Brazilian Shopping Malls

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1 Americas/Brazil Equity Research Real Estate Management & Development Research Analysts Guilherme Rocha Daniel Gasparete Vanessa Quiroga, CFA Brazilian Shopping Malls THEME An Investor Guide to the Shopping Mall Industry's Growth Path It is almost common sense among the listed shopping mall operators that there is a strong positive sentiment towards the industry s growth outlook for the next decade. Companies have been pointing to double-digit growth rates for the next couple of years, countless greenfield projects, and several potential mall expansions. However, the question that arises among investors is how much of that is feasible? In this report we delve into the growth potential of the mall industry through a detailed screening of cities and states in Brazil. The conclusion? There are still a lot of opportunities out there, but Brazil is not as underpenetrated as many may have thought or misleading stats may have indicated. We believe that 2-3 new malls per year per company for our covered sample are feasible. In our view, the shopping mall sector should continue to be favored by high single-digit real growth rates in retail and rising penetration of mall sales. A 5% real growth in retail over the next ten years (in line with CS economic team forecast) followed by a rising penetration of retail sales in malls (from ~19% currently to ~22%) would make room for new malls per year (equivalent to ~800,000 sqm of GLA per year). But opportunities are not everywhere. While Brazil s low mall penetration of 53 sqm of GLA/ 000 per capita may be misleading since the population is highly dispersed (~35% is concentrated in cities with less than 50,000 inhabitants), we found that cities with more than 500,000 people already have penetration of 130 sqm of GLA/ 000 per capita. Despite the existence of a few large cities with a penetration rate as high as 237 sqm, we identified less penetrated cities with a rate as low as 30 sqm. Mid-sized cities (350,000 to 500,000 people) seem to still have low penetration, with only 71 sqm of GLA/ 000 per capita. Short-term growth should continue to come from large cities. We applied the same criteria used by mall operators for picking a city to open a new mall and ranked the top-20 cities in Brazil. The best opportunities were mostly found in southeastern and largely populated cities (close to 40% of the sample). This should support companies preference for cities with more than 500,000 inhabitants before exploring less populated areas. BR Malls has more growth opportunities. Comparing players expansion strategies, we find that BR Malls has the broadest range of growth opportunities on the back of its wider focus (cities above 350,000 people). Iguatemi lags behind its peers owing to its stronger focus on rich and largely populated areas in the south and southeast of Brazil (we identified cities using this parameter). We continue to prefer BR Malls, Aliansce, and Sonae Sierra. Using our city scorecard, we ranked Sonae Sierra first among its peers favored by its footprint in less penetrated markets with high GDP per capita and a large share of household budget spent on malls and apparel. In terms of greenfield pipeline, we see Aliansce in first place, benefited by projects in Belem (PA) and Maceio (AL). DISCLOSURE APPENDIX CONTAINS ANALYST CERTIFICATIONS AND THE STATUS OF NON-US ANALYSTS. FOR OTHER IMPORTANT DISCLOSURES, visit researchdisclosures or call +1 (877) U.S. Disclosure: Credit Suisse does and seeks to do business with companies covered in its research reports. As a result, investors should be aware that the Firm may have a conflict of interest that could affect the objectivity of this report. Investors should consider this report as only a single factor in making their investment decision.

2 Table of Contents Executive Summary 3 Let s Grow. But to where? Decision-Making Process Choosing Where to Strike 4 7 1) Population Size and Growth 8 2) Mall-Related and Apparel Spending 9 3) Spending Elasticity through Income Levels 10 The Cities Scorecard 12 Growth Potential for our Sample 13 Final Thoughts Opportunities Still out There 15 The smaller city dilemma: is there any significant risk there? 15 Appendix 1: Ranking our Covered Sample Portfolio and Pipeline 17 Greenfield Pipeline Ranking the Growth 18 Appendix 2: Ranking of Cities with More than 350,000 People 19 Brazilian Shopping Malls 2

