This may explain why Deng and Gyourko (1999) and other authors

who use this measure find weak or insignificant results. Measuring a firm’s real estate intensity in

terms of deviation from its industry’s average using ARER, which is equal to RER1 minus average

22The two-digit SIC classification results in 36 industry groups. The 48 Fama-French industries are publishedonline at http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data library.html.

25

industry RER1, in column 4 leads to slightly stronger results in competitive industries and confirms

the non-positive relation between real estate and stock returns in concentrated industries.

As Table 3 shows, firms in competitive and concentrated industries are significantly different,

particularly with respect to size: firms operating in concentrated industries are considerably larger.

Could this size difference between firms in competitive and concentrated industries explain the

findings? This study is not comparing portfolio returns in competitive industries to portfolio returns

in concentrated industries. Rather, it compares portfolio returns within each industry concentration

group separately. Therefore, the difference in average firm size between the two groups should be

immaterial. The documented relations are more about differences in the portfolios’ returns relative

to real estate assets than overall portfolio returns, which normally capture any size effect. Finally,

the findings materialize not only in average excess and industry-adjusted portfolio returns, but also

in abnormal portfolio returns generated from a pricing model controlling for the size risk factor.

The fact that these relations evidence themselves in abnormal returns represents strong evidence

that firm size is probably not a determining factor in the importance of product market competition

in the relation between real estate and stock returns.

Conclusion

This study extends the literature by introducing product market competition as a determining

factor in the relation between real estate investments and firm risk. Approaching these capital in-

vestments from a technology perspective and borrowing from the industrial organization literature,

I propose a model of capital investment decisions in oligopolistic industries and empirically show

that the relation between real estate investments and firm risk is non-positive to negative in that

environment. Therefore, the positive relation between real estate investments and stock returns

documented in the literature applies to competitive industries where firms enjoys no pricing power,

resulting in more volatile cash flows. The findings persist after controlling for capital structure

and adjusting returns for exposure to conventional risk factors. Managers must certainly consider

the competitive environment and their firms’ strategic objectives when considering real estate in-

vestments, particularly for firms operating in highly competitive industries. Capital markets being

26

relatively efficient at pricing risk, whether from real estate or other sources, the amount of real

estate assets owned by a firm ultimately affects the type of investors attracted to that stock.

I would like to thank Brent W. Ambrose, Charles Cao, Ed. Coulson, Austin Jaffe, and Jiro Yoshidafor helpful comments. Research assistance was ably provided by Dennis McWeeny. All errors aremy own.

27

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30

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

RE to TA(Market Value)

RE to TA (Book Value)

Non-residential RE to TA (Replacement Cost)

Figure 1: Ratios of real estate (RE) owned by non-farm non-financial corporations to total assets(TA). This real estate includes land and non-residential structures (production facilities, ware-houses, office buildings, land, and retail outlets (Source: Board of Governors of the Federal ReserveSystem)

31

8 firms 10 firmsSlope of line of best fit: -0.0026 Slope of line of best fit: -0.0050

.98

.99

11.

011.

02Av

erag

e be

ta

12 13 14 15 16 17Average capital-to-other-input ratio

.98

.99

11.

011.

02Av

erag

e be

ta

12 13 14 15 16 17Average capital-to-other-input ratio

15 firms 20 firmsSlope of line of best fit: -0.0009 Slope of line of best fit: -0.0015

.98

.99

11.

011.

02Av

erag

e be

ta

12 13 14 15 16 17Average capital-to-other-input ratio

.98

.99

11.

011.

02Av

erag

e be

ta

12 13 14 15 16 17Average capital-to-other-input ratio

30 firms 100 firmsSlope of line of best fit: -0.0019 Slope of line of best fit: -0.0002

.98

.99

11.

011.

02Av

erag

e be

ta

12 13 14 15 16 17Average capital-to-other-input ratio

.98

.99

11.

011.

02Av

erag

e be

ta

12 13 14 15 16 17Average capital-to-other-input ratio

Figure 2: Scatter plots of average firm betas against average capital-to-other-input ratios for firmssorted on their capital-to-other-input ratio in each period. Betas are computed using the previous100 periods and averages are taken over all 10,000 simulated periods.

