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Multi-Criteria Saturation Index (MCSI)
A comparative static analysis was conducted for 7 randomly selected countries, including Australia, China, France, Malaysia, South Africa, Turkey and the United States of America (International Council of Shopping Centres, 2014 – 2017).
Development of the proposed multi-criteria saturation index entailed the following steps:
1. Compile a synthesised table, indicating the shopping centre floor space per capita for each country or region;
2. Tabulate the purchase power parity (PPP) gross domestic product (GDP) per capita for each country or region;
3. Calculate the GDP/capita to GLA/capita ratio for a selected year;
4. Index calculation;
5. Rank results.
The results of each step are illustrated below. Table 8.1 and Figure 8.2 provide a summary of the shopping centre floor space per capita for the selection of countries. The retail floor space for seven countries are shown from 2009 to 2014 (2015 ICSC data is not available for all countries).
Comparatively lower to negative levels of change can be observed in the developed countries of the United States (-0.19%/a), France (1.13%/a) and Australia (2.71%/a). Comparatively higher rates of annual change can be observed in developing markets such as Turkey (9.79%/a), Malaysia (6.19%/a) and South Africa (4.54%/a).
Demand Density Analysis
The RDI and MCSI offer insight into market structure and saturations levels but neither technique offers insight into the spatial distribution of demand. Trend surface mapping ads a spatial dimension to market data. The demand density analysis is proposed as such an approach.
Census household income data per sub-place is the basis for the demand density analysis. A sub-place is the geographic unit for which Statistics South Africa releases census data. Demand density analysis proposed in this paper entails the following steps:
1. Compile a database with census-based income per sub-place for a selected market area;
2. Adjust for growth;
3. Calculate retail spend (in SA Rands) per sub-place;
4. Convert retail spend to demand (turnover density)
5. Map results.
Growth Matrix
A hypothetical developer with a keen interest in rural shopping centre development identified two prospective development sites in the greater Bushbuckridge area. One site is situated in the Bushbuckridge CBD, the southernmost node in the area, which accommodates commercial and administrative functions, as well as a regional hospital. The second site is situated in Acornhoek, the northern node in the area, which similarly includes a regional hospital, a comparatively more limited commercial offering and intermodal transport facilities. Trade area based calculations indicated a similar quantity of shopping centre development potential for both areas: approximately 30 000m2. In both areas, modest levels of growth and development can be visually observed and both schemes appeal to retailers. The questions posed were:
1. do the growth prospects differ between the two areas and, if so, to what extent; and
2. how would the respective area growth prospects influence the future income generating potential of a shopping centre asset?
Essentially, which area should ideally be selected for the development? The Growth Matrix was developed to provide insight to the impact of trade area growth attributes on asset income growth generating potential.
Market growth is not simplistically determined by household growth only, but also by income growth (and by implication household expenditure and disposable income growth), as well as minimum required trading density growth (which should account for the applicable inflation environment, i.e. 5-6% annual consumer price inflation in South Africa. Real growth of 3% is required to yield sustainable rental escalations of 8%-9% (an absolute minimum of 7% has been observed in presently market conditions). Inflation adjusted consumer spend and trading density growth was calculated for both areas (Figure 8.7 and 8.8).
On face value, both markets are growing. Similarly, in both instances a widening gap between consumer expenditure on retail goods and services, and trading densities can be observed – which is ideal, but is it sufficient to sustain the desired rental escalations? In Bushbuckridge, long term household growth calculates to 0.65% per annum, final consumption expenditure growth averages 0.4% per annum over the long term and disposable income growth averages 1.0% per annum. In Acornhoek, long term household growth averaged 1.0% per annum, final consumption expenditure growth averaged 1.2% per annum over the long term and disposable income growth averaged 0.8% per annum.
