1

A Simultaneous Model

and Empirical Test of the

Demand and Supply of

Retail Space

John D. Benjamin*

G. Donald Jud**

Daniel T. Winkler***

Abstract. Much of the burgeoning distress of the 1990s in the United States retail space

markets reflects a mismatch in the amount and location of retail space demand and

supply. Understanding the demand and supply for retail space is critically important to

academics, professionals and others associated with owning, operating and financing

retail space. Using data from nineteen major metropolitan statistical areas (MSAs) for

the period 1986–95, this article develops a simultaneous model of retail space demand

and supply which includes the influence of vacancy rate. The model and results provide

evidence about how demand and supply for retail space respond to changes in retail

sales, rental prices, land-use regulation and land availability, and the cost of capital. The

results show inelastic price elasticities of demand and supply for retail space.

Introduction

Much of the burgeoning distress of the 1990s in the United States retail space markets

reflects a mismatch in the amount and location of retail space demand and supply.1

According to the International Council of Shopping Centers (ICSC) (1996a), shopping

center space that captures about 70% of non-auto retail sales is being produced at a

rate twice that of the U.S. population growth. Over the last decade, it has risen from

15.2 square feet per person in 1986 to 18.9 square feet per person as of January 1996

(a 24.3% increase while the U.S. population grew only 10.3%).2 Further, the excess

supply of retail space or retail space saturation problem is compounded by stagnating

or declining real retail sales.3 Pershio (1991), Wurtzebach (1993, 1995), Roulac

(1994), Delisle, Grissom and Neyman (1995), Litt and van Dijkum (1995), Wheaton

and Torto (1995) and other researchers have commented on the coming shakeout in

retail space generated from an imbalance of the demand for space relative to changing

supply both in the amount of retail space and its location.4 This mismatch in the

demand and supply for retail space has the potential to reduce rents, raise vacancies

and challenge the financial worthiness of retail landlords and creditors.5 Understanding

*Department of Finance and Real Estate, The American University, Washington, DC 20016 or jbenj@

american.edu.

**Department of Finance, University of North Carolina-Greensboro, Greensboro, NC 27412 or Juddon@

uncg.edu.

***Department of Finance, University of North Carolina-Greensboro, Greensboro, NC 27412 or winkler

@iago.uncg.edu.
VOLUME 16, NUMBER 1, 1998

the demand and supply for retail space is critically important to academics,

professionals and others associated with owning, operating and financing retail space.

Although many academic studies have focused on the demand and supply elasticities

for housing, few investigate the demand and supply of retail space.6 Given the

relevance of market elasticity estimates to retail space investors and lenders, there

exists a need to better understand the dynamics of retail space demand and supply.

In this article, we present a simultaneous model of retail space demand and supply

including the influence of vacancy rate. In section two, we analyze prior research on

shopping center demand and supply. Section three presents a simultaneous model of

the demand and supply for retail space. Section four describes the data and empirical

model, and section five presents the empirical results. The implications of our findings

are discussed in section six, while section seven provides a summary and conclusions.

Demand and Supply for Retail Space

Demand and supply fundamentals are some of the major determinants of investment

returns to real estate. Many retail space market researchers, as earlier described,

believe that the per capital supply of retail space is too great for the existing population

demand. According to ICSC (1996b), extreme volatility has characterized the history

of new retail supply. Changing rental rates, retail sales and other factors have

influenced the demand for retail space while the economic climate, land availability,

capital market cycles, interest rates and tax law changes have influenced the supply

of new retail space. New retail space supply is also responsive to demographic,

sociological, economic trends and local conditions. According to Ludgin, Perlmutter

and Boss (1996), for example, the investment value of a specific retail center may be

less dependent on national trends and more dependent on competition for retail sales

from other local shopping centers. Factors affecting shopping center sales and

profitability include: low retail vacancy, constraints on future development (e.g.,

zoning, few available sites, and master plan restrictions), population growth and

aggregate disposable income of the local population.

Eppli and Shilling (1996) show that local retail competition and household income

factors explain the local retail sales share for a particular mall. Using regression

analysis and a data set of thirty-eight regional shopping malls, the authors demonstrate

that retail sales at a local mall decrease based upon increased nearby competitive mall

sizes, the distance to competition malls and lower aggregate household income.