32

0.000

0.050

0.100

0.150

0.200

0.250

0.300

0.350

0.400

0.450

0.500

1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

Median (Net Sales)

Mean (Net Sales)

Mean (Total Assets)

Figure 3: Mean and median of the sample’s three-year average HHI based on net sales and totalassets from 1973 to 2010

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

0.900

1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

Low-concentration industries

High-concentration industries

Figure 4: Mean three-year average HHI (based on net sales) of the sample’s competitive andconcentrated industry groups from 1973 to 2010

33

————————————————————————–

34

0

1

2

3

4

5

6

7

8

9

10

1 2 3 4 5 6 7 8 9 10

0

1

2

3

4

5

6

7

8

9

10

1 2 3 4 5 6 7 8 9 10

I I

II

(UL) (UH)

II

0

2

4

6

8

10

12

14

16

1 2 3 4 5 6 7 8 9 100

2

4

6

8

10

12

14

16

1 2 3 4 5 6 7 8 9 10

II

I I

II

(LL) (LH)

Decile-RE Portfolios Decile-RE Portfolios

Leve

red

Exce

ss R

etur

n (%

) U

nlev

ered

Exc

ess R

etur

n (%

)

Decile-RE Portfolios Decile-RE Portfolios

Leve

red

Exce

ss R

etur

n (%

) U

nlev

ered

Exc

ess R

etur

n (%

)

Figure 5: These are average decile portfolio returns. The two top quadrants (LL and LH) showaverage levered (L) excess portfolio returns in competitive (L: low concentration) industries andconcentrated (H: high concentration) industries, respectively. The bottom quadrants (UL and UH)show the average unlevered (U) excess portfolio returns in competitive (L) and concentrated (H )industries, respectively. The graphs labeled I (II ) show equally-weighted (value-weighted) averagereturns from 1973 to 2010.

35

Parameter Description Value

γ0 Demand shifter 25γ1 Demand elasticity 2ρZ Persistence of aggregate demand shock 0.8σZ Standard deviation of aggregate demand shock 0.025α Capital share of productivity 0.36ρA Persistence of idiosyncratic productivity shock 0.8σA Standard deviation of idiosyncratic productivity shock 0.05δ Capital depreciation rate 0.02

ηlow Low capital adjustment cost 0.8ηhigh High capital adjustment cost 1.6r Real interest rate 0.04w Price of fully depreciating input 1

Table 1: Model parameterization

36

Table 2: Sample’s Industry Concentrations

Mean Median SDV Min. Max. P25 P75

HHI (sales) 0.363 0.325 0.203 0.043 0.997 0.211 0.472HHI MA (sales) 0.361 0.327 0.194 0.043 0.996 0.216 0.469HHI (assets) 0.375 0.331 0.208 0.037 0.997 0.217 0.487HHI MA (assets) 0.373 0.334 0.199 0.039 0.997 0.222 0.487

These are descriptive statistics of the sample’s industry concentrations. The dataconsist of 7,736 industrial firms spanning the 37-year period from 1973 to 2010. Eachfirm is assigned to an industry according to its three-digit SIC codes, resulting in171 industries. HHI (sales) and HHI (assets) measure industry concentration usingnet sales and total assets, respectively, with HHI MA (sales) and HHI MA (assets)representing their respective 3-year moving averages. SDV, P25, and P75 stand forstandard deviation, 25th percentile, and 75th percentile, respectively.

Table 3: Industry Concentration Quintiles

Industry Level Firm Level(1) (2) (3) (1’) (2’) (3’)

Quintile HHI N. Ind. Ind. Years HHI Cont. N. Firms Firm Years

1 (Competitive) 0.142 26 988 0.004 869 33,0252 0.238 27 1,015 0.016 392 14,8843 0.329 27 1,012 0.031 287 10,9024 0.434 27 1,015 0.058 200 7,5835 (Concentrated) 0.663 26 995 0.121 145 5,491t-statistic 5–1 115.32 33.77

This table reports the characteristics of industry concentration quintiles. Each firm is assigned to anindustry according to its three-digit SIC codes, resulting in 171 industries. At the end of June eachyear, the industries are then grouped into quintiles according to their three-year average salses HHI. Thestatistics are the 37-year averages at the industry level in columns 1 to 3 and at the firm level in columns1’ to 3’. The variable HHI Cont. (HHI Contribution) is the average of the firms’ squared ratios of netsales to aggregate industry net sales.

37

Table 4: Characteristics of Firms in Competitive and Concentrated Industry Quintiles

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)Sales TA MV BM LEV LTDR RER1 RER2 RER3 PPER

Quintile 1: Competitive 1,275 1,062 1,654 0.783 0.332 0.177 0.300 0.365 0.246 0.283

Quintile 5: Concentrated 2,889 2,460 2,425 1.043 0.435 0.251 0.288 0.359 0.274 0.304

t-statistic 5–1 8.27 9.03 4.03 13.18 28.30 22.39 -4.50 -2.05 8.71 7.71

The study sample consists of 7,736 industrial firms spanning the 37-year period from 1973 to 2010. Firms are assigned toindustries according to their three-digit SIC codes, when are then divided into HHI quintiles. The above statistics are the37-year averages. Sales, TA, and MV, stand respectively for net sales, total assets, and market value of equity, in 2010U.S. dollars (millions). Book to market (BM ) is the ratio of book value of equity to market value of equity. LEV is theratio of book value of total liabilities to total market value of firm. LTDR is the ratio of book value of long-term debt toMV plus book value of long-term debt. RER1, RER2, and RER3 are, respectively, the ratio of buildings and capitalizedleases to property, plant, and equipment (PPE); the ratio of buildings, capitalized leases, construction in progress, andland to PPE; and the ratio of buildings and land to PPE. PPER is the ratio PPE to TA.

