- Email: [email protected]
Price discrimination in shopping center leases
,O”RNAL
OF URBAN
ECONOMICS
32, 299-317
Price Discrimination
(19%)
in Shopping
Center Leases*
JOHN D. BENJAMIN Department
of Finance
ami Real Estate, Kogod School The American University, Washington,
of Business Administration, DC 20016
GLENN W. BOYLE Department
of Finance
and Quantitative Dunedin,
Analysis, University New Zealand
of Otago,
P.O. Box 56,
AND
C.F. SIRMANS Department
of Finance, School Connecticut,
of Business Storrs,
Administration, Connecticut 06269
The University
of
Received April 29, 1991; revised August 28, 1991 A simple model of rent determination for homogeneous shopping center space predicts that landlords use tenant characteristics, such as default probability and customer traffic-generating potential, to set rental rates in a discriminatory manner. Empirical tests conducted on a sample of shopping center leases support these predictions. Differences in contractual provisions do not appear to explain the observed rent dispersion among tenants favored by price discrimination, but are of importance for determining the rental liability of other tenants. Q 1992 Academic Press.
Inc.
1. INTRODUCTION Most recent contributions to the theory of leasing have emphasized the competitive nature of leasing markets. ’ For instance, Miller and Upton [ll] show that the equilibrium rental payment on a single-period lease depends on the characteristics of the asset being leased and on aggregate asset market conditions, but is independent of lessee characteristics. Extending Miller and Upton’s analysis intertemporally, McConnell and Schallheim [lo] demonstrate that, for a given asset, the equilibrium rent *We appreciate the cooperation of a Greensboro, North Carolina, shopping center developer. Helpful comments were received from Jan Brueckner, Julian Diaz, III, Jim Shilling, Leslie Young, and two reviewers. Any oversights or omissions are, of course, our own responsibility. ‘See, for instance, Lewellen et al. [S], Miller and Upton [ll], Myers et al. [12], and McConnell and Schallheim [lo]. Brueckner [3] has recently provided a theoretical analysis of the problem of optimal space allocation in shopping centers. 299 0094-1190/92 $5.00 Copyright Q 1992 hy Academic Press. Inc. All rights of reproduction m any form rexwed.
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AND SIRMANS
depends on the provisions and contingencies contained in the lease contract. Schallheim et al. [14] argue that rent should also depend on the lessee’s financial condition, since this presumably reflects the probability of default, but find little evidence to support this prediction when tested against actual rents on a wide range of leased assets. They do, however, find evidence supporting some predictions of the Miller-Upton and McConnell-Schallheim models. In contrast, other researchers, for example Flath [5] and Smith and Wakeman [15], recognize that, at least for specialized assets, equilibrium rents can be the product of noncompetitive forces. In commercial real estate, for example, concurrent selling markets frequently do not exist and, moreover, releasing is often prohibited so that lessees are effectively segmented. Both of these features encourage discriminatory rent setting and are frequently encountered in the leasing of shopping center space. In this paper we examine whether shopping center rents for homogeneous tenant spaces and services meet the Phlips [13] criterion for price discrimination. Phlips (p. 6) has proposed that “. . . price discrimination should be defined as implying that two varieties of a commodity are sold . . . to two buyers at different net prices, the net price being the price (paid by the buyer) corrected for the cost associated with the product differentiation.” In the context of commercial real estate, this implies that shopping center rents reflect price discrimination to the extent that rent dispersion, over and above that attributable to differences in contractual provisions, can be explained by differences in tenant characteristics. Our premise, therefore, is that variations in shopping center rents are linked not only to the product differentiation induced by differing contractual provisions, but also to price discrimination induced by heterogeneity in tenants. Our empirical analysis indicates that tenant characteristics are indeed important in explaining rent dispersion. Somewhat unexpectedly, however, we cannot reject the possibility that variations in rent are unrelated to differences in contractual provisions, at least for those tenants favored by price discrimination. Of course, our finding that rent depends on tenant characteristics need not indicate price discrimination on the part of landlords if the economic cost of providing space also depends on tenant characteristics. Readers who have a strong prior belief that the cost of providing space is the same regardless of who it is being provided to will interpret our results as evidence in favor of price discrimination. Others will prefer to reserve judgement until evidence regarding the relationship between tenant type and landlord costs become available. In a previous paper (Benjamin, Boyle, and Sirmans [2]), we tested for the existence of a tradeoff between base rent and percentage (or overage) rent using a sample of shopping center lease contracts. Specifically, we regressed base rents on percentage rents, threshold sales levels (the
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CENTER
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critical sales level beyond which a tenant pays percentage rent), and a number of other factors which our model suggested might be important for determining base rents. We found that base rents are negatively related to percentage rent rates and positively related to threshold sales levels. As both of these relationships are statistically significant, this indicates that, all else being equal, lease contracts requiring high (low) base rents specify low (high) percentage rent liabilities on average. If price discrimination according to tenant “quality” exists in the setting of shopping center rents, then it is presumably reflected in total rental liabilities and not just in base rents. Consequently, our earlier analysis is incapable of shedding light on the importance of tenant characteristics (or contractual provisions) for rent setting since in a simple multiple regression of base rents on percentage rents and tenant characteristic and contractual provision variables, the coefficients on the latter sets of variables represent only the impact of those variables on base rent, holding percentage rent constant. Given our finding of a tradeoff between base and percentage rents, higher base rent need not imply a higher total rent liability so that these regression coefficients are relatively meaningless when it comes to assessing the relationship between total rent and tenant and contract variables. This paper extends our previous work in two main ways. First, we construct plausible, albeit ad hoc, indexes of total rent liability so that in our regression analysis these appear as the dependent variable instead of base rent. Second, in contrast to our earlier paper in which tenant characteristics and contractual provisions served only as variables which needed to be controlled for in analyzing the relationship between base and percentage rents, we examine in detail the relative contributions of these groups of variables in rent setting. In Section 2, we advance some hypotheses regarding tenant-based discrimination in rent setting and demonstrate their coherence within a simple theoretical model. In Section 3, we briefly describe the various contingencies and provisions commonly observed in shopping center lease contracts and their probable effect on rents. Section 4 describes the data and Section 5 outlines the empirical model. Section 6 presents our empirical analysis and Section 7 contains concluding remarks. 2. THEORY Shopping center leasing is characterized by several unique institutional practices. Rents are usually set along two dimensions: A base contract rent and a (so-called) overage rent. The latter requires the tenant to pay rent (in addition to the base rent) as a percentage of total sales revenue above some predetermined (at the time of lease contracting) level. Shopping center space and services are mostly homogeneous in that they are typically subdivisions of a larger structure and that the services for all
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AND SIRMANS
tenants are similar. Shopping centers are also characterized by a variety of tenant types including national and local chains, as well as “anchor” tenants that attract shoppers to the center. Given that shopping center space and services are homogeneous, we hypothesize that landlords use two tenant characteristics to determine the different rents they charge tenants: first, the probability of default and, second, the perceived ability of a tenant to generate customer traffic, i.e., the external or indirect influence of an individual tenant on total shopping center revenues.2 A high probability of default implies low expected rental receipts; the landlord therefore extracts compensation in the form of higher rent. Increased customer traffic raises overall shopping center sales and thus increases the landlord’s overage rental receipts. A high trafficgenerating potential, usually associated with tenants who have high anticipated sales, implies a strong indirect influence on landlord revenues. The landlord therefore compensates such tenants for this positive externality by reducing rent. To explain this latter mechanism more fully, note that the leasing of shopping center space to tenant i may help (hurt) existing tenants’ sales if tenant i’s business is complementary to (competitive with) that of existing tenants, For instance, if tenant i has high anticipated sales that attracts shoppers, then the presence of tenant i is beneficial to existing tenants. On the other hand, if tenant i is, for example, a standard shoe retailer, then his presence is generally detrimental to standard shoe retailers already in the center. Such linkages affect the landlord by altering the rent revenue received from existing tenants. To see that these conjectures are theoretically possible, consider the following simple model of ex ante landlord rent-setting behavior.3 There are IZ tenants; the subset (1,. . . , m} consists of new tenants while the subset (m + 1,. . . , n} denotes existing tenants. The landlord is faced with the beginning-of-period problem of deciding the optimal quantity of shopping center space to lease to each of the m new tenants.4 Each new tenant i = 1,. . . , m has an end-of-period sales revenue function Si
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CENTER
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19;. If the realized value of Bi is f3+, then new tenant i pays rent biQi to the landlord, where bi is tenant i’s rent per unit of space.5 If Bi = O,, then tenant i defaults and the landlord receives nothing.There is a fixed probability pi that Bi = f3-. For simplicity, the cost associated with providing space for the new tenants is assumed to be a linear function of that space. That is:
C
(1)
i=l
where o and k are positive constants. 6 In practice of course, landlord costs are likely to be a nonlinear function of the space rented; this possibility is subsequently incorporated in our empirical analysis. As well as rent from new tenants, landlords also receive rent from existing tenants. This is assumed to have two components: (i) a component with expected value v which depends only on the business activities of these existing tenants (i.e., is independent of the space leased to new tenants) and (ii) a component depending on the space leased to new tenants via the external effect of the latter on existing tenant sales. We assume that the second component is a linear function of the Qi’s, i=i m. Thus, if R is the landlord’s expected profit from existing tenahts,‘;hen the expected value of R can be written as:
E[R(LL . . ..Q.)]
= v +
EAiQi
(2)
i=l
where the constant Ai is the marginal indirect or external influence of tenant i’s presence on the profit the landlord expects to receive from existing tenants. The landlord is assumed to maximize expected profits:
mQa ,fY[{Cl-Pi)bi(Qi> 1
I
+ Ai - P}Q,] +v--0.
(3)
1
jIn practice, most leases call for rent to be paid in advance. In that case, b,Qi should be interpreted as tenant i’s advance rent for next period. For simplicity, we abstract from percentage rent for new tenants. 6We assume that p is sufficiently large to ensure that landlord profits are not an infinitely increasing function of any Qi.