A number of studies have focused on the economic and demographic determinants of

demand and supply but have ignored the role of prices (i.e., rents). An extensive

review of these studies can be found in Eppli and Benjamin (1994).

At any given moment, demand and supply for retail space may be moving toward or

away from equilibrium. If demand decreases and supply increases, rents will fall and

vacancies will rise. If demand increases or supply remains stable or decreases, rents

will rise and vacancies will decrease. Given the long lead times necessary for retail
space construction, retail demand and supply are usually not in equilibrium. Prior

research confirms that retail space supply is slow to adjust to changing levels of retail

sales. The lagged adjustment models employed by Benjamin, Jud and Okoruwa

(1993), Benjamin, Jud and Winkler (1995) and Eppli and Shilling (1995) give

empirical support to the argument that retail markets adjust very slowly to changes

in the demand for retail space, as measured by the level of retail sales or the level of

retail employment (also a proxy for retail sales). Local economic and demographic

factors also impact retail sales for a particular market, but Benjamin, Jud and Okoruwa

(1993) and Benjamin, Jud and Winkler (1995) show that economic and demographic

factors are already captured in variables representing actual past retail sales. The long

adjustment lags indicate a possible explanation for the prolonged cycles in retail space

markets. These studies, however, are silent on the interaction of rents and vacancy

because none of the models include price or rent variables together with vacancy rate

in their analyses.

A Simultaneous Model of Demand and Supply for Retail Space

In order to focus more closely on the role of prices and rents, we posit a simultaneous

demand and supply model for retail space that includes the influence of vacancy rate.

The demand for space (QD) is derived from the demand for retail goods and services.

Demand is determined by the rental price (Pt

) of retail space and the level of retail

sales (Sales).7 Sales, in turn, are determined by relative prices, incomes and

demographic factors. The supply of retail space (QS) is influenced by the previous

period’s rental price of space (Pt21) and the relative cost of producing it (Cost). The

rental price (Pt) is related to the lagged rental price (P ) and the vacancy rate (VR). t21

These relations are expressed in the following model:

Q 5 ƒ(P , Sales), (1) D t

Q 5 ƒ(P , Cost), (2) S t21

P 5 ƒ(P , VR), (3) t t21

VR 5 1 2 Q /Q . (4) D S

Prices, quantities and vacancy rates are endogenous; sales and cost variables are

exogenous.

The cost variable of the supply equation includes capital costs and supply-side

constraints. The cost of capital is measured by the real interest rate, that is, the interest

rate on long-term government bonds less the inflation rate. The variation in land

supply constraints across MSAs is measured by an index of land-use regulations and

land availability.

The American Institute of Planners (AIP) (1976) developed an index of state landuse regulations that provides broad coverage and a rating of 0 (low regulation) to 8

(high regulation). Although this index is coded by state rather than MSA and dates
VOLUME 16, NUMBER 1, 1998

to the late 1970s, it is still extensively employed to measure regulation. A study by

Shilling, Sirmans and Guidry (1991), using the AIP index, reports that cities located

in states with more restrictive land-use regulations have higher land prices.

Linneman, Summers, Brooks and Buist (1990) collected data on regulatory practices

in a questionnaire about the development process in sixty large MSAs. Their resulting

index, often referred to as the Wharton Index, has broad coverage and is relatively

recent; however, it is more subjectively measured than the AIP index.

Another avenue of supply-side restraint is land availability. Segal and Srinivasan

(1985) surveyed planning officials in fifty-one cities about the percentage of land

removed from development by regulation; their index is an estimate of the percentage

of undeveloped land by MSA. Malpezzi (1994) notes that the index is robust;

although, some local responses appear inconsistent.

Rose (1989) developed a land supply index for forty MSAs of the percentage of land

removed from development because of large bodies of water. The index ranges from

0 to 1.0; higher values (such as those cities with no water restrictions) indicate less

restriction and potentially less limitation on development. Although the sample is

limited in size and dependent upon an accurate estimate of the population density

gradient (which he assumes to be the median value), the study shows that the land

supply index based on water restrictions is important in explaining interurban land

price differences.