Table 5: Characteristics of Real Estate Decile Portfolios

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)TA Sales MV BM LTDR LEV RER1 RER2 RER3 PPER

Competitive IndustriesDecile 1: Low RE Portf. (1a) 331 373 301 0.647 0.126 0.267 0.031 0.076 0.031 0.206Decile 10: High RE Portf. (2a) 703 905 931 0.818 0.213 0.358 0.651 0.716 0.401 0.387t-statistic 10–1 7.60 9.90 8.32 5.67 16.15 14.31 321.3 206.4 64.47 32.73

Concentrated IndustriesDecile 1: Low RE Portf. (1b) 900 701 823 0.807 0.211 0.366 0.028 0.098 0.045 0.322Decile 10: High RE Portf. (2b) 3,059 6,011 4,580 1.201 0.252 0.438 0.678 0.754 0.531 0.324t-statistic 10–1 3.27 3.43 3.03 4.00 2.83 4.49 93.98 70.19 32.29 0.21

Cross Tests t-statistics1b vs. 1a 5.16 2.83 3.95 2.45 7.71 8.33 -2.27 3.12 2.84 10.082b vs. 2a 3.61 3.31 2.95 4.81 3.52 6.42 3.89 5.27 8.45 -7.0)

The study sample consists of 7,736 industrial firms spanning the 37-year period from 1973 to 2010. Firms are assigned to industriesaccording to their three-digit SIC codes. At the end of June each year, the industries are then grouped into quintiles according totheir three-year average salesHHI. Firms in the competitive and concentrated industry groups are then ranked into decile portfoliosaccording to their real estate ownership proxied by REI1 defined below. The above statistics are the portfolios’ 37-year averages.Sales, TA, and MV, stand respectively for net sales, total assets, and market value of equity, in 2010 U.S. dollars (millions). Bookto market (BM ) is the ratio of book value of equity (TA minus total liabilities plus balance sheet deferred taxes and investmenttax credit minus book value of preferred stocks) to market value of equity. LEV is the ratio of book value of total liabilities tototal market value of firm. LTDR is the ratio of book value of long-term debt to MV plus book value of long-term debt. RER1,RER2, and RER3 are, respectively, the ratio of buildings and capitalized leases to property, plant, and equipment (PPE); theratio of buildings, capitalized leases, construction in progress, and land to PPE; and the ratio of buildings and land to PPE.PPER is the ratio PPE to TA.

38

Tab

le6:

Aver

age

Lev

ered

Por

tfol

ioR

etu

rns

Portfolios

Low

23

45

67

89

High

High-L

ow

Panel

A:Competitive

Industries

Exce

ssR

etu

rn4.7

08.0

69.3

510.3

49.3

99.0

99.4

610.1

210.1

710.5

95.8

9(1.02)

(2.00)

(2.43)

(2.96)

(2.74)

(2.83)

(3.01)

(2.98)

(3.05)

(2.82)

(2.72)

Ind

.A

dj.

Ret

urn

-3.6

5-0

.51

-0.1

60.5

10.0

40.4

2-0

.37

0.6

21.2

21.1

64.8

1(-2.48)

(-0.53)

(-0.20)

(0.65)

(0.06)

(0.51)

(-0.40)

(0.78)

(1.67)

(1.20)

(3.06)

Panel

B:ConcentratedIn

dustries

Exce

ssR

etu

rn11.2

65.1

37.5

14.9

67.5

45.0

18.5

510.5

78.1

24.7

9-6

.46

(2.12)

(1.02)

(2.01)

(1.36)

(2.16)

(1.44)

(2.55)

(2.92)

(2.24)

(1.28)

(-1.45)

Ind

.A

dj.

Ret

urn

3.0

9-1

.21

-0.1

4-1

.70

1.4

2-0

.19

1.0

61.2

01.5

6-1

.31

-4.4

1(1.46)

(-0.52)

(-0.09)

(-1.19)

(1.14)

(-0.15)

(0.86)

(0.88)

(0.96)

(-0.81)

(-1.58)

Th

eta

ble

rep

ort

san

nu

alize

dva

lue-weigh

ted

aver

age

month

lyle

ver

edex

cess

retu

rns

(in

per

cent)

earn

edby

the

reales

tate

ran

ked

port

folios

inco

mp

etit

ive

an

dco

nce

ntr

ate

din

du

stry

gro

up

sfo

rth

e444-m

onth

per

iod

Ju

ly1973–Ju

ne

2010

an

dth

et-

stati

stic

s(i

talici

zed

figu

res

inp

are

nth

eses

).C

olu

mn

s1

(lab

eled

Low

)th

rou

gh

10

(lab

eled

High

)re

pre

sent

stock

port

folios

ran

ked

inin

crea

sin

gord

erof

real

esta

teow

ner

ship

,p

roxie

dby

the

rati

oof

bu

ild

ings

an

dca

pit

alize

dle

ase

sto

pro

per

ty,

pla

nt,

an

deq

uip

men

t(R

ER1

).ExcessReturn

isth

eex

cess

retu

rnover

the

risk

-fre

era

te,

pro

xie

dby

the

on

e-m

onth

Tre

asu

ryb

ill

rate

sfr

om

Ibb

ots

on

an

dA

ssoci

ate

s.In

d.Adj.

