Efficiency measurement of Swiss shopping centers using Data Envelopment Analysis (DEA)

Transcription

1 Efficiency measurement of Swiss shopping centers using Data Envelopment Analysis (DEA) Master thesis MAS UZH in Real Estate (2014) European Real Estate Society (ERES) 22 nd Annual Conference Istanbul, 27 th June 2015 Dr. Alexandra Bay Senior Researcher, Wincasa AG

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 square metres (m 2 ). 2

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 3

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= 4

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 5

6 Efficiency measurement sales productivity Efficient frontier (DEA) vs. regression line Sales 5 4 SC 5 SC 8 3 SC 2 SC 4 SC 7 2 SC 3 SC 6 1 SC Sales area Shopping Center Efficient fron er Regression line 6

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)) 7

8 DEA vs. regression analysis 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 bestdmu(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 averagedmu / shopping center; tendency to the mean Benchmark: average SC built from SC 2 and SC 6 Benchmark: e.g. SC 3 or SC 8 8

9 DEA: theoretical framework / terminology Charnes-Cooper-Rhodes-Model (CCR-Model) Output-to-input-ratio with severalinputs and severaloutputs: DMU: nhomogenous Decision Making Units (DMUs) with DMU j, j = 1,..., n DMUs Shopping centers Inputs: minput factors; x ij > 0 the amount / number of the input factor i, i= 1,..., m used by DMU j ; v i the weight of input factor iweighted sum of inputs: Outputs: soutput factors; y rj > 0 the amount / number of the output factor r, r= 1,..., s produced by DMU j ; u r the weight of output factor r weighted sum of outputs: 9

10 CCR-Model Optimisation problem in words Output-to-input-ratio: Maximise the efficiency the output-to-input-ratio for the DMU o under the constraint that: for all the DMUs: 0 output-to-input-ratio 1 (normalisation) all the weights v i and u r 0 sequential processing of all the DMUs (o= 1,..., n) 10

11 CCR-Model Optimisation problem fractional form To be solved for each DMU o (o= 1,..., n) 11

12 Output-oriented CCR-Model in the envelopment form 12

13 BCC- and Additive Models as extensions of the CCR-Model 9 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 Sales Sales Sales SC 2 SC 1 SC 3 SC 4 SC 5 SC 6 SC 7 SC Sales area Shopping Center CCR: Efficient fron er SC 2 SC 1 SC 3 SC 4 SC 5 SC 6 SC 7 SC Sales area Shopping Center BCC: Efficient fron er SC 2 Point (3, 3) Slack s + : Output- Increase of 1 SC 1 SC 3 SC 4 SC 5 SC 6 SC 7 Slack s - : Input-Reduc on of 2 SC Sales area Shopping Center ADD: Efficient fron er 13

14 DEA method Pros Cons 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 14

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)? 15

16 Applications DMUs: Swiss (Wincasa) shopping centers (21) Set of factors (Wincasa data 2013) Inputs: sales area in m 2, number of parking lots, gross rent in Mio. CHF, Occupancy Cost Ratio (OCR) in %, population (residents / employee) in Outputs: sales in Mio. CHF, sales productivity in CHF / m 2, 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 16

17 Results Case 1 DEA vs. Sales productivity Shopping Center (SC) DEA vs. Sales productivity (SP) Analysis SP 1 Analysis SP 2 Analysis SP 3 Analysis SP 4 Analysis SP 5 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 m 2 1. Sales area in 1'000 m 2 1. Sales area in 1'000 m 2 1. Sales area in 1'000 m 2 1. Sales area in 1'000 m 2 2. Parking lots 2. Population in 1' 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 φ CCR φ BCC φ CCR φ BCC φ CCR φ BCC φ CCR φ BCC φ CCR φ BCC Sales Productivity SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC

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 18

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 19

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 20

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 : 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 21