All of the aforementioned measures of supply-side constraints appear to be related to

measures of land regulation and availability. Moreover, regulation and availability are

usually concurrent influences. The problem, however, is that many of these supplyside measures are constructed from additive measures or they are largely subjectively

derived from survey data. Therefore, the meaning of the unit measurements is

ambiguous, and it is necessary to construct an aggregate index to avoid the collinearity

associated with simultaneously introducing all of these measures in a statistical

(regression) model.

Data and Estimation

To estimate the simultaneous model of retail space demand and supply, we use data

from nineteen metropolitan statistical areas (MSAs) for the years 1986–95. The final

data set consists of 160 observations.8 The data for retail space and vacancy data,

obtained by MSA from F. W. Dodge, show the aggregate number of square feet of

retail space (excluding restaurants).9 Aggregate real retail sales by year and MSA are

obtained from Woods and Poole Economics, Inc., from data collected by the U.S.

Department of Commerce. The mean effective monthly rental price per square foot

of retail space is available from the National Real Estate Index’s Market History

Reports (1986–95) published by Koll Real Estate Services Company. These data are

adjusted by the Consumer Price Index (CPI-U, 1982–84 5 100) to reflect constantdollar rents. Real interest rates on ten-year U.S. government bonds have been

generated using the Citibank Data Set.
Exhibit 1

Aggregate Index of Land-Use Regulation and Land Availability by Principal

Component Analysis

Index Variable Factor Loading Factor Variance Scoring Coefficient

AIP 0.7 0.5 0.3

Wharton 0.8 0.6 0.4

Segal 0.9 0.8 0.4

Rose 20.4 0.2 20.2

Eigenvalue 2.0

Proportion of Total 0.5

Variance Explained

Data sources: American Institute of Planners (1976), Segal and Srinivasan (1985), Rose (1989) and

Linneman, Summers, Brooks and Buist (1990).

The regulatory cost indexes developed by the AIP and utilized by Shilling, Sirmans

and Guidry (1991) along with the Wharton index are used to measure land-use

regulations. The Segal and Srinivasan (1985) Index is employed as a measure of the

percentage of land made unavailable by regulation. The Rose Index (1989) is used to

gauge land availability in the presence of water restrictions. To avoid the collinearity

problems of using multiple land-use regulation and land availability indexes, we create

an aggregate measure of land-use regulation and land availability using the four

indexes and the econometric method of principal components. In essence, this method

reduces the number of regulatory variables to a smaller subset that contains most of

the regulatory information from the full set.10

Exhibit 1 reports a factor analysis with principal components as the factor method.

The eigenvalue of 2.042 (the sum of the squared factor loadings) suggests a strong

common principal component for the four indexes. The proportion of total variance

explained by one factor is approximately 51% (the eigenvalue divided by the number

of indexes). All subsequent principal components (not reported in Exhibit 1) have

eigenvalues less than 1.0 and explain less than 1/p of the variation, where p denotes

the number of original variables. Therefore, only the first principal component is

reported in Exhibit 1. The first column of Exhibit 1 shows the coefficients for the

principal components. With the exception of the Rose Index, higher values of each

index indicate greater regulation. Therefore, positive factor loadings for AIP and

Wharton are consistent with more regulation. The Segal Index measures the

percentage of land made unavailable for development by regulation; therefore, a

positive factor loading is consistent with a loading measuring more regulation. The

Rose Index measures land availability in the presence of bodies of water, with greater

land availability having values closer to 1.0 while restricted MSAs have values in the

direction closer to 0. The negative factor loading is consistent with the anticipated

inverse relation between the Rose Index and regulation. The third column renders the

scoring coefficients estimated by regression; the transformed data becomes the

regulatory variable for the regression analysis.
VOLUME 16, NUMBER 1, 1998

Estimates using these MSA data series provide a test of the validity of the

simultaneous demand, supply, price and vacancy models shown in Equations (1)–(4).

The empirical model used to estimate the demand equation shown in Equation (1) is

as follows:

QD

M 5 D 1 D P 1 D S 1 D CD 1 e , (5) ,t 0 1 M,t 2 M,t 3 M M,t

where PM,t, SM,t and CDM, are the quantity of space demanded, the mean real QD

M ,,t

effective monthly rental price of space (per square foot of retail space), real retail

sales and regional (census) dummy variables—all continuous variables expressed as

natural logarithms, respectively, for metro area M in year t. The regression parameters

are D0, D1, D2, D3 and the disturbance term is denoted by eM,t

.