Return

stan

ds

for

ind

ust

ry-a

dju

sted

retu

rn,

the

exce

ssre

turn

over

the

aver

age

retu

rnof

an

ind

ust

ryp

ort

folio

incl

ud

ing

firm

ssh

ari

ng

the

sam

eth

ree-

dig

itS

ICco

des

.T

he

colu

mn

lab

eled

High-L

ow

rep

ort

sd

iffer

ence

sin

retu

rns

bet

wee

nth

eh

igh

an

dlo

wre

al

esta

ted

ecile

port

folios.

39

Tab

le7:

Aver

age

Un

leve

red

Por

tfol

ioR

etu

rns

Portfolios

Low

23

45

67

89

High

High-L

ow

Panel

A:Competitive

Industries

Exce

ssR

etu

rn2.5

95.0

06.2

37.2

36.4

36.3

86.2

56.5

76.6

16.7

34.1

4(0.25)

(0.98)

(1.45)

(2.02)

(1.79)

(1.92)

(1.92)

(1.91)

(1.91)

(1.64)

(2.23)

Ind

.A

dj.

Ret

urn

-3.0

4-0

.68

-0.0

70.5

60.2

00.5

2-0

.31

0.2

31.0

70.6

93.7

3(-2.22)

(-0.81)

(-0.25)

(0.90)

(0.36)

(0.78)

(-0.36)

(0.38)

(1.98)

(0.83)

(2.85)

Panel

B:ConcentratedIn

dustries

Exce

ssR

etu

rn8.1

73.8

54.2

92.3

24.5

61.6

45.6

46.3

54.9

02.9

9-5

.18

(1.61)

(0.44)

(0.71)

(-0.06)

(0.93)

(-0.35)

(1.56)

(1.65)

(1.17)

(0.31)

(-1.65)

Ind

.A

dj.

Ret

urn

3.5

2-0

.24

-0.5

5-1

.69

0.8

4-0

.87

0.6

30.7

51.1

4-0

.30

-3.8

3(2.27)

(-0.12)

(-0.67)

(-1.69)

(0.84)

(-1.05)

(0.72)

(0.79)

(1.19)

(-0.10)

(-1.88)

Th

eta

ble

rep

ort

san

nu

alize

dvalu

e-w

eighte

daver

age

month

lyu

nle

ver

edex

cess

retu

rns

(in

per

cent)

earn

edby

the

reales

tate

ran

ked

port

folios

inco

mp

etit

ive

an

dco

nce

ntr

ate

din

du

stry

gro

up

sfo

rth

e444-m

onth

per

iod

Ju

ly1973–Ju

ne

2010

an

dth

et-

stati

stic

s(i

talici

zed

figu

res

inp

are

nth

eses

).U

nle

ver

edre

turn

calc

ula

tion

sass

um

en

ota

ke

taxes

an

da

cost

of

the

deb

tof

7%

acr

oss

the

board

.C

olu

mn

s1

(lab

eled

Low

)th

rou

gh

10

(lab

eled

High

)re

pre

sent

the

stock

port

folios

ran

ked

inin

crea

sin

gord

erof

real

esta

teow

ner

ship

,p

roxie

dby

the

rati

oof

bu

ild

ings

an

dca

pit

alize

dle

ase

sto

pro

per

ty,

pla

nt,

an

deq

uip

men

t(R

ER1

).ExcessReturn

isth

eex

cess

retu

rnover

the

risk

-fre

era

te,

pro

xie

dby

the

on

e-m

onth

Tre

asu

ryb

ill

rate

sfr

om

Ibb

ots

on

an

dA

ssoci

ate

s.In

d.Adj.

Return

stan

ds

for

ind

ust

ry-a

dju

sted

retu

rn,

the

exce

ssre

turn

over

the

aver

age

retu

rnof

an

ind

ust

ryp

ort

folio

incl

ud

ing

firm

ssh

ari

ng

the

sam

eth

ree-

dig

itS

ICco

des

.T

he

colu

mn

lab

eled

High-L

ow

rep

ort

sd

iffer

ence

sin

retu

rns

bet

wee

nth

eh

igh

an

dlo

wre

al

esta

ted

ecile

port

folios.

