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The Journal of Real Estate Finance and Economics
Published in: The Journal of Real Estate Finance and Economics 1/2014

01-01-2014

Expansions and Contractions of Major US Shopping Centers

Authors: John M. Clapp, Katsiaryna Salavei Bardos, Tingyu Zhou

Published in: The Journal of Real Estate Finance and Economics | Issue 1/2014

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Abstract

We analyze the determinants of expansions and contractions of shopping centers using a unique dataset of property level data for shopping centers in eleven metropolitan areas over the period from 1995 through 2005. We find that shopping centers with large operating costs are less likely to expand and are more likely to contract. Higher expected revenue per square foot increases the likelihood of expansion and decreases the likelihood of contraction. For small shopping centers the decision to change gross leasable area (GLA) is largely driven by potential revenue, while the decision to change the number of stores is largely a function of cost. We find some support for Grenadier’s theory that a larger number of competitors reduces the value of option to wait and increases the likelihood of both expansion and contraction. The market share of competitors reduces the likelihood of increasing the number of stores as suggested by the theory of strategic positioning. Our hypotheses best explain contraction decisions of large shopping centers and expansion decisions of small shopping centers. We find that both expansions and contractions of GLA are less likely for large shopping centers in MSAs with greater uncertainty about real estate prices, indicating that the option to delay has value. Moreover, small centers are significantly less sensitive to cost and revenue; since small centers are likely to have greater idiosyncratic risk than large; this provides indirect evidence for a significant delay option.
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Appendix
Available only for authorised users
Footnotes
1
The 40 % estimate is based on Bureau of Labor Statistics numbers analyzed by Fanning (2005).
 
2
Capozza and Li (2001) analyze annual data on single family building permits in 64 MSAs; Miles (2009) examines housing starts; Fu and Jennen (2009), Sivitanidou and Sivitanides (2000) and Schwartz and Torous (2007) study new commercial office construction.
 
3
Property level data is also analyzed by Bulan et al. (2009). They study condominium or strata buildings in Vancouver, Canada. Some studies focus on retail sales. Lee and Pace (2005) study the partial distribution of retail sales in Houston. Brueckner (1993) and Mejia and Eppli (2003) examine the relationship between retail sales and inter-center externalities.
 
4
Based on the changes of mall type, 14 out of 318 large (regional/super regional) centers demalled into small (power/community) centers. When we define the renovation as decrease in the number of stores by 10 % or more, 82 % of the time GLA decreased by 5 % or less, suggesting conversion to “big box” retail centers.
 
5
Morris Newman (1999), writing for The Los Angeles Times says “As shoppers find other ways to buy merchandise, several traditional shopping centers–including that famous playground for Valley girls, the former Sherman Oaks Galleria–are being converted to such new hybrids as entertainment centers, “big box” retail centers, office buildings, schools and even housing.”
 
6
According to Dollars and Cents of Shopping Centers, the operating costs for larger centers are greater both in terms of absolute value and as a percentage of rental income. For example, in 2000, super regional malls have average operating costs around USD 9.4 per sq ft, which represents 29 % of total operating receipts. Regional malls have average operating costs around USD 4.9 per sq ft, which represents 36 % of rental income. For smaller types of malls, community and neighborhood centers have average operating costs around USD 2.8 per sq ft, about 29 % and 27 % of total rent, respectively. All our regressions include a type_large dummy and many interact this with other variables in order to control costs. Our definition of large centers is essentially the same as combining regional and super regional centers. (Note that we did not find the classification in the DMM to be reliable – e.g., it changed over time in capricious ways – so we based our large classification on GLA.)
 
7
The term “irreversible” as used here simply means that any expansion or contraction is costly. Change in the number of stores is associated with relatively small costs of repositioning of walls. However, it requires costly renegotiation of leases. Change in gross leasable area (GLA) involves high construction costs. Both types of redevelopment involve disruption of existing retailing, adding to irreversibility.
 
8
Dixit and Pindyck (1994, pp 139–40) point out that real options theory is relevant in a certainty world because the flexibility to delay a project has value.
 
9
International Council of Shopping Centers (ICSC) clearly defines the range of GLA and number of stores, and trade areas for different types of shopping centers. The ICSC definition is different from Carter (2009). Some papers, such as Gatzlaff et al. (1994) and Carter and Vandell (2005) only focus on certain types of shopping centers.
 
10
The data are noisy, suggesting that results are conservative estimates of the underlying parameters.
 
11
Cho and Shilling (2007) examines the real option applications on shopping center leases. Peng and Thibodeau (2011) examine the association between interest rate changes and capital expenditures for retail properties and find it to be insignificant.
 
12
I.e., the firm can go in and out of business depending on P, but sunk costs imply that the firm may not enter the market even when it is profitable for existing firms, and may not exit even when price is below variable costs.
 
