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Mall Boom or Bust Kami Colden Brad Teter Devin Wayne.

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Presentation on theme: "Mall Boom or Bust Kami Colden Brad Teter Devin Wayne."— Presentation transcript:

1

2 Mall Boom or Bust

3 Kami Colden

4 Brad Teter

5 Devin Wayne

6 Jane Zielieke

7 Shan Huang

8 Our Presentation What is a mall What is a mall Discrete logistic growth model Discrete logistic growth model Assumptions we made Assumptions we made Our model Our model Our findings Our findings Our conclusion Our conclusion

9 What is a Mall? A collection of independent retail stores, services, and a parking area conceived, constructed, and maintained by a management firm as a unit. A collection of independent retail stores, services, and a parking area conceived, constructed, and maintained by a management firm as a unit. Shopping malls may also contain restaurants, banks, theatres, professional offices, service stations, and other establishments. Shopping malls may also contain restaurants, banks, theatres, professional offices, service stations, and other establishments.

10 Thunderbird Located in Menomonie, WI

11 London Square Located in Eau Claire, WI Located in Eau Claire, WI Younker’s Younker’s

12 Oakwood Located in Eau Claire, WI 8 million visits per year 130 stores Key Attractions: Department Stores Women's Apparel Housewares & Home Books & Entertainment Movie Theater Food Court and Restaurant

13 Mall of America Located in Bloomington, MN Located in Bloomington, MN Currently the largest fully enclosed retail and entertainment complex in the United States. Currently the largest fully enclosed retail and entertainment complex in the United States. More than 520 stores More than 520 stores 600,000 to 900,000 weekly visits depending on season 600,000 to 900,000 weekly visits depending on season Nearly $1.5 billion annually income Nearly $1.5 billion annually income

14 Discrete Logistic Growth Model

15 Population Model X(n) = population of the mall at year n X(n) = population of the mall at year n r = the intrinsic growth rate of the stores r = the intrinsic growth rate of the stores The difference between the current and previous year is represented by the equation: The difference between the current and previous year is represented by the equation: X(n + 1) – X(n) = rX(n)

16 Population Model (cont.) The population for the next year would be represented by the equation: The population for the next year would be represented by the equation: X(n+1) = RX(n) where R = r + 1 Our model assumes that the growth rate is dependant on the population. So, growth rate would be represented by r(x). Our model assumes that the growth rate is dependant on the population. So, growth rate would be represented by r(x).

17 Carrying Capacity The carrying capacity of the store population would be the maximum number of stores possible given current space restrictions. The carrying capacity is represented by a constant K. The carrying capacity of the store population would be the maximum number of stores possible given current space restrictions. The carrying capacity is represented by a constant K.

18 Ockham’s Razor If there are several possible explanations for some observation, and no significant evidence to judge the validity of those hypotheses, you should always use the simplest explanation possible. If there are several possible explanations for some observation, and no significant evidence to judge the validity of those hypotheses, you should always use the simplest explanation possible. Also known as the principle of parsimony – scientists should make no more assumptions or assume no more causes than are absolutely necessary to explain their observations. Also known as the principle of parsimony – scientists should make no more assumptions or assume no more causes than are absolutely necessary to explain their observations.

19 By Ockham’s Razor Growth rate would be linear (of the form r(x) = mx + b) Growth rate would be linear (of the form r(x) = mx + b) r(0) =  (an intrinsic growth rate without regard to restrictions like space) r(0) =  (an intrinsic growth rate without regard to restrictions like space)

20 By Ockham’s Razor (cont.) r(K)= 0 (no growth) r(K)= 0 (no growth) r(x) = -(  /K)x +  r(x) = -(  /K)x +  r(X(n)) = -(  /K)x +  r(X(n)) = -(  /K)x +   (0,  ) (K, 0)