Equation (2), representing the supply equation, is estimated for metro area M in year

t using the following empirical model:

QS

M 5 S 1 S P 1 S LR 1 S BD 1 S CD 1 z , (6) ,t 0 1 M,t21 2 M 2 t 4 M M,t

where:

QS

M,t 5 The natural logarithm of quantity of space supplied;

PM,t21 5 The natural logarithm of the mean real effective monthly rental price per

square foot of retail space lagged one year;

LRM 5 The aggregate index of land-use regulation and land availability estimated

by using principal components;

BDt 5 The natural logarithm of the real ten-year Treasury-bond yield;

CDM 5 The regional (census) dummy variables;

Si 5 The regression parameters (for i 5 1, 2, 3 or 4); and

zM,t 5 The disturbance term.

The empirical model used to estimate the rental price equation shown in Equation (3)

which includes the vacancy rate in Equation (4) is as follows:

PM 5 R 1 R P 1 R VR 1 R CD 1 u , (7) ,t 0 1 M,t21 2 M,t 3 M M,t

where:

PM,t 5 The natural logarithm of the mean real effective monthly rental price of space

(per square foot of retail space);

PM,t21 5 The natural logarithm of the mean real effective monthly rental price per

square foot of retail space lagged one year;

VRM,t 5 The natural logarithm of the vacancy rate (1 2 Q,t /Q,t

);

CDM 5 The regional (census) dummy variables;

Ri 5 The regression parameters (for i 5 1, 2 or 3); and

uM,t 5 The disturbance term.
Exhibit 2

Descriptive Statistics for 19 MSAs from 1986–95

(expressed as natural logarithms)

Variable Mean

Std.

Dev. Max. Min.

Quantity Demanded (QDM,t) 11.40 0.37 10.62 12.06

Quantity Supplied (QSM,t) 11.42 0.37 10.63 12.08

Vacancy Rate (VRM,t) 2.26 0.41 1.22 3.18

Mean (Real) Effective Monthly Rent

per Square Foot (PM,t)

2.24 0.16 1.89 2.64

Mean (Real) Effective Monthly

Rent per Square Foot Lagged (PM,t21)

2.26 0.17 1.89 2.64

Real Retail Sales (SM,t) 9.63 0.40 8.78 10.45

Aggregate Index of Land Regulation

from Principal Components (LRM,t)

,20.01 1.01 21.27 2.39

Real Ten-Year T-Bond Yield (BDt) 0.85 0.35 0.28 1.82

Note: n 5 160.

Data sources: F.W. Dodge, Woods and Poole Economics, Inc., National Real Estate Index’s Market

History Reports (1986–95) published by Koll Real Estate Services Company and Citibank Data Set.

In the following section, these simultaneous equations are estimated using the

procedure of two-stage least squares (TSLS).

Empirical Results

Descriptive statistics for variables contained in the demand, supply and price equations

as seen in Equations (5), (6) and (7) are shown in log form in Exhibit 2. For example,

taking the anti-log of the mean real rental price indicates an average retail rental price

of approximately $9.38 per square foot in 1982–84 dollars. Similarly for 1986–95,

the average real ten-year Treasury-bond yield is approximately 2.3%.

The regression results for the estimated demand, supply and price equations are shown

in Exhibit 3.11 Because the data are time series and cross-sectional, a regional census

dummy variable is introduced into the regressions to capture the effects of variation

specific to metropolitan areas.12 In addition, the set of estimated equations seen in

Equations (5), (6) and (7) has an identification problem. Therefore, a three-stage

generalized least squares procedure (TSGLS) is implemented. In the first stage,

quantities and rental prices are regressed on the exogenous variables to establish the

fitted values. The instruments include the CPI, year, credit, retail sales, lagged retail

rent, the real ten year Treasury-bond rate and the aggregate index of land regulation

and availability. In the second stage, the fitted values for supply quantities, demand

quantities and rents are applied together with the other independent variables as shown

in Equations (5), (6) and (7). The model with fitted values is first estimated ignoring
VOLUME 16, NUMBER 1, 1998