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BENJAMIN,
BOYLE,
AND
Let us suppose that each new tenant demand function,
SIRMANS
i = 1,. . . , m has a log-linear
where pi is a positive constant. After substituting (4) into (3), the first-order conditions for (3) imply that the landlord supplies Qi units of shopping center space to each tenant i where Qi satisfies:
Hence, by (4), the rent charged to tenant i is: (CL
bi = (1 -pi)(l
-
*i)
-pi)
’
(6)
Equation (6) indicates that tenant i’s rent is positively related to pi, and negatively related to Ai, consistent with our hypotheses. 3. SHOPPING
CENTER
LEASE
PROVISIONS
The above discussion assumes shopping center leases to be generic insofar as the space utilized and the flow of services prescribed in each contract are the same for all tenants; the contracts differ only in that they specify different rental liabilities for different tenants. Once this assumption is relaxed, the role of contractual provisions and contingencies must be considered. This section briefly describes a number of these features and their probable relationship to rent. In addition to specifying the tenant’s rental liability, a lease contract stipulates the length of the lease and the amount of space leased. Rent should be inversely related to lease length for two reasons. First, a longer lease benefits the landlord by reducing his exposure to tenant search expenses, foregone rent, and reletting and renovation charges. Second, as Flath [S] has argued, the shorter the lease, the greater the wear and tear on the property. While a longer lease may also benefit the tenant, this is likely to be at least partially offset by the restrictions imposed on his freedom of action. With respect to the amount of space leased, the costs of negotiating and servicing lease contracts per dollar of rent probably decreases as the square footage increases, and the effect is probably nonlinear. Three common provisions found in shopping center leases are rent escalation, tenant renewal option, and landlord right to cancel. A rent
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CENTER
LEASES
305
escalation provision benefits the landlord by entitling him to mandated or inflation-linked increases in base rent over the life of the lease. Hence, all else being equal, the landlord should be prepared to accept a lower initial rent. A tenant renewal option benefits the tenant by giving the right to renew the lease at expiration if he deems it to be in his interest to do so. Hence, all else being equal, the tenant should be prepared to pay a higher rent. Finally, a landlord cancellation right benefits the landlord by giving him the right to cancel the lease prior to expiration if he deems it to be in his interest to do so. Hence, all else being equal, he should be prepared to accept a lower rent. 4. DATA To obtain data with which to test our hypotheses, we examined the files of a shopping center developer located in Greensboro, North Carolina. Greensboro is part of the Greensboro, High Point, and Winston-Salem, North Carolina, metropolitan statistical area and is commonly referred to as the Piedmont Triad. Greensboro and the Triad have experienced considerable growth in the past two decades and are characterized by a decentralized, suburban pattern of land use. The population approaches 1 million persons, giving it a top 50 ranking among metropolitan areas. We have no reason to suspect that shopping center conditions in Greensboro differ significantly from those in other U.S. markets. Data were obtained for 103 neighborhood and community shopping center leases written during the period January 1985 to December 1987. The five shopping centers from which these data are drawn display minimal structural and locational variation among the leased spaces, are all of strip design,’ and are all located within a 3-mile radius in the same geographic area of the city. They are comparable in architecture, occupancy (near lOO%), age of construction, and amenities. The individual tenant spaces are homogeneous in that they are all subdivided spaces of large cinder block and concrete floored structures. In addition, tenant services supplied such as trash removal and common area maintenance are similar. However, the shopping centers differ somewhat in size: The smallest center has a total area of 13,930 square feet while the largest covers 390,000 square feet. For each lease, the base rent per square foot (BRNT), the thousands of square feet leased (SQFT), the percentage-of-sales rental rate (PRNT), the per square foot sales level beyond which the tenant pays percentage rent (SBKP), and the lease length in months (TERM) were recorded. In addition, we were able to determine which contracts contained each of the ‘A strip shopping center is a group of stores connected by a sidewalk which runs along the front of the stores.
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TABLE 1 Descriptive Statistics Characterizing the Sample of 103 Shopping Center Leases Originated in Greensboro, NC, during the Period January 1985-December 1987 A. Frequency distribution for base rent per square foot (BRNT) Base rent per square foot $8 or less $8.01 to $10 $10.01 to $12 $12.01 to $14 $14.01 to $16 $16.01 to $18 Over $18 Maximum = $19.55, Minimum
Number of leases
Percent of total
7 20 33 16 19 6 2
6.8 19.4 32.1 15.5 18.4 5.9 1.9
= $6.39, Mean = $11.91, Median = $11.48
B. Frequency distribution of square feet leased (SQFT) Square feet leased (in ‘000s) Number of leases Percentage of total 1 or less 1.1 to 2 2.1 to 4 4.1 to 8 8.1 to 25 Over 25
3 41 39 12 7 1
Maximum = 85.61, Minimum
2.9 39.8 37.9 11.6 6.8 1.0
= 0.61, Mean = 4.11, Median = 2.257
C. Frequency distribution of percentage rent rates (PRNT) Percentage rent rates Number of leases Percentage of total 0 to 0.02 0.025 to 0.05 0.055 to 0.07 Over 0.07
12 30 53 8
11.7 29.1 51.4 7.8
Maximum = 0.100, Minimum = 0.000, Mean = 0.052, Median = 0.060 D. Frequency distribution of sales levels beyond which the tenant pays percentage rent (SBKP) Sales level ($000 per square foot) .lOO or less .lOl to .I75 ,176 to .250 over .251
Number of leases
Percentage of Total
14 31 50 8
13.6 30.1 48.5 7.8
Maximum = 0.383, Minimum = 0.0, Mean = 0.176, Median = 0.200
SHOPPING
CENTER
307
LEASES
TABLE l-Continued E. Frequency distribution by length of lease (TERM) Length of lease (months) Number of leases Percentage of total 12 or less 13 to 24 25 to 36 37 to 100 101 to 200 Over 200
4 10 29 47 11 2 Maximum = 240, Minimum
Lease characteristic
3.9 9.7 28.1 45.7 10.7 1.9
= 12, Mean = 38.1, Median = 60
F. Lease Characteristics Number of leases
Cancellation right Rent escalation right Renewal option
5 25 30
Percentage of total 4.86 24.27 29.13
lease provisions discussed in the previous section. Summary statistics are reported in Table 1. Panel A contains a frequency distribution of BRNT and shows that a majority of the tenants in our sample paid between $8 and $12 per square foot. Panel B provides a frequency distribution of SQFT. Almost 80% of tenants leased between 1000 and 4000 square feet. Only one tenant leased over 25,000 square feet. Frequency distributions of PRNT, SBKP, and TERM appear in Panels C, D, and E, respectively. Panel F provides information about other lease provisions. Less than 5% of the leases in our sample contained a landlord cancellation right, whereas 24% (29%) contained a rent escalation provision (tenant renewal option). The information in Panel F was recorded in a series of binary variables. For instance, the variable CNCL was assigned a value of one if the lease contained a landlord cancellation right and zero otherwise. The variables ESCL and RNWL denote analogous accountings for rent escalation rights and tenant renewal options respectively. 5. EMPIRICAL
MODEL
The model of Section 2 identified a set of tenant characteristics which landlords may use to price discriminate. However, Section 3 indicates that shopping center rent dispersion may also be due to heterogeneity in lease contract provisions. Our empirical model includes each of these to determine their importance for rent dispersion. A. Dependent Variable
Anticipated rent liabilities.
total rent liability depends on both the base and overage As noted earlier, shopping center lease contracts frequently
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require that tenants pay as overage rent a percentage of sales revenue over and above some pre-specified minimum sales level. The inclusion of overage rent in the form of a percentage rent provision increases the total rent liability of the tenant, all else being equal. Overage rent is clearly increasing in PRNT, but also is decreasing in SBKP because this decreases the amount of overage rent owed, all else being the same.8 The three components of the rent, BRNT, PRNT, and SBKP, are negotiated simultaneously at the time of contracting and together they determine the actual rent paid. Thus, the anticipated total rent liability TRNT, can be written as9 TRNT =
f(BRNT, PRNT, SBKP) BRNT
if PRNT > 0, if PRNT = 0,
fi > 0; f~ > 0; f3 < 0
where fi is the partial derivative of f with respect to its ith argument. To quantify TRNT in a form suitable for regression analysis, we used TRN-I-1
= BRNT(l + PRNT) l+SBKP
(7)
and TRNT2 = BRNT( 1 + PRNT)“”
+SBKP).
(8)
TRNTl differs from TRNT2 in that 1% changes in PRNT or SBKP have a greater impact on the former than on the latter. B. Independent Variables
To obtain proxies for the tenant characteristic variables, we used The Urban Land Institute’s “Dollars & Cents of Shopping Centers,” which contains information on shopping center leases. This document [16, p. 51 defines a national chain store to be “. . . a business operating in four metropolitan areas located in three or more separate states,” an independent store to be “ . . . a business operating in not more than two outlets located in only one metropolitan area,” and a local chain store to be “ . . . a business that does not fall into either of the other two categories.” Therefore, with regard to the probability of default (pi), company-owned national and local chain tenants seem likely to have lower default risk due ‘For some empirical evidence on this, see Benjamin, Boyle, and Sirmans [2]. ‘Lease contracts sometimes contain late payment charges and rent concession agreements (92 and 3, respectively, in our sample) but, in our sample, these are generally of insufficient magnitude to materially alter the effective rent liability.