21 Basic Logistic Population Model X(n+1) – X(n) = [-(  /K)x +  ]X(n) X(n+1) – X(n) = [-(  /K)x +  ]X(n) X(n+1) = [-(  /K)x +  ]X(n) + X(n) X(n+1) = [-(  /K)x +  ]X(n) + X(n) X(n+1) = X(n)[1+  (1-X(n)/K)] X(n+1) = X(n)[1+  (1-X(n)/K)]

22 Steady State A steady state is a point where an system “likes” to remain once reached. A steady state is a point where an system “likes” to remain once reached. The fundamental equation X(n+1) = f(X(n)) is a 1 st order recurrence equation. The fundamental equation X(n+1) = f(X(n)) is a 1 st order recurrence equation. To find the steady states of our model solve the following equation for X: To find the steady states of our model solve the following equation for X: X[1+  (1-X(n)/K)] = X X = 0, X= K

23 Steady State (cont.) Essentially, once the mall reaches capacity it has will most likely remain full. Essentially, once the mall reaches capacity it has will most likely remain full. Conversely, once a mall becomes vacant it is highly unlikely that any stores will be attracted to the location. Conversely, once a mall becomes vacant it is highly unlikely that any stores will be attracted to the location.

24 Stability Stability is the tendency to approach a steady state. Stability is the tendency to approach a steady state. To determine stability, find the derivative of f(x) = X[1+  (1-X(n)/K)] To determine stability, find the derivative of f(x) = X[1+  (1-X(n)/K)] Which is: f’(x) = 1 +  - (2  /K)X Which is: f’(x) = 1 +  - (2  /K)X Stable if |f’(x)| < 1 Stable if |f’(x)| < 1

25 Stability (cont.) Findings: Findings: If the intrinsic growth rate is out of range, we find chaotic behavior in the model. If the intrinsic growth rate is out of range, we find chaotic behavior in the model. f’(0)f’(K) f’(0) = 1 +  f’(K) = 1 +  - 2  |1 +  | < 1 = |1 – f| < 1 (0 is an unstable -1 < 1 -  < 1 fixed point) 0 <  < 2

26 Assumptions

27 Assumptions We Made The mall is a fixed size and location The mall is a fixed size and location In our model we will be considering customers, stores, and mall management. In our model we will be considering customers, stores, and mall management.

28 Assumptions (cont.) Mall management rationally and intentionally controls what they charge for rent in an effort to get a maximum profit for the mall. Mall management rationally and intentionally controls what they charge for rent in an effort to get a maximum profit for the mall. Stores pass rent off to the customer within the prices of the products they sell. Stores pass rent off to the customer within the prices of the products they sell.

29 Assumptions (cont.) Symbiosis Symbiosis Population of customers and stores are positively associated. Population of customers and stores are positively associated. If one increases or decreases the other follows until they reach capacity. If one increases or decreases the other follows until they reach capacity. Finite Carrying Capacity Finite Carrying Capacity There is a maximum number of customers and stores a mall can have. There is a maximum number of customers and stores a mall can have.

30 Laws of economics Supply is positively associated with the price. Supply is positively associated with the price. Demand is negatively associated with the price. Demand is negatively associated with the price. Demand Curve Supply Curve Equilibrium Point Price (dollars) Quantity

31 Opportunistic Rent Year n-1 Year n-1 stores make a profit stores make a profit Year n Year n mall management increases the rent to maximize their profit mall management increases the rent to maximize their profit stores pass off the increase of rent to the customers by increasing prices stores pass off the increase of rent to the customers by increasing prices

32 Opportunistic Rent (cont.) Year n+1 Year n+1 A noticeable loss in customers will be observed and store will lose profit A noticeable loss in customers will be observed and store will lose profit Year n+2 Year n+2 stores will leave if not making a profit stores will leave if not making a profit mall management will have to decrease the rent to keep stores or get new stores to move in mall management will have to decrease the rent to keep stores or get new stores to move in This cycle will continue until mall management and the stores both reach an agreeable opportunistic rent. This cycle will continue until mall management and the stores both reach an agreeable opportunistic rent.