Exhibit 3

Three-Stage Regression Results

Coefficient Demand Equation Supply Equation Price Equation

Constant 3.44

(29.39)

10.58

(28.48)

0.49

(37.38)

Real Retail Sales (SM,t) 0.87

(62.34)

Mean (Real) Effective Monthly Rent per

S.F. (PM,t)

20.20

(25.96)

Mean (Real) Effective Monthly Rent per

S.F. Lagged (PM,t21)

0.49

(2.84)

0.88

(176.32)

Aggregate Index of Land Regulation from

Principal Components (LRM,t)

20.12

(23.21)

Natural Log of the Real Ten-Year T-Bond

Yield (BDt)

20.03

(20.34)

Vacancy Rate (VRM,t) 20.10

(223.30)

F-Value 964 16 5,056

Adj. R2 0.98 0.49 0.99

Mean AR(1)r 0.15 0.15 0.15

Note: The regression results do not report the regional census dummy variables that are present

to avoid a specification problem. An F-test of the full and reduced regression specification (without

regional census variables) indicates the regional dummies are statistically significant at the .01

level. There are n 5 160 observations.

the autocorrelation in order to obtain a consistent estimate of r for each MSA. In the

third step, the r estimates are applied to the data set using a Cochrane-Orcutt

transformation; in this transformation, zit 5 Zit 2 rZi,t21 where Zit and Zi,t21 are the

dependent and independent variables (Greene, 1993). The results reported in Exhibit

3 are the application of the transformed data to Equations (5), (6) and (7) including

respective regional census dummies.13

The demand regression shown in Exhibit 3 is statistically significant at the .01 level,

and the regression has an adjusted R2 exceeding 98%. The coefficient estimates for

the retail sales and rental price coefficients possess the correct theoretical signs and

statistical significance at the .01 level. The rental price coefficient of 20.2 shows that

a 1% change in the rental price results in a 20.2% change in demand for retail space.

Therefore, the demand for retail space is highly inelastic with respect to rental price

changes.

The estimated demand model also suggests that retail sales are very important in

shaping the demand for retail space. The retail sales coefficient of 0.87 is a little less

than 1.0, indicating that a 1% change in sales creates approximately a 0.87% change
in retail space demand. The retail sales coefficient of near unity supports the earlier

findings of Benjamin, Jud and Okoruwa (1993) and Benjamin, Jud and Winkler (1995)

who show that retail sales influence the quantity of retail space.

The second column in Exhibit 3 reports the results of the supply equation regression.

The regression equation is statistically significant at the .01 level. Our findings confirm

that retail space supply responds to variations in price and land regulation/availability.

The rental price coefficient of 0.49 indicates that the supply of retail space is inelastic

with respect to price.14 The land regulation/availability index coefficient is negative,

revealing that more restrictive land-use regulations restrict the supply of retail space.

The cost of capital coefficient is anticipated to be negative, as higher interest rates

should reduce retail space availability. The bond rate coefficient, however, is not

statistically significant. The real interest rate appears to have a minimal influence on

the supply of retail space during this time period.

The third column of Exhibit 2 shows the results of the price equation regression.

Almost all of the variation in rental prices is explained by the two included variables:

the mean real rental price is directly associated with the previous rental price. Holding

constant the monthly rent, a 1% increase in the vacancy rate decreases the rental price

by 0.1%; rental prices, therefore, are inelastic with respect to changes in vacancy.

Implications

The implications for structural changes in supply and demand for retail space

including the problem of over-storing are examined in more detail by means of

simulation analysis. Using the estimated coefficient values shown in Exhibit 3 and the

mean values of the variables from Exhibit 2, we show the separate effects of 10%

autonomous increases in demand and supply in Exhibit 4.

As shown from data from the nineteen MSAs contained within our sample, a 10%

increase in demand initially results in an increase in rent (15.6%) and a large drop

in the existing vacancy rate (248.0%). In subsequent periods (additional years) as

supply begins to rise, the increase in rent slows and the vacancy rate rises. The system

stabilizes after ten periods with higher rent and a lower vacancy rate and with larger

quantities of shopping space demanded and supplied. An increase in supply of 10%

leads to an initial fall in rent and an increase in the vacancy rate. As demand rises in

response to lower rent, the pace of the decline in rent slows and the vacancy rate

falls. In the end, the system stabilizes with rent lower and the vacancy rate higher

along with both quantity demanded and supplied higher.