SHOPPING
CENTER
309
LEASES
to their greater creditworthiness and operational experience.” Thirty-five of the tenants in our sample are members of such chains and these were assigned a value of one for the dummy variable CHN. The Urban Land Institute [16, pp. 128-2051 also provides a detailed breakdown of median sales volume per square foot of gross leased area according to tenant classification for community and neighborhood shopping centers. We recorded this information in the variable MSLS. As high sales tend to be associated with greater customer traffic, we decided to use MSLS as a proxy for the externality variable hi, no superior measure being available. As our sample covers a 3-year time span and includes five shopping centers, we created some additional sets of variables to control for intertemporal and intercenter variation in rental liability. To control for the former, a value of one was assigned to the dummy variable YEAR 2 (YEAR 3) if the lease originated in 1986 (1987) and zero otherwise.” We used two methods to control for intercenter variation in rental liability: Either (i) a value of one was assigned to the dummy variable CENTj (j = 1, . ..) 4) if the lease originated in center j or (ii> the lease was characterized according to the size (CENSIZE) of the center in which it originated. Finally, to allow for possible economies of scale in leasing space to large tenants, we included SQFI as an independent variable in our regressions and since the effect is probably nonlinear, we also included the square of the amount of square feet leased [SQFT2].‘* C. Model
The following estimated: InTRNT,
empirical
models of shopping center rental liabilities
= 70 + YiSQFTi
+ y,SQFT2,
+ f: GjYEARj, j=2
are
+ y3TERMi
+ ~ KjCENTji j=l
+ PlCNCLi
+ P,ESCL,
+ P,RNWLi
+ P,CHNi
+ PsMSLSi
+ Ei
(9.4)
“This might not be true of franchised outlets. However, our sample contained only company-owned operations. “Eighteen of the leases in our sample were signed in 1985, 41 in 1986, and 44 in 1987. “Being measures of quantity purchased by the tenant, both SQPT and TERM are likely to have some endogeneity problems, i.e., low rents may induce tenants to sign large and long leases. Since we are concerned with these variables insofar as they control for size and length-induced variations in rent, we chose to ignore this issue here.
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BENJAMIN,
InTRNT,
BOYLE, AND SIRMANS
= ~0 + 7lSQFTi + i
+
GjYEARji
yzSQFT2,+
y,TERMi
+ kCENSIZE
j=2
+ P,CNCLi + P,CHNi
+ P,ESCLi + p,MSLSi
+ p,RNWLi + Ei*
(9B)
We used the natural log of TRNT as the dependent variable so that the regression coefficients can be interpreted as indicating the percentage effect on TRNT of a unit change in the corresponding independent variable.13 6. RESULTS We first estimated various forms of (9) for the total sample to test the effects of tenant and lease characteristics on rents. We found that, in general, tenant characteristics are significant predictors of cross-sectional variation in TRNT, but that lease provisions are less important. We then examined various subsamples of our data set in order to test for possible differences in rent setting according to tenant characteristics and lease provisions. These results indicate that there are some significant interaction effects in lease pricing. A. Total Sample Results
The results of our ordinary least-squares regressions using our entire sample of 103 leases appear in Table 2.14 Results are reported for the two measures of the dependent variable using the natural log of TRNTl (TRNT2). Models 1, 2, 5, and 6 use the dummy variables CENTj to capture rent variation across centers, whereas models 3, 4, 7, and 8 use center size CENSIZE (measured by total leasable area in square feet) to t31n recent years, the question of the correct functional form of hedonic models has arisen in the housing and rent property literature (see Butler [4], Halvorsen and Pollakowski [61,and Marks [9]). Butler reports that when scholars have compared alternative functional forms for hedonic indexes of housing, they find little basis for choosing one over another. Alternate linear and log forms model provided consistent estimates. In addition, Kennedy [7] has shown that empirical estimation using a semilogarithmic functional form with dummy independent variables induces a degree of bias in the estimated coefficients. The amount of bias depends on the magnitude of the estimated coefficient and its variance. In our sample, the adjustment suggested by Kennedy did not lead to any economically meaningful changes. t4Using variance inflation factors, condition indices, and eigenvalue procedures as outlined by Belsley, Kuh, and Welsch [I], no evidence of multicollinearity was discovered. Standard residual plots revealed no indication of heteroscedasticity. In addition, we checked for outliers/influential observations using the Belsley, Kuh, and Welsch statistic DFFITS. Regression results with outliers/influential observations deleted yielded comparable results.
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CENTER
311
LEASES
TABLE 2 Regressions of Total Rent on Contractual Provision and Tenant Characteristic Variables for a Sample of 103 Shopping Center Leases Originated over the Period January 1985-December 1987” Model’ Dependent Variableb CNCL ESCL RNWL CHN MSLS TERM SQFT SQFT2d CENSIZEd R2 F-value
1 -0.1159 (1.50) 0.0500 (1.12) 0.0895 (2.15) - 0.0845 (2.08) - 0.0014 (2.58) -0.0006 (1.01) - 0.0367 (5.08) 3.0730 (3.72) .60 11.77
variable 2
- 0.0840 (2.03) - 0.0014 (2.64) -0.ooo5 c.83) -0.0317 (4.59) 2.6236 (3.28)
.57 13.50
Dependent
= In TRNTl 3
4
- 0.0877 (1.08) 0.0407 c.87) 0.0967 (2.24) - 0.0736 (1.73) -0.0016 (2.81) - 0.0003 c.42) - 0.0397 (5.23) 3.4586 (4.01) 5.6448 (4.80)
- 0.0735 (1.71) - 0.0016 (2.90) -0.0002 C.31) - 0.0338 (4.74) 2.9097 (3.54) 4.6601 (4.16)
.55 12.13
.53 15.20
-
5 -0.1079 (1.39) 0.0231 c.52) 0.0857 (2.04) -0.1116 (2.73) - 0.0008 (1.47) - 0.0001 C.05) - 0.0280 (3.84) 2.2233 (2.67) .54 9.45
variable 6
-
-0.1131 (2.74) -0.0008 (1.49) - 0.0001 C.04) - 0.0241 (3.49) 1.9034 (2.38)
.52 11.01
= In TRNT2 ____I
8
- 0.0744 C.90) 0.0092 C.19) 0.1020 (2.32) - 0.0984 (2.27) - 0.0010 (1.79) 0.0003 c.52) - 0.0333 (3.93) 2.5255 (2.87) 5.3475 (4.46)
-0.1010 (2.30) - 0.0011 (1.83) 0.0003 t.44 - 0.0253 (3.49) 2.0717 (2.47) 4.2935 (3.77)
.46 9.05
.45 11.24
-
‘t statistics are in parentheses. ‘Independent variables are: dummy variables indicating whether lease contains a (i) landlord cancellation right (CNCL), (ii) rent escalation provision (ESCL), (iii) tenant renewal option (RNWL); a dummy variable if a tenant is a member of a national or local chain (CHN); median sales per square foot for merchant type (MSLS); length of lease (TERM); amount of square feet rented (SQFT); and the size of the center (CENSIZE). ‘Each model also included an intercept and a number of dummy variables designed to control for intertemporal (time dummies) variations in rent; models 1, 2, 5, 6 also included a number of dummy variables designed to control for intercenter (location dummies) variations in rent. The estimated coefficients for these are not reported in order to conserve space. Most were significant. dThe coefficient on SQPT2 should be shifted 10 places to the left; the coefficient on CENSIZE should be shifted 7 places to the left.
control for any intercenter variations. The degree of correspondence across models is quite strong. On average, tenants with leases containing a landlord cancellation right pay 8-10% less rent while those with leases containing a renewal option pay S-10% more rent. Thus, a cancellation right and the renewal option have about the same (absolute) effect on rents. The renewal option is
312
BENJAMIN,
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AND SIRMANS
significant in all of the models, whereas the cancellation right is less important. l5 The escalation provision is even less significant in all the estimates. By contrast, the coefficients on both the tenant characteristic variables are significant at the 1% level when In TRNTl is the dependent variable. When In TRNT2 is the dependent variable, MSLS is not significant at the 10% level while CHN is significant at the 1% level. On average, chain tenants pay 9-12% less rent while an extra $100 of (anticipated) per-square-foot sales lowers rent by O.l-0.2%. It is of course possible that some of the chain discount is due to other factors associated with the name of the chain, but most of it seems likely to be associated with lower default probability, consistent with our hypothesis. Similarly, the negative coefficient on MSLS indicates that tenants with high anticipated sales do indeed receive a rental discount, albeit a relatively small one. Further insight into the rent-setting process can be gained by comparing the coefficient on both CHN and MSLS when TRNTl is the dependent variable with their respective counterparts when TRNT2 is the dependent variable. As can be seen in Table 2, the coefficient on CHN is greater in absolute value when TRNT2 is used. Recalling that percentage rent has lower weighting in TRNT2, this indicates that the discount for lower default probability is received primarily through lower base rent. By contrast, the coefficient on MSLS is greater in absolute value when TRNTl is used, thereby indicating that the positive externality discount is received primarily through a lower percentage rent rate. This is intuitive: tenants which attract customers to the center have high sales themselves so that the appropriate economic incentive involves reducing the liability they incur in the process of generating such high sales and customer traffic, i.e., percentage rent. The implication of the regressions in Table 2, that contractual provisions have relatively little effect on rental liability, is somewhat surprising. To further examine this issue, we excluded the contractual provision variables (CNCL, ESCL, and RNWL) from the model. The results appear in the models 2, 4, 6, and 8 in Table 2. The R*s are lower when the contract provisions are excluded (typically by 2-3%). The F statistic of 2.67 obtained by comparing this with the unrestricted equation is statistically significant at the 10% level. Thus, for aggregate data, we find weak evidence to support the hypothesis that rental liability is related to the presence and prevalence of contractual provisions. Consistent with our discussion in Section 2, the effect of TERM is negative, but insignificant. As expected, rents decline significantly as the
“However, with only five leases containing a cancellation right, any results concerning CNCL seem likely to be somewhat idiosyncratic.