33 Misc. Factors Not Considered Niche effectiveness (different types of stores) Niche effectiveness (different types of stores) Price elasticity (insensitivity to price change) Price elasticity (insensitivity to price change) Economies of scale (more variety) Economies of scale (more variety) Population of surrounding area Population of surrounding area Attractiveness of the mall Attractiveness of the mall

34 Our Model

35 Formulating the Mall Model Let X(n) be the population of mall customers at year n Let X(n) be the population of mall customers at year n Let Y(n) be the number of stores in the mall at year n Let Y(n) be the number of stores in the mall at year n Let K be the mall carrying capacity of stores Let K be the mall carrying capacity of stores

36 The Customers Population of customers is proportional to the number of stores in the mall: Population of customers is proportional to the number of stores in the mall: X(n + 1) = A * Y(n) where A is a multiple of the stores that are open Then A * K will be the customer carrying capacity of the mall Then A * K will be the customer carrying capacity of the mall

37 The Stores The store model based on the discrete logistic growth model is The store model based on the discrete logistic growth model is Y(n + 1) = Y(n)[1 +  (1 – Y(n) / K)] Where  is the intrinsic growth rate (the rate at which the stores fill the mall) Where  is the intrinsic growth rate (the rate at which the stores fill the mall)

38 Minimum Operating Costs Electricity Electricity Insurance Insurance Snow removal Snow removal Etc. Etc.

39 The Greed Factor (Opportunistic Rent) Incorporating the greed factor into the customer model Incorporating the greed factor into the customer model X(n + 1) = A*Y(n) - R(X(n), Y(n)) Where R(X, Y) represents the customers attrition due to the greed factor Where R(X, Y) represents the customers attrition due to the greed factor Let R(X(n), Y(n)) =  (n)X(n) +  (n)Y(n) Let R(X(n), Y(n)) =  (n)X(n) +  (n)Y(n) For some positive sequences of {  (n)},  (n)} For some positive sequences of {  (n)},  (n)}

40 Building the Mall Model The Customers X(n + 1) = A * Y(n) -  (n)X(n) -  (n)Y(n) Where -  (n)X(n) -  (n)Y(n) is customer attrition from last years price increase The Stores Y(n + 2) = Y(n +1)[1 +  (1 - Y(n) / K)] - B(  (n)X(n) +  (n)Y(n) Where the B is a constant multiplied by the customer attrition in year n

41 Behold the Mall Model Customers: X(n + 1) = A * Y(n) -  (n)X(n) -  (n)Y(n) Stores: Y(n + 1) = y(n) )[1 +  (1 - Y(n) / K)] - B(  (n - 1)X(n - 1) -  (n - 1)Y(n - 1))

42 Mall Management & Money A large greed factor will produce millions right away = no profits in years to come A large greed factor will produce millions right away = no profits in years to come Why? Why? Stores have moved or gone out of business, since increase in rent was passed onto customers, whom have gone elsewhere to find lower prices Stores have moved or gone out of business, since increase in rent was passed onto customers, whom have gone elsewhere to find lower prices

43 Mall Viability The key to mall viability is a function of the mall managements long term profits The key to mall viability is a function of the mall managements long term profits Σ 24 n=0 (  (n)X(n) +  (n)Y(n)) Want  has high as possible without driving stores out and new stores from moving in due to high rent Want  has high as possible without driving stores out and new stores from moving in due to high rent Want to find sequences of {  (n)}, {  (n)} which will maximize this sum Want to find sequences of {  (n)}, {  (n)} which will maximize this sum

44 Our Model at Work

45 Many thanks to Manager at Ben Franklin Manager at Ben Franklin Marketing personal at Oakwood Mall Marketing personal at Oakwood Mall And of course, Mr. Deckelman And of course, Mr. Deckelman


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