3 Executive Summary Growth opportunities coming from increasing retail sales. In our opinion, the Brazilian shopping mall sector should continue to be boosted by the high single-digit real growth rate of the retail sector, coupled with an increasing penetration of mall sales in the consolidated figures. According to our calculations, theoretically there is room for new malls per year through the next decade (considering a 5% real growth in retail sales in the long run and a rising penetration of retail sales on malls, from the current ~19% to ~22% in the next ten years). Our covered sample should continue to be favored by this trend, as we arrive at 2-3 new malls per year for each company (assuming their market share increases to 25% from the current 19%). Not as underpenetrated as previously indicated. A comparison between the Brazilian mall penetration of 53 sqm GLA/ 000 per capita with that of other countries may give a wrong idea of the current situation, since Brazil s population is strongly dispersed across the country, with ~35% concentrated in cities with less than 50,000 people. In turn, mall penetration in large cities (above 500,000 people) is close to 130 sqm of GLA/ 000 per capita, similar to that of some developed countries (except US and Canada). Although a few large cities still offer considerable opportunities, we see mid-sized cities (350,000 to 500,000 people) as the next expansion frontier for the mall sector (penetration of only 70 sqm of GLA/ 000 per capita) and rising income per family. How to pick the right city to invest in? Based on our conversation with experts on mall expansion strategies, we described the process of choosing a location for a greenfield project step by step. This process includes a screening test which scores cities by: (a) population size; (b) population growth; (c) potential mall-related expenses; (d) wealth generation; (e) expenditure elasticity through income levels; and (f) GLA offer and street retail strength. Following this rationale and using our proprietary database developed in partnership with CS Retail & Consumption Team (Gustavo Wigman, Claudio Lensing, Antonio Gonzalez and Allan Lopez), we ranked the top-20 cities to invest in. It is interesting to note that opportunities are well distributed throughout Brazil (9 in the southeast and 11 in the north and northeast; besides 9 in cities with less than 500,000 people). Growing in the most populated areas. According to companies management, growth should continue to come from (1) greenfields in cities with more than half a million inhabitants - despite the existence of a few areas that are already well-served (Vila Velha - ES, Ribeirão Preto - SP, Londrina - PR and Jundiai - SP, for instance); (2) expansion of already operating malls (high returns with low risks); and (3) new ventures in mid-sized cities (350,000 to 500,000 inhabitants). When we compared the strategies of the companies in our coverage universe, we found that BR Malls stands out for having the broadest range of opportunities (given its focus on cities with 350,000 people or above), followed by Aliansce and Sonae Sierra. Iguatemi comes in last place, impacted by its strategy of focusing on rich and largely populated areas in the south and southeast of Brazil and a high share of class A households (we identified cities within this parameter). Small is also beautiful. In our view, the focus on larger cities seems to be much more a core strategy than necessarily the only option for expansion plans. Investing in smaller cities (from 350,000 to 500,000 people) does not necessarily imply lower returns or more complex operations. According to our calculations, greenfields returns are more sensitive to changes in construction costs (Capex/sqm) than to changes in the performance of sales per sqm, which helps to minimize project risks. Ranking the portfolio: Sonae Sierra in first place. We ranked the companies portfolios applying the same scorecard we used to rank the top-20 cities to invest in. As a result, Sonae Sierra stands out among peers, benefited by its footprint presence in less penetrated markets with high GDP per capita and a large share of household budget spent on malls and apparel. Iguatemi came in in second place, followed by BR Malls, Aliansce and Multiplan, practically tied in the third place. As for their growth portfolio, Aliansce came in first benefited by projects in both Belem (PA) and Maceio (AL). Multiplan was in last place due to its presence in Rio de Janeiro (RJ), which accounts for ~55% of its expansion pipeline. Brazilian Shopping Malls 3

4 Let s Grow. But to where? The near-term growth path of the mall industry is already known. According to data published by the Brazilian Malls Association (Abrasce), shopping malls should continue posting strong growth rates, opening 42 malls in 2012 and 33 in 2013 (a ~20% growth from current levels). As was the case in the past couple of years, most of this expansion trend should come from listed players, which should therefore increase their market share (from the current 19% to ~23% in 2013). Even so, competition is getting tougher as non-listed players and investment funds join the game to secure a stake in such fast-growing, defensive, and inflation-protected business. The question now lies in the following: What about growth after 2013? Is there more to come? A few listed companies have been pointing to 2-3 new mall openings per year. Is it feasible? We believe it is. Exhibit 1: GLA Growth in millions of sqm Sample 7.5 Others Source: ABRASCE, Company data, Credit Suisse estimates. Sample: BRML3, MULT3, IGTA3, ALSC3, SSBR3. In our opinion, the sector should continue to be boosted by an attractive combination of high single-digit growth rate in retail sales coupled with an increasing share of malls in retail sales. Therefore, according to our calculations shown in Exhibit 2 and Exhibit 3, theoretically, there might be room for mall openings per year during the next decade (assuming an average size of ~25,000 sqm, in line with Brazil s average). We reach this number by assuming that retail sales will continue to grow above GDP (1.9x the historical ratio). In the exercise we assumed a 5% expansion rate in the long run (in line with the projection of CS economics team). Coupled with that, we assume that mall sales should continue to gain weight in retail figures, increasing its penetration from the current 18.5% to close to 22% within ten years. Brazilian Shopping Malls 4