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CENTER
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313
amount of space leased (SQFT) increases. The effect also decreases with increased space, since the SQFT2 coefficient is positive.16 Finally, models 3,4, 7, and 8 indicate that rental liability is an increasing function of center size, as all coefficients on CENSIZE are significantly positive. This finding is intuitively plausible. Bigger centers typically contain more retail outlets and hence generate greater consumer synergies. Thus, proportionately more shoppers are attracted to large centers, thereby encouraging tenants to offer higher rental payments. B. Some Further Analysis: Subsample Results
Results for the total sample indicate that rental liability is influenced by tenant characteristics and, to a lesser extent, by variations in lease provisions. Given the importance of the latter variables for earlier work on leasing, our finding that these are only marginally significant for shopping center rents is surprising. However, interaction effects between the independent variables may be exaggerating this (lack of) effect. That is, it is possible that contractual provisions are important in determining the rental liability for some subset of tenants but not for others, thereby causing the coefficients obtained from aggregate data to be less significant. To investigate this possibility, we examined various subsamples of our data set. First, we examined the possibility that the rent-setting process differs according to size of center. Since the size of the center may itself act as a draw, it could be that having “draw” tenants is potentially less important as center size increases. Also, having a low probability of default tenant may be more important for a small center, thereby implying a larger rent discount. To test this hypothesis, we separated our sample into the observations in “large” versus “small” centers based on total square footage. In the large center sample we included the two centers with total area of 208,000 and 390,320 square feet. The small center sample contains the remaining three centers: 13,930, 79,657, and 122,397 square feet. The sample sizes are 53 and 50, respectively. These results are shown in Panel A of Table 3.17 In general, for our sample, the lease provisions appear to be more important for the large centers. The most striking result is the effect of CHN. Tenant default probability is much more important in the small centers, low probability generating substantial rent discounts in those centers. The MSLS effect is “However, the coefficient on SQFT2 is sufficiently small to ensure that this non-linear effect never dominates in our sample; i.e., the SQFT value needed for TRNT to increase in SQFT greatly exceeds the maximum SQFI’ value in our sample so that TRNT is a decreasing function of SQFT for all leases in our sample. “To conserve space, we report only the results using In TRNTl as the dependent variable. The results using In TRNTZ are essentially the same.
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TABLE 3 Regressions of Total Rent Liability on Contractual Provision and Tenant Characteristic Variables’ Panel A: large versus small centers CNCL ESCL RNWL MSLS Large Centers Small Centers
- 0.2541 (2.26) 0.0856 (0.94)
0.0188 (0.29) 0.0339 (0.63)
0.1106 (1.25) 0.0667 (1.69)
-0.0012 (1.54) - 0.0010 (1.45)
CHN
R*
F
-0.0094 (0.16) - 0.1786 (3.02)
54
4.43
.86
16.27
R2
F
62
12.4
MSLS
R*
F
-0.0038 (3.53) -0.0002 (0.30)
0.69
5.4
0.62
7.6
Panel B: difficult versus easy contracts DIPP CHN MSLS - 0.039 C.86)
- 0.077 (1.82)
- 0.0015 (2.70)
Panel C: chain versus independent tenants CNCL ESCL RNWL Chain Tenants Independent Tenants
- 0.0380 (0.21) - 0.2306 (2.85)
0.0164 (0.16) 0.0600 (1.31)
0.0166 (0.14) 0.0708 (1.77)
Note: t statistics are in parentheses. “Dependent variable is InTRNTl. Each regression also included an intercept, the lease length (TERM), the number of square foot leased (SQPT), and a number of dummy variables designed to control for intertemporal and intercenter variations in rent. The estimated coefficients for these variables are not reported in order to conserve space. See Eq. (9) for a description of TRNTI. Independent variables are: dummy variable indicating whether lease contains a landlord cancellation right (CNCL), dummy variable indicating whether lease contains a rent escalation provision (ESCLI, dummy variable indicating whether lease contains a tenant renewal option (RNWL), dummy variable indicating whether tenant is a member of a national or local chain (CHN), and median sales per square foot for merchant type (MSLS). DIPP is a dummy variable equal to one if a lease has at least two contractual provisions and zero otherwise.
virtually the same for large and small centers, indicating that customer drawing power is valued equally across center size. Our limited sample might not allow us to measure the effect of each individual lease provision. To test the hypothesis that contractual provisions have a significant impact on rents only when there are a sufficient number of them, we separated our sample into “difficult” versus “easy” contracts. The former were defined as those containing at least two of the three provisions discussed previously (26% of the sample fell into this category).
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These results are shown in Panel B of Table 3. The DIFF variable is equal to one if a lease has at least two provisions and zero otherwise.” While the DIFF variable has the correct sign, the effect is not significant. All of the other variables have the expected impact. As a further test, we split our sample into those tenants favored and not favored by price discrimination. These results are shown in Panel C of Table 3. In the first row, we report the results from rerunning (9)-excluding CHN-for a subsample of 35 company-owned national and local chain tenants, i.e., those leases which were assigned a value of 1 in CHN. In the second row, we report the results obtained from the same regression for the independent tenants (68 leases) in our sample. The results are illuminating. The rental liabilities of those tenants favored by price discrimination still appear to be unrelated to the presence of contractual provisions in their leases, but CNCL and RNWL are now significantly related to the rental liabilities of independent tenants at the 10% level or better. On average, independent tenants with leases containing a cancellation right pay 23% less rent while those with leases containing a renewal option pay 7% more rent. By contrast, while CHN tenants still appear to receive a small customer traffic discount (MSLS is significant at the 1% level), no such discount appears to accrue to independent tenants. Thus, there appears to be a separation of the rent-setting process along the following lines: The rental liability of tenants with low probability of default and high customer traffic potential seems to be primarily determined by the simple existence of these phenomena (as well as other non-contract-specific variables such as shopping center location) and is independent of contractual provisions, while the rental liability of other tenants seems to partially depend on the provisions included in the lease contract. 7. CONCLUDING REMARKS Recent treatments of the leasing process have concentrated on perfectly competitive markets. If this assumption is relaxed, then shopping center rent dispersion for homogenous tenant spaces and services could be due to discriminatory pricing practices on the part of landlords as well as to differences in contractual provisions. Our empirical results indicate that some portion of the contract-specific cross-sectional variation in shopping center rents for shopping centers with generic tenant space and services is due to tenant heterogeneity. For tenants favored by price discrimination, little or none of this variation is due to differences in lease contract ‘*For these estimates, we changed the definition of the RENEW variable such that it was set equal to zero if the lease contained a tenant renewal option and one otherwise. Thus all three provision variables have the same (negative) expected effect.
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provisions. These findings contrast with the view, prevalent in the financial economics literature, which stresses the importance of lease conditions in determining asset rents and indicates that product differentiation effects may be dominated by price discrimination effects in imperfectly competitive markets. The clearest picture to emerge from our analysis is that of a two-stage rent-setting process for homogeneous tenant space and services. First, low-risk, high traffic-generating tenants receive rental discounts. Second, the rental liabilities of other tenants are dependent on the contractual provisions which landlords and tenants agree to insert in the lease. However, we must stress that these findings are far from conclusive given the shortcomings of our data set. For instance, with so few leases containing cancellation rights, it is difficult to come to any firm conclusions concerning the importance of this provision. Moreover, our variables for low risk and high traffic generation are somewhat less than ideal. The analysis of this paper can be extended in a number of ways. First, as the theoretical structure assumes risk-neutral landlords, the introduction of risk aversion seems a worthwhile extension, particularly if the conclusions can be stated in a form amenable to direct empirical testing. A more challenging problem involves the explicit incorporation of contractual provisions and contingencies into the theoretical rent-setting model. This would permit the form of the lease contract to be derived as a function of tenant, landlord, and shopping center characteristics, thereby providing a formalization of the Smith and Wakeman [15] analysis. Empirically, extension of our methodology to other geographical markets and different assets may prove fruitful. REFERENCES 1. D. A. Belsley, E. Kuh, and R. E. Welsch, “Regression Diagnostics,” Wiley, New York (1980). 2. J. D. Benjamin, G. W. Boyle, and C. F. Sirmans, Retail leasing: The determinants of shopping center rents, AREUEA Journal, 18, 302-312 (1990). 3. J. K. Brueckner, Inter-store externalities and space allocation in shopping centers, Journal of Real Estate Finance and Economics, to appear, 1992. 4. R. V. Butler, The specification of hedonic indexes for urban housing,” Land Economics, 58, 96-108
(1982).
5. D. Flath, The economics of short-term leasing, Economic Inquiry, 18, 247-259 (1980). 6. R. Halvorsen and H. 0. Pollakowski, Choice of functional form for hedonic price equations, Journal of Urban Economics, 10, 37-39 (1981). 7. P. E. Kennedy, Estimation with correctly interpreted dummy variables in semilogarithmic equations, American Economic Review, 71, 801 (1981). 8. W. G. Lewellen, M. S. Long, and J. J. McConnell, Asset leasing in competitive capital markets, Journal of Finance, 31, 787-798 (1976). 9. D. Marks, The effect of rent control on the price of rental housing: An hedonic approach, Land Economics, 60, 81-94 (1984).
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10. J. J. McConnell and J. S. Schallheim, Valuation of asset leasing contracts, Journal of Fimnciul Economics, 12, 237-61 (1983). 11. M. Miller and C. Upton, Leasing, buying, and the cost of capital services, Journal of Finance, 31, 761-786 (1976). 12. S. C. Myers, D. A. Dill, and A. J. Bautista, Valuation of financial lease contracts, Jouma~ of Finance, 31, 799-819 (1976). 13. L. Phlips, “The Economics of Price Discrimination,” Cambridge Univ. Press, Cambridge (1983). 14. J. S. Schallheim, R. E. Johnson, R. C. Lease, and J. J. McConnell, The determinants of yields on financial leasing contracts, Journal of Financial Economics, 19, 45-67 (1987). 15. C. W. Smith, Jr., and L. MacDonald Wakeman, Determinants of corporate leasing policy, Journal of Finance, 40, 895-908 (1985). 16. Urban Land Institute, “The Dollars and Cents of Shopping Centers: 1987,” The Urban Land Institute, Washington, DC (1987).