13
A change in supply is one of the distinguishing features between real options and financial options. In this regard, real options are like stock warrants.
 
14
The literature contains numerous empirical studies of the call option for housing: i.e., the option to tear down and rebuild a larger or more luxurious structure, or to substantially renovate. The tear down option is the subject of Rosenthal and Helsley (1994); Dye and McMillen (2007); Rosenthal (2008); Clapp and Salavei (2010); Clapp et al. (2011). Vacant land (zoned commercial and residential) has been studied by Quigg (1993); theory derives from the seminal work of Titman (1985). Commercial property call option exercise has been studied by Childs, Riddiough and Triantis (1996) and by Schwartz and Torous (2007). Empirical studies of put option have focused on mine openings and closings (Brennan and Schwartz 1985; Moel and Tufano 2002). The salient point here is the relatively high operating costs that can be saved by shutting down. Most housing and office properties would bear substantial operating costs (property taxes, insurance, security) even if shuttered, so an owner with an over-improved property has little choice but to wait for depreciation to reduce the value of the investment. Glaeser and Gyourko (2005) study asymmetrical investment decisions in housing.
 
15
Shopping center types (e.g., community, regional or superregional) will be discussed below.
 
16
The internal configuration of the shopping center (i.e., mix of stores and placement of stores within the structure) has received some attention in the literature. See, for example,: Schulz and Stahl (1996), Carter (2009) and Benjamin et al. (1990 and 1992).
 
17
Enclosed is considered as one of the features of the shopping center design in Sirmans and Guidry (1993).
 
18
Both are measured at the beginning of the period.
 
19
We adjust trade area to depend on the size of the shopping center. WATS is the weighted average market share times household income within the trade area, per square foot of GLA. See the Appendix for detailed calculations. Benjamin et al. (1992), Pashigan and Gould (1998) and Carter (2009) show a positive relation between sales per sq ft and rent per sq ft. See also Mejia and Benjamin (2002).
 
20
Bulan et al. (2009) use the number of competing residential projects as the measure of competition in their real option framework.
 
21
Offsetting effects occur because the hypothesis predicts that the cut point for expansion and contraction are shifted in the same direction without predicting which shift is greater.
 
22
Note that the ρl cost term can drive the abandonment point far below variable costs.
 
23
Smith and Hay (2005) have an interesting application to the agglomeration economies of independent owners (“streets” of independent retailers), shopping centers and “supermarkets,” defined as a single store that offers many different product lines (e.g., butcher, baker, pharmacy and bank) within the store.
 
24
Of course, we control for center type (e.g., regional or community).
 
25
According to International Council of Shopping Centers (ICSC), the national quarterly occupancy rate for all types of shopping centers between 1995 and 2005 ranges from 92 % to 96 %.
 
26
We could not find any other dataset of US shopping centers that provides property level data for the entire US over a 10 year period. The data we were able to obtain from industry sources and by searching Lexis-Nexis was incomplete. Shopping mall data in CoStar is given at parcel rather than property level and does not provide reliable mall characteristics. Moreover, it is available only for the last five years for most shopping centers.
 
27
Although we include Boston as one of our MSAs, only 7 observations are located in downtown Boston. We also run separate regressions deleting the downtown malls and the results are similar.
 
28
The 11 MSAs include (1) Boston-Cambridge-Quincy, MA-NH Metropolitan Statistical Area; (2) Charlotte-Gastonia-Concord, NC-SC Metropolitan Statistical Area; (3) Cleveland-Elyria-Mentor, OH Metropolitan Statistical Area; (4) Denver-Aurora, CO Metropolitan Statistical Area; (5) Las Vegas-Paradise, NV Metropolitan Statistical Area; (6) Minneapolis-St. Paul-Bloomington, MN-WI Metropolitan Statistical Area; (7) San Diego-Carlsbad-San Marcos, CA Metropolitan Statistical Area; (8) San Jose-Sunnyvale-Santa Clara, CA Metropolitan Statistical Area; (9) Seattle-Tacoma-Bellevue, WA Metropolitan Statistical Area; (10) Tampa-St. Petersburg-Clearwater, FL Metropolitan Statistical Area; and (11) Portland-Vancouver-Beaverton, OR-WA Metropolitan Statistical Area.
 
29
We use the 2002 Directory to verify and revise variables from the 2000 Directory.
 
30
We do not consider the change of ownership as a failed case. The directory assigns an identification number to each shopping center. The identification number does not change as the owner or name changes.
 
31
We apply 6 miles for community centers, 5 miles for power centers, 15 miles for regional shopping centers and 25 miles for super regional shopping centers. Note that community centers are power centers are classified as small shopping centers and regional and superregional malls are classified as large shopping centers.
 
