Efficiency measurement of Swiss shopping centers using ...library.eres.org/eres2015/presentationupload/53.pdf · Non-monetary : customer satisfaction / loyalty, service quality, etc.

European Real Estate Society (ERES) 22nd Annual Conference

Istanbul, 27th June 2015

Dr. Alexandra Bay

Senior Researcher, Wincasa AG

Efficiency measurement of

Swiss shopping centers using

Data Envelopment Analysis (DEA)

Master thesis – MAS UZH in Real Estate (2014)

2

Shopping center

ICSC / ULI Definition

� ‘A group of retail and other commercial establishments that is planned, developed,

owned and managed as a single property. On-site parking is provided. The center’s size

and orientation are generally determined by the market characteristics of the trade

area served by the center. The two main configurations of shopping centers are malls

and open-air strip centers’.

� ‘[…] defines a European shopping center as a retail property that is planned, built and

managed as a single entity, comprising units and “communal” areas, with a minimum

Gross Leasable Area (GLA) of 5’000 square metres (m2)’.

3

Shopping center

Classification

� Format: traditional, innovative, specialised ...

� Location: city center, urban quarter, suburban, greenfield

� Catchment area: neighbourhood center, community center, regional center,

super-regional center

� Accessibility: walking distance, car, public transport, parking lots …

� Size / sales area: GLA, NLA, ICSC classes (very large, large, medium, small) …

� Tenant: mono-category vs. multi-categories, fashion center, convenience center …

� Anchor: super market, fashion, media / electronic; one anchor vs. several anchors …

� Specialisation: retail park, urban entertainment center, factory-outlet center,

theme-oriented center, lifestyle center …

4

Productivity of shopping centers

Performance drivers

� Production function: a shopping center “produces” with several inputs several

different outputs

� Inputs

� Endogenous: sales area, parking lots, number of tenants, anchor, number of

employees, wages, marketing-mix, opening hours / days, etc.

� Exogenous: purchasing power, population, location, accessibility, competition,

demography, socio-economic characteristics, etc.

� Outputs

� Monetary: sales, profit, financial key figures, etc.

� Non-monetary: customer satisfaction / loyalty, service quality, etc.

� Productivity: performance measure � Ratio = ������

�����

5

Productivity of shopping centers

Performance measures

� Productivity: performance measure � Ratio = ������

�����

� Employee productivity � Ratio = ���

�� ���� ����

� Sales productivity � Ratio = ���

������

� Output: sales

� Input: sales area

� Examples with ONE input factor and ONE output factor

� Performance ≈ Efficiency ≈ Productivity

6

Efficiency measurement – sales productivity

Efficient frontier (DEA) vs. regression line

SC 1

SC 2

SC 3

SC 4

SC 5

SC 6

SC 7

SC 8

0

1

2

3

4

5

6

7

8

9

0 1 2 3 4 5 6 7 8 9 10

Sa

les

Sales area

Shopping Center Efficient fron er Regression line

7

Data Envelopment Analysis (DEA)

Definition

� ‘Data Envelopment Analysis (DEA) is a “data-oriented” approach for evaluating the

performance of a set of peer entities called Decision-Making Units (DMUs), which convert

multiple inputs into multiple outputs. The definition of a DMU is generic and flexible’.

(Cooper, Seiford, and Zhu (2011))

� ‘The name Data Envelopment Analysis, as used in DEA, comes from this property because

in mathematical parlance, such a frontier is said to “envelop” these points’.

(Cooper, Seiford, and Tone (2006))

� ‘DEA was designed to measure the relative efficiency where market prices are not available

[…]. Such previous DEA studies provide useful managerial information on improving the

performance. In particular, DEA is an excellent tool for improving the productivity of service

businesses […]’. (Zhu (2009))

DEA vs. regression analysis

8

DEA method – efficient frontier

� touches at least one point

� example: efficient point SC 2

� points, i.e. shopping centers, are

either on or below the efficient

frontier

� refers to the best DMU(s) / shopping

center(s) (best in class approach)

� Benchmark: point / shopping center

� example: SC 2

Regression analysis

� goes through the middle of all the

points / shopping centers

� there are shopping centers deviating

from the mean / average – upwards

or downwards

� refers to the average DMU / shopping

center; tendency to the mean

� Benchmark: average SC – built from

SC 2 and SC 6

� Benchmark: e.g. SC 3 or SC 8

9

DEA: theoretical framework / terminology

Charnes-Cooper-Rhodes-Model (CCR-Model)