OF URBAN
ECONOMICS
32, 299-317
Price Discrimination
(19%)
in Shopping
Center Leases*
JOHN D. BENJAMIN Department
of Finance
ami Real Estate, Kogod School The American University, Washington,
of Business Administration, DC 20016
GLENN W. BOYLE Department
of Finance
and Quantitative Dunedin,
Analysis, University New Zealand
of Otago,
P.O. Box 56,
AND
C.F. SIRMANS Department
of Finance, School Connecticut,
of Business Storrs,
Administration, Connecticut 06269
The University
of
Received April 29, 1991; revised August 28, 1991 A simple model of rent determination for homogeneous shopping center space predicts that landlords use tenant characteristics, such as default probability and customer traffic-generating potential, to set rental rates in a discriminatory manner. Empirical tests conducted on a sample of shopping center leases support these predictions. Differences in contractual provisions do not appear to explain the observed rent dispersion among tenants favored by price discrimination, but are of importance for determining the rental liability of other tenants. Q 1992 Academic Press.
Inc.
1. INTRODUCTION Most recent contributions to the theory of leasing have emphasized the competitive nature of leasing markets. ’ For instance, Miller and Upton [ll] show that the equilibrium rental payment on a single-period lease depends on the characteristics of the asset being leased and on aggregate asset market conditions, but is independent of lessee characteristics. Extending Miller and Upton’s analysis intertemporally, McConnell and Schallheim [lo] demonstrate that, for a given asset, the equilibrium rent *We appreciate the cooperation of a Greensboro, North Carolina, shopping center developer. Helpful comments were received from Jan Brueckner, Julian Diaz, III, Jim Shilling, Leslie Young, and two reviewers. Any oversights or omissions are, of course, our own responsibility. ‘See, for instance, Lewellen et al. [S], Miller and Upton [ll], Myers et al. [12], and McConnell and Schallheim [lo]. Brueckner [3] has recently provided a theoretical analysis of the problem of optimal space allocation in shopping centers. 299 0094-1190/92 $5.00 Copyright Q 1992 hy Academic Press. Inc. All rights of reproduction m any form rexwed.
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AND SIRMANS
depends on the provisions and contingencies contained in the lease contract. Schallheim et al. [14] argue that rent should also depend on the lessee’s financial condition, since this presumably reflects the probability of default, but find little evidence to support this prediction when tested against actual rents on a wide range of leased assets. They do, however, find evidence supporting some predictions of the Miller-Upton and McConnell-Schallheim models. In contrast, other researchers, for example Flath [5] and Smith and Wakeman [15], recognize that, at least for specialized assets, equilibrium rents can be the product of noncompetitive forces. In commercial real estate, for example, concurrent selling markets frequently do not exist and, moreover, releasing is often prohibited so that lessees are effectively segmented. Both of these features encourage discriminatory rent setting and are frequently encountered in the leasing of shopping center space. In this paper we examine whether shopping center rents for homogeneous tenant spaces and services meet the Phlips [13] criterion for price discrimination. Phlips (p. 6) has proposed that “. . . price discrimination should be defined as implying that two varieties of a commodity are sold . . . to two buyers at different net prices, the net price being the price (paid by the buyer) corrected for the cost associated with the product differentiation.” In the context of commercial real estate, this implies that shopping center rents reflect price discrimination to the extent that rent dispersion, over and above that attributable to differences in contractual provisions, can be explained by differences in tenant characteristics. Our premise, therefore, is that variations in shopping center rents are linked not only to the product differentiation induced by differing contractual provisions, but also to price discrimination induced by heterogeneity in tenants. Our empirical analysis indicates that tenant characteristics are indeed important in explaining rent dispersion. Somewhat unexpectedly, however, we cannot reject the possibility that variations in rent are unrelated to differences in contractual provisions, at least for those tenants favored by price discrimination. Of course, our finding that rent depends on tenant characteristics need not indicate price discrimination on the part of landlords if the economic cost of providing space also depends on tenant characteristics. Readers who have a strong prior belief that the cost of providing space is the same regardless of who it is being provided to will interpret our results as evidence in favor of price discrimination. Others will prefer to reserve judgement until evidence regarding the relationship between tenant type and landlord costs become available. In a previous paper (Benjamin, Boyle, and Sirmans [2]), we tested for the existence of a tradeoff between base rent and percentage (or overage) rent using a sample of shopping center lease contracts. Specifically, we regressed base rents on percentage rents, threshold sales levels (the
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critical sales level beyond which a tenant pays percentage rent), and a number of other factors which our model suggested might be important for determining base rents. We found that base rents are negatively related to percentage rent rates and positively related to threshold sales levels. As both of these relationships are statistically significant, this indicates that, all else being equal, lease contracts requiring high (low) base rents specify low (high) percentage rent liabilities on average. If price discrimination according to tenant “quality” exists in the setting of shopping center rents, then it is presumably reflected in total rental liabilities and not just in base rents. Consequently, our earlier analysis is incapable of shedding light on the importance of tenant characteristics (or contractual provisions) for rent setting since in a simple multiple regression of base rents on percentage rents and tenant characteristic and contractual provision variables, the coefficients on the latter sets of variables represent only the impact of those variables on base rent, holding percentage rent constant. Given our finding of a tradeoff between base and percentage rents, higher base rent need not imply a higher total rent liability so that these regression coefficients are relatively meaningless when it comes to assessing the relationship between total rent and tenant and contract variables. This paper extends our previous work in two main ways. First, we construct plausible, albeit ad hoc, indexes of total rent liability so that in our regression analysis these appear as the dependent variable instead of base rent. Second, in contrast to our earlier paper in which tenant characteristics and contractual provisions served only as variables which needed to be controlled for in analyzing the relationship between base and percentage rents, we examine in detail the relative contributions of these groups of variables in rent setting. In Section 2, we advance some hypotheses regarding tenant-based discrimination in rent setting and demonstrate their coherence within a simple theoretical model. In Section 3, we briefly describe the various contingencies and provisions commonly observed in shopping center lease contracts and their probable effect on rents. Section 4 describes the data and Section 5 outlines the empirical model. Section 6 presents our empirical analysis and Section 7 contains concluding remarks. 2. THEORY Shopping center leasing is characterized by several unique institutional practices. Rents are usually set along two dimensions: A base contract rent and a (so-called) overage rent. The latter requires the tenant to pay rent (in addition to the base rent) as a percentage of total sales revenue above some predetermined (at the time of lease contracting) level. Shopping center space and services are mostly homogeneous in that they are typically subdivisions of a larger structure and that the services for all
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AND SIRMANS
tenants are similar. Shopping centers are also characterized by a variety of tenant types including national and local chains, as well as “anchor” tenants that attract shoppers to the center. Given that shopping center space and services are homogeneous, we hypothesize that landlords use two tenant characteristics to determine the different rents they charge tenants: first, the probability of default and, second, the perceived ability of a tenant to generate customer traffic, i.e., the external or indirect influence of an individual tenant on total shopping center revenues.2 A high probability of default implies low expected rental receipts; the landlord therefore extracts compensation in the form of higher rent. Increased customer traffic raises overall shopping center sales and thus increases the landlord’s overage rental receipts. A high trafficgenerating potential, usually associated with tenants who have high anticipated sales, implies a strong indirect influence on landlord revenues. The landlord therefore compensates such tenants for this positive externality by reducing rent. To explain this latter mechanism more fully, note that the leasing of shopping center space to tenant i may help (hurt) existing tenants’ sales if tenant i’s business is complementary to (competitive with) that of existing tenants, For instance, if tenant i has high anticipated sales that attracts shoppers, then the presence of tenant i is beneficial to existing tenants. On the other hand, if tenant i is, for example, a standard shoe retailer, then his presence is generally detrimental to standard shoe retailers already in the center. Such linkages affect the landlord by altering the rent revenue received from existing tenants. To see that these conjectures are theoretically possible, consider the following simple model of ex ante landlord rent-setting behavior.3 There are IZ tenants; the subset (1,. . . , m} consists of new tenants while the subset (m + 1,. . . , n} denotes existing tenants. The landlord is faced with the beginning-of-period problem of deciding the optimal quantity of shopping center space to lease to each of the m new tenants.4 Each new tenant i = 1,. . . , m has an end-of-period sales revenue function Si
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19;. If the realized value of Bi is f3+, then new tenant i pays rent biQi to the landlord, where bi is tenant i’s rent per unit of space.5 If Bi = O,, then tenant i defaults and the landlord receives nothing.There is a fixed probability pi that Bi = f3-. For simplicity, the cost associated with providing space for the new tenants is assumed to be a linear function of that space. That is:
C
(1)
i=l
where o and k are positive constants. 6 In practice of course, landlord costs are likely to be a nonlinear function of the space rented; this possibility is subsequently incorporated in our empirical analysis. As well as rent from new tenants, landlords also receive rent from existing tenants. This is assumed to have two components: (i) a component with expected value v which depends only on the business activities of these existing tenants (i.e., is independent of the space leased to new tenants) and (ii) a component depending on the space leased to new tenants via the external effect of the latter on existing tenant sales. We assume that the second component is a linear function of the Qi’s, i=i m. Thus, if R is the landlord’s expected profit from existing tenahts,‘;hen the expected value of R can be written as:
E[R(LL . . ..Q.)]
= v +
EAiQi
(2)
i=l
where the constant Ai is the marginal indirect or external influence of tenant i’s presence on the profit the landlord expects to receive from existing tenants. The landlord is assumed to maximize expected profits:
mQa ,fY[{Cl-Pi)bi(Qi> 1
I
+ Ai - P}Q,] +v--0.