32
Our results are similar when we use 5-year spans of 1995–2000 and 2000–2005, a 10-year span of 1995–2005 and a pooled sample of 5-year spans of 1995–2000 and 2000–2005.
 
33
All changes are calculated by comparing the first and the last observation of the time span; level variables are for the first year so that prior shopping center characteristics can be allowed to predict subsequent renovation. For example, GLA and number of stores in Table 2 Panel A are from DMM 1995 for 1995–2000 time span observations and are from DMM 2000 for 2000–2005 observations.
 
34
We believe that the year-open variable reported in DDM is not reliable. Some malls report the year of last major renovation as the year-open. In results not reported, we included year opened, year last renovated and combinations of the two in all of our models and find insignificant coefficients on these variables.
 
35
MSA-level variables (available on request) show that our observations are fairly equally distributed across eleven MSAs. Comparatively, the distributions of renovations show more variations. For example, some MSAs, such as Minneapolis and Boston have a greater percentage of expansions and contractions. Case-Shiller growth rate (growth5) and standard deviation (std5) variables are used to test the basic prediction of the uncertainty version of Dixit’s model. They are available only at MSA level. Because we pool two five-year sample periods, we choose the annualized Case-Shiller growth rate and standard deviation in the middle year of the first and last observations. We use the alternatives of 1-year, 3-year and 5-year average within the middle year. As a result, each MSA has 2 observations for annualized growth and standard deviation.
 
36
We present both the absolute value and the log value of WATS. While we use log value of WATS in regressions, the results are similar when we use absolute value.
 
37
Benjamin et al. (1992) also conclude that the larger the centers, the higher the rents.
 
38
Power centers became quite common in recent years. A power center usually refers to a shopping center with 200,000 to 800,000 square feet of gross leasable areas that contains three or more big-box retailers or department stores and a number of smaller retailers. Movie center conversion refers to the renovation in which shopping center owner replaces movie center with more retailers.
 
39
Store size is measured by GLA per store. This is an exchange option, so revenue loss from any change is part of the cost of exercise. Excessive GLA per store lowers revenue lost from contraction.
 
40
Sirmans and Guidry (1993) find that enclosed shopping centers have higher rents because they provide more variety and have higher ability to attract customers.
 
41
Recall that offsetting effects occur because both expansion and contraction are accelerated by number of competitors, and theory does not say which effect is greater.
 
42
Prior literature also used hazard models to examine the determinants of time between renovations (Bulan et al. (2009)). Our data is not well suited for such tests because we observe a shopping center only at three points during a ten year interval. Moreover, DMM does not provide reliable data on the year built and year since last renovation, variables essential to hazard analysis.
 
43
We chose 10 % change in GLA and number of stores because of the natural break in the data, suggesting that 10 % represents major renovation. Specifically, the 10th percentile for GLA change is −9 % and the 90th percentile is 9 %. The distribution for Store change is less symmetric: 10 % percentile represents −19 % change and 90th percentile is 8 %.
 
44
See marginal effects for large shopping centers in Table 3.
 
45
Note that the p-value is .26, suggesting that an increased sample size will produce a significant positive sign.
 
46
Section “Robustness Tests” discusses the evidence supporting the presence of the option to wait.
 
47
Overall, we expect asymmetric results for expansions and contractions because it is much less costly to adjust with vacancy on the downside.
 
48
We also run additional regressions by using subsamples of (1) large and enclosed malls, (2) small and enclosed malls, (3) large and open malls and (4) small and open malls. We find some support for Grenadier theory. For example, coefficient of compet_ttl is positive and significant for store expansion in multinomial logit model for large and enclosed malls, store contraction and expansion in multinomial logit model for small and open malls, and simple logit model for small and open malls.
 
49
WATS is closely related to a gravity potential variable explained and tested by Eppli and Shilling (1996). The most important difference is that they allow more flexibility in the effect of GLA on sales. However, their table 3 shows little sensitivity to this extra flexibility, and a model similar to ours explains 62 % of the variation in shopping center sales. Moreover, when they tested a gravity potential variable against alternatives (e.g., national chain tenancy) they found that gravity potential has much more explanatory power.
 
50
Lee and Pace (2005) show the importance of customer distance from the shopping center in determining sales.
 
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Metadata
Title
Expansions and Contractions of Major US Shopping Centers
Authors
John M. Clapp
Katsiaryna Salavei Bardos
Tingyu Zhou
Publication date
01-01-2014
Publisher
Springer US
Published in
The Journal of Real Estate Finance and Economics / Issue 1/2014
Print ISSN: 0895-5638
Electronic ISSN: 1573-045X
DOI
https://doi.org/10.1007/s11146-012-9382-z

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