This sensitivity analysis clearly identifies the risks to shopping center developers.

Developers who respond to positive demand shifts by building new shopping space

should recognize that additional market supply following the increase in demand will

slow the increase in rents and raise vacancy rates. Likewise on the supply side,

moderate supply shifts decrease rents and raise vacancy rates as a result of the relative

price inelasticity of shopping space demand.
VOLUME 16, NUMBER 1, 1998

Exhibit 4

Simulation Results

Period

Quantity

Demanded (%)

Quantity

Supplied (%) Rent (%)

Vacancy

Rate (%)

Panel A: 10% Increase in Demand

1 7.3 0.0 5.6 248.0

2 20.8 2.5 2.0 25.3

3 20.4 0.9 0.9 8.0

4 20.2 0.4 0.4 3.3

5 20.1 0.2 0.2 1.5

Total Change 5.8 4.1 9.3 28.7

Panel B: 10% Increase in Supply

1 1.6 9.5 23.8 32.8

2 0.9 21.7 22.3 29.5

3 0.5 21.0 21.3 26.2

4 0.3 20.6 20.7 23.8

5 0.2 20.3 20.4 22.2

Total Change 3.7 5.5 29.1 8.7

Summary and Conclusions

In this article, we develop a simultaneous model of retail space demand and supply

that includes vacancy rate. To estimate the model, we employ MSA data from twenty

metropolitan markets for the period 1986–95. Our empirical results reveal inelastic

price demand and supply elasticities for retail space. Because demand and supply are

price inelastic, shifts in demand (supply) are found to result in relatively large changes

in rent and relatively small changes in quantity demanded (supplied). Rental prices

are largely explained by the previous year’s rental price and the current year’s vacancy

rate, with higher vacancy rates resulting in lower rental prices.

In addition to the effects of price, we find that the demand for space is strongly

influenced by the real level of retail spending; in particular, space demand seems to

rise slightly less than in proportion to increases in real retail sales. On the supply side,

the supply of space is negatively affected by more stringent land-use regulation and

less land availability, while capital costs, as measured by interest rates, do not appear

to have a significant impact on supply.
Appendix

Pearson Correlation Coefficient Matrix

(Probability in Parentheses)

SALES RETRENT LRETRENT RBOND FACTOR1 RETVR

SALES 1.000

(0.0)

0.249

(0.002)

0.226

(0.004)

0.034

(0.666)

20.268

(,0.001)

20.112

(0.159)

RETRENT 0.249

(0.002)

1.000

(0.0)

0.963

(,0.001)

0.091

(0.255)

0.375

(,0.001)

20.200

(0.011)

LRETRENT 0.226

(0.004)

0.963

(,0.001)

1.000

(0.0)

20.002

(0.975)

0.357

(,0.001)

20.119

(0.134)

RBOND 0.034

(0.666)

0.091

(0.255)

20.002

(0.975)

1.000

(0.0)

0.002

(0.984)

20.138

(0.082)

FACTOR1 20.268

(0.001)

0.375

(,0.001)

0.357

(,0.001)

0.002

(0.984)

1.000

(0.0)

20.298

(,0.001)

RETVR 20.112

(0.159)

20.200

(0.011)

20.119

(0.134)

20.138

(0.082)

20.298

(,0.001)

1.000

(0.0)

Notes

1 Casey (1996), Ludgin, Perlmutter and Boss (1996) and many others report on the widespread

weakness in the retail space sector. Similarities exist between the retail space glut of the 1990s

and the office space glut of the 1980s when office space supply exceeded demand. Recent

bankruptcies among regional mall specialty stores such as Merry Go Round, Petrie Stores and

Discovery Zone have raised the risks of retail investing. Ludgin et al. (1996) note that

capitalization rates for retail properties have increased by 100 to 150 basis points during 1996

to reflect the growing uncertainty associated with investments in retail real estate. These authors

note write-downs in mall values of 5%–15% due to the higher cap rates associated with greater

perceived risk.