(3)
1
jIn practice, most leases call for rent to be paid in advance. In that case, b,Qi should be interpreted as tenant i’s advance rent for next period. For simplicity, we abstract from percentage rent for new tenants. 6We assume that p is sufficiently large to ensure that landlord profits are not an infinitely increasing function of any Qi.
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AND
Let us suppose that each new tenant demand function,
SIRMANS
i = 1,. . . , m has a log-linear
where pi is a positive constant. After substituting (4) into (3), the first-order conditions for (3) imply that the landlord supplies Qi units of shopping center space to each tenant i where Qi satisfies:
Hence, by (4), the rent charged to tenant i is: (CL
bi = (1 -pi)(l
-
*i)
-pi)
’
(6)
Equation (6) indicates that tenant i’s rent is positively related to pi, and negatively related to Ai, consistent with our hypotheses. 3. SHOPPING
CENTER
LEASE
PROVISIONS
The above discussion assumes shopping center leases to be generic insofar as the space utilized and the flow of services prescribed in each contract are the same for all tenants; the contracts differ only in that they specify different rental liabilities for different tenants. Once this assumption is relaxed, the role of contractual provisions and contingencies must be considered. This section briefly describes a number of these features and their probable relationship to rent. In addition to specifying the tenant’s rental liability, a lease contract stipulates the length of the lease and the amount of space leased. Rent should be inversely related to lease length for two reasons. First, a longer lease benefits the landlord by reducing his exposure to tenant search expenses, foregone rent, and reletting and renovation charges. Second, as Flath [S] has argued, the shorter the lease, the greater the wear and tear on the property. While a longer lease may also benefit the tenant, this is likely to be at least partially offset by the restrictions imposed on his freedom of action. With respect to the amount of space leased, the costs of negotiating and servicing lease contracts per dollar of rent probably decreases as the square footage increases, and the effect is probably nonlinear. Three common provisions found in shopping center leases are rent escalation, tenant renewal option, and landlord right to cancel. A rent
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escalation provision benefits the landlord by entitling him to mandated or inflation-linked increases in base rent over the life of the lease. Hence, all else being equal, the landlord should be prepared to accept a lower initial rent. A tenant renewal option benefits the tenant by giving the right to renew the lease at expiration if he deems it to be in his interest to do so. Hence, all else being equal, the tenant should be prepared to pay a higher rent. Finally, a landlord cancellation right benefits the landlord by giving him the right to cancel the lease prior to expiration if he deems it to be in his interest to do so. Hence, all else being equal, he should be prepared to accept a lower rent. 4. DATA To obtain data with which to test our hypotheses, we examined the files of a shopping center developer located in Greensboro, North Carolina. Greensboro is part of the Greensboro, High Point, and Winston-Salem, North Carolina, metropolitan statistical area and is commonly referred to as the Piedmont Triad. Greensboro and the Triad have experienced considerable growth in the past two decades and are characterized by a decentralized, suburban pattern of land use. The population approaches 1 million persons, giving it a top 50 ranking among metropolitan areas. We have no reason to suspect that shopping center conditions in Greensboro differ significantly from those in other U.S. markets. Data were obtained for 103 neighborhood and community shopping center leases written during the period January 1985 to December 1987. The five shopping centers from which these data are drawn display minimal structural and locational variation among the leased spaces, are all of strip design,’ and are all located within a 3-mile radius in the same geographic area of the city. They are comparable in architecture, occupancy (near lOO%), age of construction, and amenities. The individual tenant spaces are homogeneous in that they are all subdivided spaces of large cinder block and concrete floored structures. In addition, tenant services supplied such as trash removal and common area maintenance are similar. However, the shopping centers differ somewhat in size: The smallest center has a total area of 13,930 square feet while the largest covers 390,000 square feet. For each lease, the base rent per square foot (BRNT), the thousands of square feet leased (SQFT), the percentage-of-sales rental rate (PRNT), the per square foot sales level beyond which the tenant pays percentage rent (SBKP), and the lease length in months (TERM) were recorded. In addition, we were able to determine which contracts contained each of the ‘A strip shopping center is a group of stores connected by a sidewalk which runs along the front of the stores.
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TABLE 1 Descriptive Statistics Characterizing the Sample of 103 Shopping Center Leases Originated in Greensboro, NC, during the Period January 1985-December 1987 A. Frequency distribution for base rent per square foot (BRNT) Base rent per square foot $8 or less $8.01 to $10 $10.01 to $12 $12.01 to $14 $14.01 to $16 $16.01 to $18 Over $18 Maximum = $19.55, Minimum
Number of leases
Percent of total
7 20 33 16 19 6 2
6.8 19.4 32.1 15.5 18.4 5.9 1.9
= $6.39, Mean = $11.91, Median = $11.48
B. Frequency distribution of square feet leased (SQFT) Square feet leased (in ‘000s) Number of leases Percentage of total 1 or less 1.1 to 2 2.1 to 4 4.1 to 8 8.1 to 25 Over 25
3 41 39 12 7 1
Maximum = 85.61, Minimum
2.9 39.8 37.9 11.6 6.8 1.0
= 0.61, Mean = 4.11, Median = 2.257
C. Frequency distribution of percentage rent rates (PRNT) Percentage rent rates Number of leases Percentage of total 0 to 0.02 0.025 to 0.05 0.055 to 0.07 Over 0.07
12 30 53 8
11.7 29.1 51.4 7.8
Maximum = 0.100, Minimum = 0.000, Mean = 0.052, Median = 0.060 D. Frequency distribution of sales levels beyond which the tenant pays percentage rent (SBKP) Sales level ($000 per square foot) .lOO or less .lOl to .I75 ,176 to .250 over .251
Number of leases
Percentage of Total
14 31 50 8
13.6 30.1 48.5 7.8
Maximum = 0.383, Minimum = 0.0, Mean = 0.176, Median = 0.200
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CENTER
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TABLE l-Continued E. Frequency distribution by length of lease (TERM) Length of lease (months) Number of leases Percentage of total 12 or less 13 to 24 25 to 36 37 to 100 101 to 200 Over 200
4 10 29 47 11 2 Maximum = 240, Minimum
Lease characteristic
3.9 9.7 28.1 45.7 10.7 1.9
= 12, Mean = 38.1, Median = 60
F. Lease Characteristics Number of leases
Cancellation right Rent escalation right Renewal option
5 25 30
Percentage of total 4.86 24.27 29.13
lease provisions discussed in the previous section. Summary statistics are reported in Table 1. Panel A contains a frequency distribution of BRNT and shows that a majority of the tenants in our sample paid between $8 and $12 per square foot. Panel B provides a frequency distribution of SQFT. Almost 80% of tenants leased between 1000 and 4000 square feet. Only one tenant leased over 25,000 square feet. Frequency distributions of PRNT, SBKP, and TERM appear in Panels C, D, and E, respectively. Panel F provides information about other lease provisions. Less than 5% of the leases in our sample contained a landlord cancellation right, whereas 24% (29%) contained a rent escalation provision (tenant renewal option). The information in Panel F was recorded in a series of binary variables. For instance, the variable CNCL was assigned a value of one if the lease contained a landlord cancellation right and zero otherwise. The variables ESCL and RNWL denote analogous accountings for rent escalation rights and tenant renewal options respectively. 5. EMPIRICAL
MODEL
The model of Section 2 identified a set of tenant characteristics which landlords may use to price discriminate. However, Section 3 indicates that shopping center rent dispersion may also be due to heterogeneity in lease contract provisions. Our empirical model includes each of these to determine their importance for rent dispersion. A. Dependent Variable
Anticipated rent liabilities.
total rent liability depends on both the base and overage As noted earlier, shopping center lease contracts frequently
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require that tenants pay as overage rent a percentage of sales revenue over and above some pre-specified minimum sales level. The inclusion of overage rent in the form of a percentage rent provision increases the total rent liability of the tenant, all else being equal. Overage rent is clearly increasing in PRNT, but also is decreasing in SBKP because this decreases the amount of overage rent owed, all else being the same.8 The three components of the rent, BRNT, PRNT, and SBKP, are negotiated simultaneously at the time of contracting and together they determine the actual rent paid. Thus, the anticipated total rent liability TRNT, can be written as9 TRNT =
f(BRNT, PRNT, SBKP) BRNT
if PRNT > 0, if PRNT = 0,
fi > 0; f~ > 0; f3 < 0
where fi is the partial derivative of f with respect to its ith argument. To quantify TRNT in a form suitable for regression analysis, we used TRN-I-1
= BRNT(l + PRNT) l+SBKP
(7)
and TRNT2 = BRNT( 1 + PRNT)“”
+SBKP).
(8)
TRNTl differs from TRNT2 in that 1% changes in PRNT or SBKP have a greater impact on the former than on the latter. B. Independent Variables
To obtain proxies for the tenant characteristic variables, we used The Urban Land Institute’s “Dollars & Cents of Shopping Centers,” which contains information on shopping center leases. This document [16, p. 51 defines a national chain store to be “. . . a business operating in four metropolitan areas located in three or more separate states,” an independent store to be “ . . . a business operating in not more than two outlets located in only one metropolitan area,” and a local chain store to be “ . . . a business that does not fall into either of the other two categories.” Therefore, with regard to the probability of default (pi), company-owned national and local chain tenants seem likely to have lower default risk due ‘For some empirical evidence on this, see Benjamin, Boyle, and Sirmans [2]. ‘Lease contracts sometimes contain late payment charges and rent concession agreements (92 and 3, respectively, in our sample) but, in our sample, these are generally of insufficient magnitude to materially alter the effective rent liability.