5 Exhibit 2: Retail Sales Growth vs. Mall Penetration In % 12% 10% Retail Sales Growth y/y (LHS) Mall Penetration 21.5% 23% 21% 8% 19% 6% 17% 4% % Source: IBGE, Company data, Credit Suisse estimates This positive trend seems to be even more favorable for our coverage universe where new malls per year (or 2-3 new malls per company annually) may be potentially opened. We reach this range by assuming that listed players will continue increasing their market share (to up to 25% in this exercise from 19%). Exhibit 3: Potential for Mall Openings In # of malls Sample Sector Covered Sample Share (RHS) % 25.0% 24.0% 23.0% 22.0% % 20.0% 19.0% - Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 18.0% Source: ABRASCE, Company data, Credit Suisse estimates. Sample: BRML3, MULT3, IGTA3, ALSC3. SSBR3 If investors were to talk to any of the listed companies, they would probably think there is no concern on where to open these new malls. Many companies would say that Brazil continues to be significantly underpenetrated and there is potential almost everywhere. But is Brazil actually that underpenetrated as many may think? A quick look into Brazil s demographics would reveal that comparing total GLA offer with the whole population may be misleading. The main reason is Brazil s geographic dispersion. As shown in Exhibit 4, one-third of the Brazilian population lives in cities with less than 50,000 inhabitants with poor infrastructure and mobility, therefore not served by the mall industry. At the same time, only 11 million people live in cities with a population between 350,000 and 500,000 people, and only 30% of the population is concentrated in cities with more than 500,000 inhabitants (the focus of most of our sample). Just to give a Brazilian Shopping Malls 5

6 better idea of the Brazilian geographic dispersion, we compare Brazil s demographic density of 23 inhabitants per square kilometer with 41 of South Africa; 55 of Mexico; 114 of France and 337 of Japan (Canada and Australia are low at only 3). Exhibit 4: Brazilian Population Breakdown (2011) % of Population # of Cities 500K 350K-500K 200K-350K 50K-200K 30% 6% 9% 22% Below 50K 33% 4,953 Source: IBGE, Company data, Credit Suisse estimates Adjusting mall penetration to the size of the cities, we find that Brazil is not as underpenetrated as many would think. Much to the contrary, cities with more than 500,000 people seem to be well served, with up to 130 sqm of GLA/ 000 per capita, the same ratio as that of a few developed countries. The figures seem even more interesting when taking into account the developments already announced to be opened by the end of We plotted the relationship between GLA/ 000 per capita and GDP per capita (adjusted by purchasing power parity) to have a better idea of the mall offer in each country. As shown in Exhibit 5, while some of the most populated cities (500K+) in Brazil appear to be well served by malls, cities with 350,000 to 500,000 inhabitants appear as a good opportunity. Exhibit 5: GLA Penetration vs. GDP Per Capita GLA/ 000 citizens in sqms South Africa Portugal US 2,205 GLA/ 000 Australia 901 GLA/ 000 Netherlands 678 GLA/ 000 Denmark Japan GLA/'000 citizens Malaysia France United Kingdom Spain 500K> ('13) 500K> ('11) Germany Mexico Italy 350K-500K ('13) 200K-350K ('11) 350K-500K ('11) Brazil '11 Brazil '13 South Korea 50K-200K ('11) Argentina <50K ('11) 50K-200K ('13) <50K ('13) 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000 Source: ABRASCE, UN, Company data and Credit Suisse estimates GDP per capita (U$ PPP Adjusted) Now that we have seen a more adjusted view of the shopping mall sector in Brazil, one may ask whether there is really any growth potential left and to where companies should be directing their greenfields over the next few years. We try to address these questions on the following pages. Brazilian Shopping Malls 6