40

Tab

le8:

Ab

nor

mal

Por

tfol

ioR

etu

rns

Portfolios

Low

23

45

67

89

High

High-L

ow

Com

pet

itiv

eIn

du

stri

es(1

)-3

.08

-0.0

21.3

23.0

11.7

21.5

42.1

02.4

11.9

41.7

64.8

5(-1.76)

(-0.01)

(1.12)

(2.85)

(1.86)

(1.74)

(2.23)

(2.51)

(1.81)

(1.31)

(2.80)

Con

centr

ate

dIn

du

stri

es(2

)2.5

2-1

.63

-1.0

8-2

.71

-0.8

7-4

.21

0.9

4-0

.17

-1.0

8-2

.53

-5.0

5(0.96)

(-0.73)

(-0.72)

(-1.69)

(-0.61)

(-3.20)

(0.77)

(-0.10)

(-0.72)

(-1.39)

(-1.65)

Observa

tions

444

444

444

444

444

444

444

444

444

444

444

Adj.

R2Estim

ation(1)

0.787

0.813

0.838

0.843

0.872

0.864

0.844

0.859

0.817

0.791

0.171

Adj.

R2Estim

ation(2)

0.505

0.564

0.609

0.570

0.633

0.672

0.642

0.537

0.618

0.513

0.083

Th

ista

ble

rep

ort

ses

tim

ate

dab

norm

al

port

folio

retu

rns

(in

per

cent

an

dan

nu

alize

d)

from

valu

e-w

eighte

dm

onth

lyu

nle

ver

edex

cess

port

folio

retu

rns

inco

mp

etit

ive

an

dco

nce

ntr

ate

din

du

stri

es.

Th

eit

alici

zed

figu

res

inp

are

nth

eses

are

the

rob

ust

t-st

ati

stic

sof

the

coeffi

cien

tes

tim

ate

s.U

nle

ver

edre

turn

calc

ula

tion

sass

um

en

ota

xes

an

da

cost

of

deb

tof

7%

acr

oss

the

board

.T

hes

ees

tim

ate

sare

base

don

the

thre

e-fa

ctor

mod

el4

con

sist

ing

of

the

exce

ssm

ark

etre

turn

(rm−r f

)an

dth

etw

oF

am

aan

dF

ren

chst

ock

risk

fact

ors

,sm

ban

dhml.

Colu

mn

s1

(lab

eled

Low

)th

rou

gh

10

(lab

eled

High

)re

pre

sent

the

stock

port

folios

ran

ked

inin

crea

sin

gord

erof

real

esta

teow

ner

ship

,p

roxie

dby

the

rati

oof

bu

ild

ings

an

dca

pit

alize

dle

ase

sto

pro

per

ty,

pla

nt,

an

deq

uip

men

t(R

ER1

).r m−r f

rep

rese

nts

the

valu

e-w

eighte

dm

onth

lyre

turn

son

CR

SP

-lis

ted

stock

sm

inu

sth

eon

e-m

onth

Tre

asu

rybil

lra

tes

from

Ibb

ots

on

an

dA

ssoci

ate

s.sm

ban

dhml

are

the

aver

age

retu

rns

on

small-s

tock

port

folios

min

us

the

aver

age

retu

rnon

big

-sto

ckp

ort

folios

an

dth

eaver

age

retu

rnon

valu

e-st

ock

port

folios

min

us

the

aver

age

retu

rnon

gro

wth

-sto

ckp

ort

folios,

resp

ecti

vel

y.M

onth

lyst

ock

retu

rnd

ata

are

from

CR

SP

,w

ithr m

,r f

,sm

b,an

dhml

from

Ken

net

hF

ren

ch’s

web

site

.T

he

colu

mn

lab

eled

High-L

ow

rep

ort

sab

norm

al

retu

rns

from

diff

eren

ces

inex

cess

retu

rns

bet

wee

nth

eh

igh

an

dlo

wre

al

esta

tedec

ile

port

folios.

41

Table 9: High-Low Portfolio Factor Loadings

Competitive ConcentratedIndustries Industries

alpha 4.85 -5.05(2.80) (1.65)

rm − rf -0.147 -0.165(4.56) (2.87)

smb -0.215 -0.188(4.66) (2.29)

hml 0.164 0.282(3.39) (3.26)

Observations 444 444Adj. R-squared 0.171 0.083

This table presents the regression results of differences in value-weightedunlevered excess returns between the high and low real estate decile port-folios. Average unexplained returns (alpha) are in percent and annualized.rm,t−rf,t represents the value-weighted monthly returns on CRSP-listedstocks minus the one-month Treasury bill rates from Ibbotson and As-sociates. smb and hml are the average returns on small-stock portfoliosminus the average return on big-stock portfolios and the average returnon value-stock portfolios minus the average return on growth-stock port-folios, respectively. The italicized figures in parentheses are the robustt-statistics of the coefficient estimates.