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CENTER
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to their greater creditworthiness and operational experience.” Thirty-five of the tenants in our sample are members of such chains and these were assigned a value of one for the dummy variable CHN. The Urban Land Institute [16, pp. 128-2051 also provides a detailed breakdown of median sales volume per square foot of gross leased area according to tenant classification for community and neighborhood shopping centers. We recorded this information in the variable MSLS. As high sales tend to be associated with greater customer traffic, we decided to use MSLS as a proxy for the externality variable hi, no superior measure being available. As our sample covers a 3-year time span and includes five shopping centers, we created some additional sets of variables to control for intertemporal and intercenter variation in rental liability. To control for the former, a value of one was assigned to the dummy variable YEAR 2 (YEAR 3) if the lease originated in 1986 (1987) and zero otherwise.” We used two methods to control for intercenter variation in rental liability: Either (i) a value of one was assigned to the dummy variable CENTj (j = 1, . ..) 4) if the lease originated in center j or (ii> the lease was characterized according to the size (CENSIZE) of the center in which it originated. Finally, to allow for possible economies of scale in leasing space to large tenants, we included SQFI as an independent variable in our regressions and since the effect is probably nonlinear, we also included the square of the amount of square feet leased [SQFT2].‘* C. Model
The following estimated: InTRNT,
empirical
models of shopping center rental liabilities
= 70 + YiSQFTi
+ y,SQFT2,
+ f: GjYEARj, j=2
are
+ y3TERMi
+ ~ KjCENTji j=l
+ PlCNCLi
+ P,ESCL,
+ P,RNWLi
+ P,CHNi
+ PsMSLSi
+ Ei
(9.4)
“This might not be true of franchised outlets. However, our sample contained only company-owned operations. “Eighteen of the leases in our sample were signed in 1985, 41 in 1986, and 44 in 1987. “Being measures of quantity purchased by the tenant, both SQPT and TERM are likely to have some endogeneity problems, i.e., low rents may induce tenants to sign large and long leases. Since we are concerned with these variables insofar as they control for size and length-induced variations in rent, we chose to ignore this issue here.
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InTRNT,
BOYLE, AND SIRMANS
= ~0 + 7lSQFTi + i
+
GjYEARji
yzSQFT2,+
y,TERMi
+ kCENSIZE
j=2
+ P,CNCLi + P,CHNi
+ P,ESCLi + p,MSLSi
+ p,RNWLi + Ei*
(9B)
We used the natural log of TRNT as the dependent variable so that the regression coefficients can be interpreted as indicating the percentage effect on TRNT of a unit change in the corresponding independent variable.13 6. RESULTS We first estimated various forms of (9) for the total sample to test the effects of tenant and lease characteristics on rents. We found that, in general, tenant characteristics are significant predictors of cross-sectional variation in TRNT, but that lease provisions are less important. We then examined various subsamples of our data set in order to test for possible differences in rent setting according to tenant characteristics and lease provisions. These results indicate that there are some significant interaction effects in lease pricing. A. Total Sample Results
The results of our ordinary least-squares regressions using our entire sample of 103 leases appear in Table 2.14 Results are reported for the two measures of the dependent variable using the natural log of TRNTl (TRNT2). Models 1, 2, 5, and 6 use the dummy variables CENTj to capture rent variation across centers, whereas models 3, 4, 7, and 8 use center size CENSIZE (measured by total leasable area in square feet) to t31n recent years, the question of the correct functional form of hedonic models has arisen in the housing and rent property literature (see Butler [4], Halvorsen and Pollakowski [61,and Marks [9]). Butler reports that when scholars have compared alternative functional forms for hedonic indexes of housing, they find little basis for choosing one over another. Alternate linear and log forms model provided consistent estimates. In addition, Kennedy [7] has shown that empirical estimation using a semilogarithmic functional form with dummy independent variables induces a degree of bias in the estimated coefficients. The amount of bias depends on the magnitude of the estimated coefficient and its variance. In our sample, the adjustment suggested by Kennedy did not lead to any economically meaningful changes. t4Using variance inflation factors, condition indices, and eigenvalue procedures as outlined by Belsley, Kuh, and Welsch [I], no evidence of multicollinearity was discovered. Standard residual plots revealed no indication of heteroscedasticity. In addition, we checked for outliers/influential observations using the Belsley, Kuh, and Welsch statistic DFFITS. Regression results with outliers/influential observations deleted yielded comparable results.
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TABLE 2 Regressions of Total Rent on Contractual Provision and Tenant Characteristic Variables for a Sample of 103 Shopping Center Leases Originated over the Period January 1985-December 1987” Model’ Dependent Variableb CNCL ESCL RNWL CHN MSLS TERM SQFT SQFT2d CENSIZEd R2 F-value
1 -0.1159 (1.50) 0.0500 (1.12) 0.0895 (2.15) - 0.0845 (2.08) - 0.0014 (2.58) -0.0006 (1.01) - 0.0367 (5.08) 3.0730 (3.72) .60 11.77
variable 2
- 0.0840 (2.03) - 0.0014 (2.64) -0.ooo5 c.83) -0.0317 (4.59) 2.6236 (3.28)
.57 13.50
Dependent
= In TRNTl 3
4
- 0.0877 (1.08) 0.0407 c.87) 0.0967 (2.24) - 0.0736 (1.73) -0.0016 (2.81) - 0.0003 c.42) - 0.0397 (5.23) 3.4586 (4.01) 5.6448 (4.80)
- 0.0735 (1.71) - 0.0016 (2.90) -0.0002 C.31) - 0.0338 (4.74) 2.9097 (3.54) 4.6601 (4.16)
.55 12.13
.53 15.20
-
5 -0.1079 (1.39) 0.0231 c.52) 0.0857 (2.04) -0.1116 (2.73) - 0.0008 (1.47) - 0.0001 C.05) - 0.0280 (3.84) 2.2233 (2.67) .54 9.45
variable 6
-
-0.1131 (2.74) -0.0008 (1.49) - 0.0001 C.04) - 0.0241 (3.49) 1.9034 (2.38)
.52 11.01
= In TRNT2 ____I
8
- 0.0744 C.90) 0.0092 C.19) 0.1020 (2.32) - 0.0984 (2.27) - 0.0010 (1.79) 0.0003 c.52) - 0.0333 (3.93) 2.5255 (2.87) 5.3475 (4.46)
-0.1010 (2.30) - 0.0011 (1.83) 0.0003 t.44 - 0.0253 (3.49) 2.0717 (2.47) 4.2935 (3.77)
.46 9.05
.45 11.24
-
‘t statistics are in parentheses. ‘Independent variables are: dummy variables indicating whether lease contains a (i) landlord cancellation right (CNCL), (ii) rent escalation provision (ESCL), (iii) tenant renewal option (RNWL); a dummy variable if a tenant is a member of a national or local chain (CHN); median sales per square foot for merchant type (MSLS); length of lease (TERM); amount of square feet rented (SQFT); and the size of the center (CENSIZE). ‘Each model also included an intercept and a number of dummy variables designed to control for intertemporal (time dummies) variations in rent; models 1, 2, 5, 6 also included a number of dummy variables designed to control for intercenter (location dummies) variations in rent. The estimated coefficients for these are not reported in order to conserve space. Most were significant. dThe coefficient on SQPT2 should be shifted 10 places to the left; the coefficient on CENSIZE should be shifted 7 places to the left.
control for any intercenter variations. The degree of correspondence across models is quite strong. On average, tenants with leases containing a landlord cancellation right pay 8-10% less rent while those with leases containing a renewal option pay S-10% more rent. Thus, a cancellation right and the renewal option have about the same (absolute) effect on rents. The renewal option is
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AND SIRMANS
significant in all of the models, whereas the cancellation right is less important. l5 The escalation provision is even less significant in all the estimates. By contrast, the coefficients on both the tenant characteristic variables are significant at the 1% level when In TRNTl is the dependent variable. When In TRNT2 is the dependent variable, MSLS is not significant at the 10% level while CHN is significant at the 1% level. On average, chain tenants pay 9-12% less rent while an extra $100 of (anticipated) per-square-foot sales lowers rent by O.l-0.2%. It is of course possible that some of the chain discount is due to other factors associated with the name of the chain, but most of it seems likely to be associated with lower default probability, consistent with our hypothesis. Similarly, the negative coefficient on MSLS indicates that tenants with high anticipated sales do indeed receive a rental discount, albeit a relatively small one. Further insight into the rent-setting process can be gained by comparing the coefficient on both CHN and MSLS when TRNTl is the dependent variable with their respective counterparts when TRNT2 is the dependent variable. As can be seen in Table 2, the coefficient on CHN is greater in absolute value when TRNT2 is used. Recalling that percentage rent has lower weighting in TRNT2, this indicates that the discount for lower default probability is received primarily through lower base rent. By contrast, the coefficient on MSLS is greater in absolute value when TRNTl is used, thereby indicating that the positive externality discount is received primarily through a lower percentage rent rate. This is intuitive: tenants which attract customers to the center have high sales themselves so that the appropriate economic incentive involves reducing the liability they incur in the process of generating such high sales and customer traffic, i.e., percentage rent. The implication of the regressions in Table 2, that contractual provisions have relatively little effect on rental liability, is somewhat surprising. To further examine this issue, we excluded the contractual provision variables (CNCL, ESCL, and RNWL) from the model. The results appear in the models 2, 4, 6, and 8 in Table 2. The R*s are lower when the contract provisions are excluded (typically by 2-3%). The F statistic of 2.67 obtained by comparing this with the unrestricted equation is statistically significant at the 10% level. Thus, for aggregate data, we find weak evidence to support the hypothesis that rental liability is related to the presence and prevalence of contractual provisions. Consistent with our discussion in Section 2, the effect of TERM is negative, but insignificant. As expected, rents decline significantly as the
“However, with only five leases containing a cancellation right, any results concerning CNCL seem likely to be somewhat idiosyncratic.