� Output-to-input-ratio with several inputs and several outputs: ∑ ���������

∑ ���������

� DMU: n homogenous Decision Making Units (DMUs) with DMUj , j = 1, ... , n

� DMUs ≈ Shopping centers

� Inputs: m input factors; xij > 0 the amount / number of the input factor i, i = 1, ... , m used

by DMUj ; vi the weight of input factor i � weighted sum of inputs:

� Outputs: s output factors; yrj > 0 the amount / number of the output factor r, r = 1, ... , s

produced by DMUj ; ur the weight of output factor r � weighted sum of outputs:

10

CCR-Model

Optimisation problem – in words

� Output-to-input-ratio: ∑ ���������

∑ ���������

� Maximise the efficiency – the output-to-input-ratio – for the DMUo under the

constraint that:

� for all the DMUs: 0 ≤ output-to-input-ratio ≤ 1 (normalisation)

� all the weights vi and ur ≥ 0

� sequential processing of all the DMUs (o = 1, ... , n)

11

CCR-Model

Optimisation problem – fractional form

To be solved for each DMUo (o = 1, ... , n)

Output-oriented CCR-Model in the envelopment form

12

13

BCC- and Additive Models as extensions of the CCR-Model

� Charnes-Cooper-Rhodes-Model (CCR-Model, 1978)

� constant returns-to-scale assumption (CRS)

� input orientation OR output orientation

� Banker-Charnes-Cooper-Model (BCC-Model, 1984)

� variable returns-to-scale assumption (VRS)

� input orientation OR output orientation

� convexity constraint

� Additive Model (Charnes et al. (1985))

� convexity constraint

� input orientation AND output orientation

simultaneously

SC 1

SC 2

SC 3

SC 4

SC 5

SC 6

SC 7

SC 8

0

1

2

3

4

5

6

7

8

9

0 1 2 3 4 5 6 7 8 9

Sa

les

Sales area

Shopping Center CCR: Efficient fron er

SC 1

SC 2

SC 3

SC 4

SC 5

SC 6

SC 7

SC 8

0

1

2

3

4

5

6

7

8

9

0 1 2 3 4 5 6 7 8 9

Sa

les

Sales area

Shopping Center BCC: Efficient fron er

SC 1

SC 2

SC 3

SC 4

SC 5

SC 6

SC 7

SC 8

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

6

0 1 2 3 4 5 6 7 8 9

Sa

les

Sales area

Shopping Center ADD: Efficient fron er

Slack s-: Input-Reduc on of 2

Slack s+: Output-

Increase of 1

Point (3, 3)

DEA method

Pros – Cons

14

Pros

� DEA simultaneously handles

multiple input factors and

multiple output factors in a

single aggregated efficiency measure

without prior fixing of the factor weights

� no assumptions regarding

� probability distribution

(� non-parametric)

� input-output-function

� best practice approach

� Operations Research based linear

programming approach

Cons

� extreme value method

� dependency on the selection of DMUs

� relative – not absolute – efficiency

� numerically challenging

15

DEA method

Summary

� Which DMUs are efficient and why?

� What are the sources of inefficiency?

� Which efficient DMU / combination of efficient DMUs should an inefficient DMU

compare to?

� What are the benchmarks / reference DMUs in a peer group comparison?

� Which factors do bear some potential for efficiency enhancement

(input reduction and / or output increase)?

16

Applications

� DMUs: Swiss (Wincasa) shopping centers (21)

� Set of factors (Wincasa data 2013)

� Inputs: sales area in 1 000 m2, number of parking lots, gross rent in Mio. CHF,

Occupancy Cost Ratio (OCR) in %, population (residents / employee) in 1 000

� Outputs: sales in Mio. CHF, sales productivity in 1 000 CHF / m2,

ratio 1/OCR = sales/gross rent

� DEA-Models: output-oriented CCR-Model (CCR-O), output-oriented BCC-Model

(BCC-O), Additive Model (ADD) – in the envelopment form

� Three cases: DEA versus sales productivity, ratios as factors, focus: gastronomy /

restaurants

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Results – Case 1

DEA vs. Sales

productivity

Output Output Output Output Output

1. Sales in Mio. CHF 1. Sales in Mio. CHF 1. Sales in Mio. CHF 1. Sales in Mio. CHF 1. Sales in Mio. CHF

Input Input Input Input Input

1. Sales area in 1'000 m2 1. Sales area in 1'000 m2 1. Sales area in 1'000 m2 1. Sales area in 1'000 m2 1. Sales area in 1'000 m2

2. Parking lots 2. Population in 1'000 2. Gross rent in Mio. CHF 2. OCR

CCR-O BCC-O CCR-O BCC-O CCR-O BCC-O CCR-O BCC-O CCR-O BCC-O Ratio

Shopping

Center (SC)