7 Decision-Making Process Choosing Where to Strike We have talked with listed players in order to better understand the dynamics of opening a new mall. Among several important steps and items, we listed the key points and processes on Exhibit 6. Inarguably, the decision-making process can be much more complex than the step-bystep we describe below, as companies may choose a top-down analysis (from regions then down to land plots) or a bottom-up approach (when a land seller offers an opportunity and the city is then studied). Either way, the process we present below is more common. Exhibit 6: Decision-Making Process for Greenfields 1 Screening Test Filter Brazilian cities through (a) population size; (b) population growth rate; (c) income per household; (d) discretionary income and; (e) Existing GLA. 3 Positioning and delimitation of influence zone Research institute traces the primary, secondary and tertiary zones using time distance to the mall. 2 Site Location After the city is chosen starts the search for the right land plot. Tracking income growth patterns through the city; road access; time distance. Primary Secondary Tertiary 5 Store mix and sales potential Anchor stores are questioned about their interest for the region. Store mix is calculated. Using Abrasce s reference on sales/sqm, potential sales are calculated. 4 Market potential Research institute points the average income and consuming potential of the region. Measures the strength of the already existing centers and calculates the potential market share in the region. Source: Company data, Credit Suisse estimates Brazilian Shopping Malls 7

8 Using our proprietary data base, developed in partnership with CS Retail & Consumption Team, and counting with the breakdown of households budget of all cities in Brazil, we try to delve into the most important items of the screening test, namely: population size, growth, wealth, discretionary income, and GLA offer. 1) Population Size and Growth From our conversations with the listed players, we found that most of our covered sample focuses its analysis on cities with population above 500,000 inhabitants. The main reason for that are basically their larger consumption (at least in absolute terms), higher sales resilience, and people traffic. The problem with this strategy of focusing on cities with more than 500,000 people is that companies need to concentrate their analysis in fewer cities. As shown in Exhibit 7, there are only 39 cities with more than 500,000 inhabitants, mostly state capitals. If companies were to focus also on mid-sized towns (between 350,000 and 500,000 inhabitants) their sample would be expanded, actually by ~70%, with 66 cities. Fortunately, most of these cities are located in the southeast of Brazil, basically the focus of most of our covered universe. We highlight that with the sole exception of BR Malls, the rest of our covered sample does not focus on cities with less than 500,000 inhabitants. Exhibit 7: Cities by Region In # of cities 500k +: 2 350k +: 5 500k +: k +: k +: 4 350k +: 5 500k +: k +: k +: 4 350k +: 7 Source: IBGE (2011), Company data, Credit Suisse estimates Population growth is also considered in the analysis, with companies searching for those with a higher expansion pace. We found that non-core regions are the fastest-growing. As shown in Exhibit 8, the population of both the north and the central west regions are growing above 2% annually. Coupled with that, small-sized cities (50,000 to 200,000 people) have also been outperforming their larger peers, with growth rates ranging at 1.5%. Brazilian Shopping Malls 8

9 Exhibit 8: Growth Rate by City Size In %, 3YR CAGR Exhibit 9: Growth Rate by Region In %, 3Y CAGR 2.3% 2.1% 1.7% 1.6% 1.5% 1.0% 1.1% 1.2% 1.2% 1.2% 1.0% 1.2% Below 50K 50K-200K 200K-350K 350K-500K Above 500K Brazil North Northeast Southeast South Mid West Brazil Source: IPC Marketing, Company data, Credit Suisse estimates 2) Mall-Related and Apparel Spending Source: IPC Marketing, Company data, Credit Suisse estimates Another relevant driver tracked by the mall operators is the population s discretionary income, and more importantly, the potential income for spending on malls. To give a sense of the importance of this driver, when deciding on a new project, Iguatemi considers only the cities with more than R$1bn in discretionary income, which reduces the number of potential cities to 56. In view of the debate about the items included in mall-related expenditures (supermarket and building materials are sometimes included), we preferred to focus only on items more commonly found in a shopping mall, namely: apparel, household appliances, furniture, books, hygiene and healthcare products, food away from home, and leisure. Moreover, we thought it would be interesting to pay special attention to apparel, as it accounts for a significant share of total expenditure. The results shown on Exhibit 10 may surprise most investors, as they indicate that the share of household income spent on malls is highest in the north and northeast regions. The southeast and south regions, on the other hand, stand out on absolute terms but disappoint on a relative basis, mostly as a consequence of a tougher purchasing power comparison, we believe. Brazilian Shopping Malls 9