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amount of space leased (SQFT) increases. The effect also decreases with increased space, since the SQFT2 coefficient is positive.16 Finally, models 3,4, 7, and 8 indicate that rental liability is an increasing function of center size, as all coefficients on CENSIZE are significantly positive. This finding is intuitively plausible. Bigger centers typically contain more retail outlets and hence generate greater consumer synergies. Thus, proportionately more shoppers are attracted to large centers, thereby encouraging tenants to offer higher rental payments. B. Some Further Analysis: Subsample Results
Results for the total sample indicate that rental liability is influenced by tenant characteristics and, to a lesser extent, by variations in lease provisions. Given the importance of the latter variables for earlier work on leasing, our finding that these are only marginally significant for shopping center rents is surprising. However, interaction effects between the independent variables may be exaggerating this (lack of) effect. That is, it is possible that contractual provisions are important in determining the rental liability for some subset of tenants but not for others, thereby causing the coefficients obtained from aggregate data to be less significant. To investigate this possibility, we examined various subsamples of our data set. First, we examined the possibility that the rent-setting process differs according to size of center. Since the size of the center may itself act as a draw, it could be that having “draw” tenants is potentially less important as center size increases. Also, having a low probability of default tenant may be more important for a small center, thereby implying a larger rent discount. To test this hypothesis, we separated our sample into the observations in “large” versus “small” centers based on total square footage. In the large center sample we included the two centers with total area of 208,000 and 390,320 square feet. The small center sample contains the remaining three centers: 13,930, 79,657, and 122,397 square feet. The sample sizes are 53 and 50, respectively. These results are shown in Panel A of Table 3.17 In general, for our sample, the lease provisions appear to be more important for the large centers. The most striking result is the effect of CHN. Tenant default probability is much more important in the small centers, low probability generating substantial rent discounts in those centers. The MSLS effect is “However, the coefficient on SQFT2 is sufficiently small to ensure that this non-linear effect never dominates in our sample; i.e., the SQFT value needed for TRNT to increase in SQFT greatly exceeds the maximum SQFI’ value in our sample so that TRNT is a decreasing function of SQFT for all leases in our sample. “To conserve space, we report only the results using In TRNTl as the dependent variable. The results using In TRNTZ are essentially the same.
314
BENJAMIN,
BOYLE,
AND SIRMANS
TABLE 3 Regressions of Total Rent Liability on Contractual Provision and Tenant Characteristic Variables’ Panel A: large versus small centers CNCL ESCL RNWL MSLS Large Centers Small Centers
- 0.2541 (2.26) 0.0856 (0.94)
0.0188 (0.29) 0.0339 (0.63)
0.1106 (1.25) 0.0667 (1.69)
-0.0012 (1.54) - 0.0010 (1.45)
CHN
R*
F
-0.0094 (0.16) - 0.1786 (3.02)
54
4.43
.86
16.27
R2
F
62
12.4
MSLS
R*
F
-0.0038 (3.53) -0.0002 (0.30)
0.69
5.4
0.62
7.6
Panel B: difficult versus easy contracts DIPP CHN MSLS - 0.039 C.86)
- 0.077 (1.82)
- 0.0015 (2.70)
Panel C: chain versus independent tenants CNCL ESCL RNWL Chain Tenants Independent Tenants
- 0.0380 (0.21) - 0.2306 (2.85)
0.0164 (0.16) 0.0600 (1.31)
0.0166 (0.14) 0.0708 (1.77)
Note: t statistics are in parentheses. “Dependent variable is InTRNTl. Each regression also included an intercept, the lease length (TERM), the number of square foot leased (SQPT), and a number of dummy variables designed to control for intertemporal and intercenter variations in rent. The estimated coefficients for these variables are not reported in order to conserve space. See Eq. (9) for a description of TRNTI. Independent variables are: dummy variable indicating whether lease contains a landlord cancellation right (CNCL), dummy variable indicating whether lease contains a rent escalation provision (ESCLI, dummy variable indicating whether lease contains a tenant renewal option (RNWL), dummy variable indicating whether tenant is a member of a national or local chain (CHN), and median sales per square foot for merchant type (MSLS). DIPP is a dummy variable equal to one if a lease has at least two contractual provisions and zero otherwise.
virtually the same for large and small centers, indicating that customer drawing power is valued equally across center size. Our limited sample might not allow us to measure the effect of each individual lease provision. To test the hypothesis that contractual provisions have a significant impact on rents only when there are a sufficient number of them, we separated our sample into “difficult” versus “easy” contracts. The former were defined as those containing at least two of the three provisions discussed previously (26% of the sample fell into this category).
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315
These results are shown in Panel B of Table 3. The DIFF variable is equal to one if a lease has at least two provisions and zero otherwise.” While the DIFF variable has the correct sign, the effect is not significant. All of the other variables have the expected impact. As a further test, we split our sample into those tenants favored and not favored by price discrimination. These results are shown in Panel C of Table 3. In the first row, we report the results from rerunning (9)-excluding CHN-for a subsample of 35 company-owned national and local chain tenants, i.e., those leases which were assigned a value of 1 in CHN. In the second row, we report the results obtained from the same regression for the independent tenants (68 leases) in our sample. The results are illuminating. The rental liabilities of those tenants favored by price discrimination still appear to be unrelated to the presence of contractual provisions in their leases, but CNCL and RNWL are now significantly related to the rental liabilities of independent tenants at the 10% level or better. On average, independent tenants with leases containing a cancellation right pay 23% less rent while those with leases containing a renewal option pay 7% more rent. By contrast, while CHN tenants still appear to receive a small customer traffic discount (MSLS is significant at the 1% level), no such discount appears to accrue to independent tenants. Thus, there appears to be a separation of the rent-setting process along the following lines: The rental liability of tenants with low probability of default and high customer traffic potential seems to be primarily determined by the simple existence of these phenomena (as well as other non-contract-specific variables such as shopping center location) and is independent of contractual provisions, while the rental liability of other tenants seems to partially depend on the provisions included in the lease contract. 7. CONCLUDING REMARKS Recent treatments of the leasing process have concentrated on perfectly competitive markets. If this assumption is relaxed, then shopping center rent dispersion for homogenous tenant spaces and services could be due to discriminatory pricing practices on the part of landlords as well as to differences in contractual provisions. Our empirical results indicate that some portion of the contract-specific cross-sectional variation in shopping center rents for shopping centers with generic tenant space and services is due to tenant heterogeneity. For tenants favored by price discrimination, little or none of this variation is due to differences in lease contract ‘*For these estimates, we changed the definition of the RENEW variable such that it was set equal to zero if the lease contained a tenant renewal option and one otherwise. Thus all three provision variables have the same (negative) expected effect.
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provisions. These findings contrast with the view, prevalent in the financial economics literature, which stresses the importance of lease conditions in determining asset rents and indicates that product differentiation effects may be dominated by price discrimination effects in imperfectly competitive markets. The clearest picture to emerge from our analysis is that of a two-stage rent-setting process for homogeneous tenant space and services. First, low-risk, high traffic-generating tenants receive rental discounts. Second, the rental liabilities of other tenants are dependent on the contractual provisions which landlords and tenants agree to insert in the lease. However, we must stress that these findings are far from conclusive given the shortcomings of our data set. For instance, with so few leases containing cancellation rights, it is difficult to come to any firm conclusions concerning the importance of this provision. Moreover, our variables for low risk and high traffic generation are somewhat less than ideal. The analysis of this paper can be extended in a number of ways. First, as the theoretical structure assumes risk-neutral landlords, the introduction of risk aversion seems a worthwhile extension, particularly if the conclusions can be stated in a form amenable to direct empirical testing. A more challenging problem involves the explicit incorporation of contractual provisions and contingencies into the theoretical rent-setting model. This would permit the form of the lease contract to be derived as a function of tenant, landlord, and shopping center characteristics, thereby providing a formalization of the Smith and Wakeman [15] analysis. Empirically, extension of our methodology to other geographical markets and different assets may prove fruitful. REFERENCES 1. D. A. Belsley, E. Kuh, and R. E. Welsch, “Regression Diagnostics,” Wiley, New York (1980). 2. J. D. Benjamin, G. W. Boyle, and C. F. Sirmans, Retail leasing: The determinants of shopping center rents, AREUEA Journal, 18, 302-312 (1990). 3. J. K. Brueckner, Inter-store externalities and space allocation in shopping centers, Journal of Real Estate Finance and Economics, to appear, 1992. 4. R. V. Butler, The specification of hedonic indexes for urban housing,” Land Economics, 58, 96-108
(1982).
5. D. Flath, The economics of short-term leasing, Economic Inquiry, 18, 247-259 (1980). 6. R. Halvorsen and H. 0. Pollakowski, Choice of functional form for hedonic price equations, Journal of Urban Economics, 10, 37-39 (1981). 7. P. E. Kennedy, Estimation with correctly interpreted dummy variables in semilogarithmic equations, American Economic Review, 71, 801 (1981). 8. W. G. Lewellen, M. S. Long, and J. J. McConnell, Asset leasing in competitive capital markets, Journal of Finance, 31, 787-798 (1976). 9. D. Marks, The effect of rent control on the price of rental housing: An hedonic approach, Land Economics, 60, 81-94 (1984).
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10. J. J. McConnell and J. S. Schallheim, Valuation of asset leasing contracts, Journal of Fimnciul Economics, 12, 237-61 (1983). 11. M. Miller and C. Upton, Leasing, buying, and the cost of capital services, Journal of Finance, 31, 761-786 (1976). 12. S. C. Myers, D. A. Dill, and A. J. Bautista, Valuation of financial lease contracts, Jouma~ of Finance, 31, 799-819 (1976). 13. L. Phlips, “The Economics of Price Discrimination,” Cambridge Univ. Press, Cambridge (1983). 14. J. S. Schallheim, R. E. Johnson, R. C. Lease, and J. J. McConnell, The determinants of yields on financial leasing contracts, Journal of Financial Economics, 19, 45-67 (1987). 15. C. W. Smith, Jr., and L. MacDonald Wakeman, Determinants of corporate leasing policy, Journal of Finance, 40, 895-908 (1985). 16. Urban Land Institute, “The Dollars and Cents of Shopping Centers: 1987,” The Urban Land Institute, Washington, DC (1987).