Table 10: Abnormal Returns on High–Low Portfolio from July 1973 to June 2005

Competitive Industries Concentrated Industries(1) (2) (1´) (2´)

Levered Returns Unlevered Returns Levered Returns Unlevered Returns

alpha 6.73 4.64 -10.63 -6.84(3.08) (2.53) (-2.31) (-2.10)

Risk Factors Yes Yes Yes Yes

Observations 390 390 390 390Adj. R2 0.161 0.186 0.127 0.095

This table presents abnormal returns from regression of differences value-weighted levered and unlevered excessreturns between the high and low real estate decile portfolios from 1973 to 2005. Columns (1 ) and (2 ) applyto competitive industries; columns (1´) and (2´) are for concentrated industries. These estimates are based onthe three-factor model 4 consisting of the excess market return (rm,t − rf,t) and the two Fama and French stockrisk factors, smb and hml. Average unexplained excess returns are annualized values. rm,t − rf,t represents thevalue-weighted monthly returns on CRSP-listed stocks minus the one-month Treasury bill rates from Ibbotson andAssociates. smb and hml are the average returns on small-stock portfolios minus the average return on big-stockportfolios and the average return on value-stock portfolios minus the average return on growth-stock portfolios,respectively.

42

Table 11: Quintile Portfolios and Alternative Industry Classifications

(1) (2) (3) (4) (5)Base Case Quintile Portf. 2-digit SIC FF Industries HHI (asset)

Competitive IndustriesExcess Return 4.14 2.47 3.44 3.45 4.17

(2.23) (1.60) (1.79) (2.22) (2.25)Ind. Adj. Return 3.73 2.42 3.62 3.02 4.07

(2.85) (2.92) (2.39) (2.58) (2.95)

Concentrated IndustriesExcess Return -5.18 -3.21 -4.46 -12.15 -4.06

(-1.65) (-1.43) (-1.46) (-2.30) (-1.45)Ind. Adj. Return -3.83 -1.85 -1.67 -9.31 -1.85

(-1.88) (-1.42) (-0.68) (-1.91) (-0.93)

The table reports differences in value-weighted average unlevered excess and industry-adjusted returns (in per-cent) between the high and low real estate decile portfolios during the 444-month period from July 1973 to June2010 and their t-statistics (italicized figures in parentheses). Column 1 represents the Base Case (High-Lowreturns from Table 7). Column 2 lists returns on quintile, rather than decile, portfolios based on RER1. Incolumn 3, firms are assigned to industries according to their two-digit SIC codes and in column 4 according tothe 48 Fama and industry groups. In column 5, industry concentrations are based on total assets, rather thannet sales.

Table 12: Alternative Real Estate Measures

(1) (2) (3) (4)Base Case RER2 PPER ARER

Competitive IndustriesExcess Return 4.14 4.70 4.33 4.01

(2.23) (2.18) (1.56) (2.69)Ind. Adj. Return 3.73 4.28 4.86 4.53

(2.85) (2.84) (2.92) (3.26)

Concentrated IndustriesExcess Return -5.18 -5.01 -3.02 -1.03

(-1.65) (-1.52) (-0.99) (-0.41)Ind. Adj. Return -3.83 -2.78 -0.36 -1.11

(-1.88) (-1.33) (-0.17) (-0.47)

The table reports differencesf in value-weighted average unlevered excess and industry-adjustedreturns (in percent) between the high and low real estate decile portfolios during the 444-monthperiod from July 1973 to June 2010 and their t-statistics (italicized figures in parentheses). Incolumn 1, the Base Case (High-Low returns from Table 7), firms are sorted into decile portfoliosaccording to RER1, the ratio of buildings and capitalized leases to property, plant, and equipment(PPE). The figures in columns 2 to 4 are based on different measures of real estate ownership. RER2and PPER are respectively the ratio of buildings, capitalized leases, construction in progress, andland to PPE, and the ratio PPE to total assets (TA). ARER is firm RER1 minus the averageindustry RER1 (i.e., firms belonging to the same three-digit SIC codes).

43

Tab

le.1

:E

quall

y-W

eigh

ted

Ave

rage

Por

tfol

ioR

etu

rns

Portfolios

Low

23

45

67

89

High

High-L

ow

Panel

A:Competitive

Industries

Exce

ssR

etu

rn9.7

612.6

611.9

214.0

613.9

211.7

914.8

914.1

112.6

114.9

25.1

6(1.95)

(2.85)

(2.73)

(3.65)

(3.51)

(3.21)

(3.91)

(3.72)

(3.29)

(3.57)

(2.25)

Ind

.A

dj.