2 Additionally, those per capita supply amounts apply only to shopping center space and exclude

free-standing big box space such as that from the rapidly expanding Wal-Mart and Target chains.

Casey (1996) reports that the amount of free-standing store space constructed on a yearly basis

doubled between the 1980s and the 1990s to 79 million square feet annually. Delisle, Grissom

and Neyman (1995) comment that much of the big box retail space expansion is in the form

of properties that are self-financed by retailers who seek market share at the expense of

individual store profitability.

3 Wheaton and Torto (1995) document long-term declines in inflation-adjusted retail sales per

square foot and inflation adjusted sales per retail worker. They also state that the growth of

retail sales has been about half the construction rate for all retail space and only about a quarter

of the construction rate for shopping center space. Further, the U.S. Bureau of the Census’

Annual Retail Trade Survey and the National Research Bureau show the growth in sales of

shopping centers to have been less than that of total retail sales. Total retail trade increased

6.3% in 1993 and 7.5% in 1994, while shopping center sales have only increased 5% in 1993

and 5.5% in 1994. Casey (1996) reports that sales per square foot growth in regional shopping

centers has been a sluggish 2.1% in 1993, 1.8% in 1994 and 0.5% in 1995.

4 Slowing occupancy rates for retail space hide the fact that growing demand based on increasing

retail sales exists for retail space, but not in the amount of retail supply that is being created.

Casey (1996) comments that new retail space represents a net addition to supply because much

of the outmoded retail space reappears as recycled retail space in the hands of new owners.
VOLUME 16, NUMBER 1, 1998

The potential additional repricing of retail property values reflects the increasing risks associated

with the mismatch between retail demand and supply.

5 The possibility that non-store retailing such as direct marketing and electronic retailing will

erode traditional retail sales is a growing threat. Electronic shopping methods include on-line

retailing through the World Wide Web or through an online service provider, television shopping

and CD-Rom retailing. Consumers may bypass traditional store retailers when electronic

retailing gains widespread acceptance. Although electronic retailing is still in its infancy,

Wheaton (1996) and Miller (1996) comment that electronic retailing including Internet retail

sales could displace substantial retail space. Miller further comments (p. 21), ‘‘We have yet to

see the electronic market unleashed, and when we do, the retail market share could easily exceed

double the current estimate of 12% of all retail sales. Electronic retailing may take a generation

to mature, but there is no doubt that it can go far beyond the limits of direct mail.’’

6 For a review of multi-family housing studies in this area, see Jud, Benjamin and Sirmans

(1996).

7 Lagged retail sales are not included in the demand equation, but because of the lagged effect

of prices and vacancy rate, any change in retail sales produces a lagged effect on price, quantity

and vacancy rate. See the next section.

8 Rental price data spanned 1986–95 for eleven MSAs, 1988–95 for one MSA and 1990–95

for seven MSAs.

9 F. W. Dodge has been collecting square footage data since 1967 for use by the construction

trades and others. Information concerning what is being built and where it is being built is

reported.

10 Principle component analysis is used to explain the variance-covariance data structure through

a lesser number of linear combinations of the original variables. The principal components

replace the original variables (see, Johnson and Wichern, 1982).

11 The Pearson correlation coefficient matrix presented in the Appendix shows no substantial

multicollinearity among the independent variables in the demand, supply and price equations.

12 A fixed effects model with MSA groups would typically be appropriate for the empirical

regressions; however, regression coefficients are collinear in this specification. The aggregate

index of land regulation and availability estimated from principal components possesses a

unique value for each MSA. Therefore, a MSA fixed effects specification with the land

regulation/availability variable leads to substantial regressor collinearity. An alternative

specification is to use regional census dummy variables to control for variation associated with

specific MSAs, which minimizes collinearity problems while capturing most of the variation

associated with the MSA group variables. In addition, the real bond rate variable controls for

most of the time variation.

13 Autocorrelation is removed by MSA because census regions usually have multiple MSAs. To

remove autocorrelation, each cross-sectional unit must show consecutive and unique data points

for the time series. The regional census variables are included in the regression equations to

allow for separate intercepts for each region. The results reported with the transformed data are

homoskedastic.

14 Supply is relatively non-responsive to changes in current prices.

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