φ CCR φ BCC φ CCR φ BCC φ CCR φ BCC φ CCR φ BCC φ CCR φ BCC Sales

Productivity

SC 1 1.01 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 7.4

SC 2 1.27 1.00 1.26 1.00 1.18 1.00 1.27 1.00 1.00 1.00 5.9

SC 3 4.56 3.08 4.56 3.05 4.56 3.08 3.86 3.07 4.56 3.08 1.6

SC 4 1.43 1.39 1.43 1.39 1.12 1.11 1.32 1.24 1.42 1.39 5.2

SC 5 1.56 1.56 1.56 1.56 1.56 1.56 1.48 1.46 1.55 1.54 4.8

SC 6 1.09 1.00 1.09 1.00 1.00 1.00 1.09 1.00 1.00 1.00 6.9

SC 7 1.14 1.00 1.14 1.00 1.06 1.00 1.00 1.00 1.14 1.00 6.6

SC 8 2.37 1.59 2.37 1.56 2.35 1.59 2.36 1.59 2.37 1.59 3.2

SC 9 1.35 1.18 1.35 1.18 1.31 1.18 1.26 1.18 1.35 1.18 5.6

SC 10 3.58 1.00 3.58 1.00 3.40 1.00 2.87 1.00 3.58 1.00 2.1

SC 11 2.94 1.10 2.94 1.00 2.78 1.10 2.28 1.00 2.94 1.00 2.5

SC 12 2.40 2.39 2.40 2.39 2.22 2.18 1.72 1.67 2.39 2.39 3.1

SC 13 2.22 2.06 2.21 1.73 1.11 1.00 1.53 1.36 2.22 2.06 3.4

SC 14 1.24 1.02 1.24 1.02 1.24 1.02 1.23 1.02 1.24 1.02 6.0

SC 15 2.71 2.61 2.71 2.61 2.70 2.54 2.29 2.26 2.71 2.61 2.8

SC 16 2.29 2.28 2.28 2.28 2.27 2.25 2.27 2.26 2.29 2.28 3.3

SC 17 2.82 2.81 2.81 2.81 2.80 2.73 2.11 2.05 2.82 2.81 2.7

SC 18 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 7.5

SC 19 1.37 1.27 1.37 1.27 1.26 1.26 1.37 1.27 1.36 1.27 5.5

SC 20 1.36 1.27 1.36 1.27 1.27 1.27 1.36 1.27 1.35 1.27 5.5

SC 21 2.06 1.89 2.04 1.00 2.06 1.89 2.05 1.89 2.06 1.89 3.6

DEA vs. Sales productivity (SP)

Analysis SP 1 Analysis SP 2 Analysis SP 3 Analysis SP 4 Analysis SP 5

18

Results – Case 2

Ratios as factors

� Shopping center manager: with focus on sales productivity (high), OCR (sustainable)

� Inputs: sales area, number of parking lots

� Outputs: sales productivity, 1/OCR

� Results:

� SC 18 is only BCC-efficient; SC 7 is CCR-efficient

� BCC-O with four factors adequately captures the performance drivers

� alternative to the simple performance measure “sales productivity”

� Advantage: more information in one performance measure

19

Results – Case 3

Focus: gastronomy / restaurants

� Success of a shopping center: tenant mix, third-place qualities � gastronomy

� Inputs: retail sales area, gastronomy sales area (variation: parking lots, population)

� Outputs: retail sales, gastronomy sales (variation: gastronomy 1/OCR, total 1/OCR)

� Results:

� shopping center sample too small

� results are not reliable

� Advantage: new approach of capturing the efficiency of the shopping center’s tenant mix

20

Practical implications

Outlook

� Potential as a performance measurement, benchmarking, or rating tool; building a MIS

� Larger universe of input and output factors

� Larger shopping center universe

� Window analysis: efficiency changes over time

� Sensitivity analysis

� Stochastic DEA model: stochastic input and / or output factors

� Reverse optimization: optimal tenant mix

21

Dr. Alexandra Bay

� Studies in Economics, Econometrics and Operations Research at the University of

Zurich

� PhD at University of Zurich „Multiperiod ALM-Models with CVaR-Minimisation for

Swiss Pension Funds” (2008)

� Master of Advanced Studies (MAS) UZH in Real Estate at the Department of Banking

and Finance – Center of Urban and Real Estate Management (CUREM); Thesis

„Efficiency measurement of Swiss shopping centers using Data Envelopment Analysis

(DEA)“ (2014)

� Since April 2015: Senior Researcher at Wincasa AG

2009 – 2015: Senior Investment Consultant at ECOFIN Investment Consulting AG

Before: Strategist at Swisscanto Asset Management AG, Research Assistant

at the Department of Operations Research of the University of Zurich and

actuary at Swiss Life