Ret

urn

-2.7

10.2

0-1

.35

0.7

10.6

2-0

.68

0.7

40.5

0-0

.04

2.1

64.8

6(-1.86)

(0.18)

(-1.36)

(0.76)

(0.59)

(-0.67)

(0.73)

(0.51)

(-0.05)

(2.01)

(2.80)

Panel

B:ConcentratedIn

dustries

Exce

ssR

etu

rn17.1

512.1

110.5

77.1

811.0

09.4

111.8

912.0

27.7

48.8

9-8

.26

(2.82)

(1.95)

(2.38)

(1.75)

(2.79)

(2.40)

(2.78)

(2.45)

(1.93)

(1.99)

(-1.65)

Ind

.A

dj.

Ret

urn

4.2

41.2

9-0

.03

-2.3

30.6

5-0

.33

2.1

5-0

.24

-1.6

5-2

.18

-6.4

2(1.47)

(0.44)

(-0.02)

(-1.08)

(0.34)

(-0.18)

(1.03)

(-0.09)

(-0.69)

(-0.93)

(-1.72)

Th

eta

ble

rep

ort

san

nu

alize

deq

ually-w

eighte

daver

age

month

lyle

ver

edre

turn

s(i

np

erce

nts

)ea

rned

by

the

real

esta

tera

nked

port

folios

inth

eco

mp

etit

ive

an

dco

nce

ntr

ate

din

du

stry

gro

up

s)fo

rth

e444-m

onth

per

iod

from

Ju

ly1973

toJu

ne

2010

an

dth

eirt-

stati

stic

s(i

talici

zed

figu

res

inp

are

nth

eses

).C

olu

mn

s1

(lab

eled

Low

)th

rou

gh

10

(lab

eled

High

)re

pre

sent

the

stock

port

folios

ran

ked

inin

crea

sin

gord

erof

real

esta

teow

ner

ship

,p

roxie

dby

the

rati

oof

bu

ild

ings

an

dca

pit

alize

dle

ase

sto

pro

per

ty,

pla

nt,

an

deq

uip

men

t(R

ER1

).ExcessReturn

isth

eex

cess

retu

rnover

the

risk

-fre

era

te,

pro

xie

dby

the

on

e-m

onth

Tre

asu

ryb

ill

rate

sfr

om

Ibb

ots

on

an

dA

ssoci

ate

s.In

d.

Adj.

Return

stan

ds

for

ind

ust

ry-a

dju

sted

retu

rnan

dis

the

exce

ssre

turn

over

the

aver

age

retu

rnof

an

indu

stry

port

folio

regro

up

ing

firm

ssh

ari

ng

the

sam

eth

ree-

dig

itS

ICco

des

.T

he

colu

mn

lab

eled

High-L

ow

rep

ort

sd

iffer

ence

sin

retu

rns

bet

wee

nth

eh

igh

an

dlo

wre

al

esta

ted

ecile

port

folios.

44

Tab

le.2

:A

bn

orm

alP

ortf

olio

Ret

urn

sfr

omL

ever

edE

xce

ssR

eturn

s

Portfolio

Low

23

45

67

89

High

High-L

ow

Com

pet

itiv

eIn

du

stri

es(1

)-3

.54

0.2

51.5

73.5

42.2

91.9

13.2

03.8

23.1

73.0

26.5

6(-1.90)

(0.17)

(1.23)

(2.90)

(2.08)

(1.72)

(2.80)

(3.17)

(2.35)

(2.01)

(3.20)

Con

centr

ate

dIn

du

stri

es(2

)3.7

9-3

.68

-2.0

6-3

.47

-1.5

4-4

.76

0.5

00.2

8-0

.56

-3.7

6-7

.56

(1.07)

(-1.15)

(-0.91)

(-1.51)

(-0.76)

(-2.44)

(0.25)

(0.12)

(-0.24)

(-1.51)

(-1.75)

Observa

tions

444

444

444

444

444

444

444

444

444

444

444

Adj.

R2Estim

ation(1)

0.844

0.866

0.895

0.884

0.902

0.887

0.874

0.881

0.844

0.847

0.143

Adj.

R2Estim

ation(2)

0.569

0.616

0.647

0.618

0.676

0.701

0.666

0.600

0.607

0.576

0.109

Th

ista

ble

rep

ort

ses

tim

ate

dab

norm

al

port

folio

retu

rns

(in

per

cent

an

dan

nu

alize

d)

from

valu

e-w

eighte

dm

onth

lyle

ver

edex

cess

port

folio

retu

rns

inco

mp

etit

ive

ind

ust

ries

.T

he

italici

zed

figu

res

inp

are

nth

eses

are

the

rob

ust

t-st

ati

stic

sof

the

coeffi

cien

tes

tim

ate

s.T

hes

ees

tim

ate

sare

base

don

the

thre

e-fa

ctor

mod

el4

con

sist

ing

of

the

exce

ssm

ark

etre

turn

(rm,t−r f,t

)an

dth

etw

oF

am

aan

dF

ren

chst

ock

risk

fact

ors

,sm

ban

dhml.