CHAPTER 1: INTRODUCTION
1.1. BACKGROUND
1.2. THE PROBLEM AND ITS SETTING
1.3. DOCUMENT OUTLINE
CHAPTER 2: RESEARCH METHODOLOGY
2.1. INTRODUCTION
2.2. THEORETICAL FRAMEWORK
2.3. TYPE OF DESIGN AND THE ASSUMPTIONS THAT UNDERLIE THE DESIGN
2.4. DATA SAMPLING AND DATA COLLECTION
2.5. DATA ANALYSIS STRATEGIES
2.6. METHODS OF ACHIEVING VALIDITY
2.7. INTERPRETATION OF RESEARCH FINDINGS
2.8. CONCLUSIONS
CHAPTER 3: ECONOMIC FRAMEWORK
3.1. INTRODUCTION
3.2. ECONOMICS AND HUMAN BEHAVIOUR
3.3. DEFINING REAL ESTATE AND RELATED CONCEPTS
3.4. FACTORS AFFECTING SPACE AND CAPITAL MARKETS – AND EQUILIBRIUM
3.5. RELATIONSHIP BETWEEN REAL ESTATE AND ASSET MARKETS
3.6. CONCLUSIONS
CHAPTER 4: A CRITICAL APPRAISAL OF CENTRAL PLACE THEORY
4.1. INTRODUCTION
4.2. LITERATURE REVIEW
4.3. THEORIES TESTED
4.4. CONCLUSIONS
CHAPTER 5: PERCEPTIONS ABOUT THE IMPACT OF DUAL AND MULTIPLE FOOD GROCER ANCHORAGE ON THE PERFORMANCE OF SHOPPING CENTRES IN SOUTH AFRICA
5.1. INTRODUCTION
5.2. LITERATURE REVIEW
5.3. A PERSPECTIVE ON CONSUMER BEHAVIOUR
5.4. RESEARCH METHODOLOGY
5.5. RESULTS: PERSPECTIVE OF SHOPPING CENTRE OWNERS AND INVESTORS
5.6. RESULTS: PERSPECTIVE OF FOOD GROCER RETAILERS
5.7. RESULTS: STATED CONSUMER PREFERENCES
5.8. CONCLUSIONS
CHAPTER 6: THE IMPACT OF DUAL AND MULTIPLE FOOD GROCER ANCHORAGE ON THE PERFORMANCE OF SHOPPING CENTRES IN SOUTH AFRICA
6.1. INTRODUCTION
6.2. RESEARCH METHODOLOGY
6.3. DEFINITIONS AND CONCEPTS
6.4. QUANTITATIVE ANALYSIS
6.5. CONCLUSIONS
CHAPTER 7: TECHNIQUES FOR THE ANALYSIS OF TRADE AREAS: RETAIL DIVERSIFICATION
7.1. INTRODUCTION
7.2. LITERATURE REVIEW
7.3. RESEARCH METHODOLOGY
7.4. DATA ANALYSIS
7.5. INTERPRETATION OF FINDINGS
7.6. RETAIL DIVERSIFICATION INDEX APPLIED TO INTER-CITY COMPARISON
7.7. CONCLUSIONS
CHAPTER 8: FURTHER TECHNIQUES FOR THE ANALYSIS OF TRADE AREAS: SATURATION, DEMAND DENSITY AND GROWTH
8.1. INTRODUCTION
8.2. LITERATURE REVIEW
8.3. RESEARCH METHODOLOGY
8.4. DATA ANALYSIS
8.5. CONCLUSIONS
CHAPTER 9: PROPOSED IMPROVED METHODOLOGY
9.1. INTRODUCTION
9.2. THE HYPOTHESIS AND SPECIFICATION OF FUNCTIONAL RELATIONSHIPS
9.3. AN INTEGRATED, CONCEPTUAL FRAMEWORK FOR RETAIL MARKET DEMAND ANALYSIS
9.4. A TEST FOR VALIDITY
9.5. CONCLUSIONS
CHAPTER 10: SUMMARY AND DIRECTION FOR FURTHER RESEARCH
10.1. INTRODUCTION
10.2. SUMMARY OF MAIN FINDINGS
10.3. DIRECTION FOR FURTHER RESEARCH
REFERENCES