Colu

mn

s1

(lab

eled

Low

)th

rou

gh

10

(lab

eled

High

)re

pre

sent

the

stock

port

folios

ran

ked

inin

crea

sing

ord

erof

real

esta

teow

ner

ship

,p

roxie

dby

the

rati

oof

bu

ild

ings

an

dca

pit

alize

dle

ase

sto

pro

per

ty,

pla

nt,

an

deq

uip

men

t(R

ER1

).r m

,t−r f,t

rep

rese

nts

the

valu

e-w

eighte

dm

onth

lyre

turn

son

CR

SP

-lis

ted

stock

sm

inu

sth

eon

e-m

onth

Tre

asu

ryb

ill

rate

sfr

om

Ibb

ots

on

an

dA

ssoci

ate

s.sm

ban

dhml

are

the

aver

age

retu

rns

on

small-s

tock

port

folios

min

us

the

aver

age

retu

rnon

big

-sto

ckp

ort

folios

an

dth

eaver

age

retu

rnon

valu

e-st

ock

port

folios

min

us

the

aver

age

retu

rnon

gro

wth

-sto

ckp

ort

folios,

resp

ecti

vel

y.T

he

colu

mn

lab

eled

High-L

ow

rep

ort

sab

norm

al

retu

rns

from

diff

eren

ces

inre

turn

sb

etw

een

the

hig

han

dlo

wre

al

esta

ted

ecile

port

folios.

45

Appendix: Model

Equilibrium

This appendix explains the equilibrium concept. This concept, extended oblivious equilibrium, istaken from Weintraub et al. (2010). In an extended oblivious equilibrium, rather than tracking thestate of every other firm as a state variable (as would be the case in a Markov perfect equilibrium ofEricson and Pakes (1995)), each firm instead tracks the long-run average industry state, conditionalon today’s value of the aggregate industry shock. That is, rather than tracking every other firm’scapital holdings and productivity and responding appropriately, each firm follows a strategy thatis “oblivious” in the sense that it responds to a hypothetical average firm and is oblivious to theactions of any particular competitor. Furthermore, the equilibrium definition in Weintraub et al.(2010) is simplified by disallowing firm entry and exit and by allowing the firms to condition theirbehavior on only the current value of the aggregate shock, rather than a long history of shocks.This greatly reduces the computational burden of solving for an equilibrium and also allows thenumber of firms in the industry to be varied easily.

In the model, an extended oblivious equilibrium is a strategy µ∗ = µ∗ (Ki,t, Ai,t, Zi,t) such that

supµV (Ki,t, Ai,t, Zt|µ, µ∗) = V (Ki,t, Ai,t, Zt|µ∗, µ∗) .

for every Ki,t, Ai,t, and Zi,t and for every i where V (Ki,t, Ai,t, Zt|µ′, µ) is the value function solvingthe Bellman equation (2) that results when firm i plays strategy µ′ and all N − 1 of his opponentsplay strategy µ. This equilibrium concept has the advantage that it converges to a perfectlycompetitive equilibrium as the number of firms increases.

Computational Approach

This appendix describes the algorithm used to numerically solve the model for an extended obliviousequilibrium with N firms and all other parameters given. This algorithm is presented in a moregeneral form in Weintraub et al. (2009).

The steps of the algorithm are as follows:

1. Use the method of Tauchen (1986) to discretize the AR(1)’s for Zt and Ai,t, obtaining fivenodes and the corresponding transition matrices for each.

2. Discretize the Ki,t and Li,t spaces on a fine grid.

3. Conjecture an extended oblivious equilibrium strategy µGuess (Ki,t, Ai,t, Zi,t) (i.e., the policyfunctions for Li,t and Ki,t+1).

4. Using µGuess (Ki,t, Ai,t, Zt), compute the average firm state conditional on Zt; call it sµ(Zt).This is done in a way that is similar to finding the stationary distribution of assets in aHuggett (1993) or Aiyagari (1994) economy for each value of the aggregate shock. Note thatsµ(Zt) is simply a scalar for each Zt.

5. Solve firm i’s Bellman equation (2) using value function iteration with∑

j 6=iQj,t = (N − 1) ·sµ(Zt). The implied policy functions/extended oblivious equilibrium strategy µNew (Ki,t, Ai,t, Zi,t)is obtained in the process.

46

6. Check if sup ‖ µGuess (Ki,t, Ai,t, Zi,t) − µNew (Ki,t, Ai,t, Zi,t) ‖< ε for some small toleranceε is true. If so, µGuess (Ki,t, Ai,t, Zi,t) is an extended oblivious equilibrium, µ∗. If not, setµGuess (Ki,t, Ai,t, Zi,t) = µNew (Ki,t, Ai,t, Zi,t) and return to step 4.

After an extended oblivious equilibrium has been found, the behavior of the N firms is simulatedfor 11,000 time periods (discarding the first 1,000 to eliminate dependence on initial conditions) toobtain the sequences of Ki,t, Li,t, and βi,t.

47