10 Exhibit 10: Mall-Related and Apparel Annual Spending by Region As a % of annual household budget, 2011 data R$2,509 R$2,027 R$2,259 R$2,392 R$2,115 R$2,231 R$8,315 R$7,211 R$9,438 R$8,494 R$8,017 R$8, % 20.4% 17.7% 17.1% 16.7% 18.1% 6.4% 5.7% 4.2% 4.8% 4.4% 4.7% North Northeast Southeast South Mid West Brazil Mall Related Apparel # Source: IPC Marketing, Company data, Credit Suisse estimates # Mall Related Apparel The same trend can be seen when we break down the numbers by city size. As shown in Exhibit 11, cities with up to 200,000 people stand out as having a larger share of household income spent on malls and apparel. Cities with more than 500,000 people lag behind their smaller peers basically because of the same reason mentioned above for the southeast region. Exhibit 11: Mall-Related and Apparel Annual Spending by City Size As a % of total household budget, 2011 data R$6,870 R$7,948 R$8,617 R$9,306 R$10,139 R$8,604 R$1,861 R$2,100 R$2,248 R$2,392 R$2,546 R$2, % 18.4% 18.1% 18.0% 17.8% 18.1% 5.1% 4.9% 4.7% 4.6% 4.5% 4.7% Below 50K 50K-200K 200K-350K 350K-500K Above 500K Brazil Mall Related Apparel # Source: IPC Marketing, Company data, Credit Suisse estimates # Mall Related Apparel 3) Spending Elasticity through Income Levels In their analyses, mall operators also pay strong attention to the potential increases in spending that may come as a consequence of changes in income levels. It is reasonable Brazilian Shopping Malls 10

11 to assume that as the population climbs the income ladder, a larger amount of disposable income will be spent on items that can be purchased at shopping malls. In Exhibit 12 and Exhibit 13 we show how mall-related spending varies through income levels. It is interesting to see that the trend now reverses, and both the Southeast and South regions show the highest spending elasticity. Exhibit 12: Elasticity of Mall-Related Spending by Region In R$ per household and per year, 2011 data 7.6x 35,762 29,455 33,380 30,115 28,150 D C2 B2 A2 32, x 7.7x 7.6x 7.0x 7.3x 12,283 11,563 10,656 10,306 10,373 10,797 4,701 4,693 4,342 3,429 3,961 3,996 4,398 3,182 3,075 2,762 2,842 3,073 North Northeast Southeast South Mid West Brazil Source: IPC Marketing, Company data and Credit Suisse estimates Similar results can be found when analyzing the data from the standpoint of city size. Cities with a population greater than 500,000 people or between 350,000 and 500,000 people appear to have the most significant increase in spending when they move upward in their income level. Exhibit 13: Elasticity of Mall-Related Spending by City Size In R$ per household and per year, 2011 data D C2 B2 A2 27,696 28,984 29,276 33,097 31,717 32, x 6.8x 6.9x 7.0x 7.0x 7.3x 11,813 11,262 10,925 10,667 10,655 10,797 4,246 4,289 4,243 4,546 4,725 4,398 2,850 3,014 3,028 3,234 3,309 3,073 Below 50K 50K-200K 200K-350K 350K-500K Above 500K Brazil Source: IPC Marketing, Company data and Credit Suisse estimates Brazilian Shopping Malls 11

12 The Cities Scorecard After listing all the above-mentioned items, we ran a screening test on our proprietary database in order to find the most attractive cities to invest in. In the exercise shown on the following tables, we divided our sample in quartiles, attributing points to each characteristic according to their importance. The items considered in the analysis and their respective weights are: 1) Existing GLA and announced increases 2 to 10 points The existing GLA and the greenfields to be opened by the end of 2013 (according to ABRASCE) were taken into account. Theoretically, underpenetrated cities tend to offer better opportunities. 2) City Size 1 to 6 points We considered cities with population above 350,000 people since smaller cities are not the focus of our sample. Theoretically, the larger the city the more attractive it is. 3) GDP per capita 1 to 4 points Despite agreeing that income per household would be a better metric (unfortunately no public breakdown by city is available), we believe that GDP/capita is also an interesting way of measuring the wealth of a city. 4) Spending as a % of household income 1 to 4 points We considered both mall-related (2 points) and apparel (2 points) spending as a % of household income. 5) Spending elasticity 1 to 4 points The increase in mall-related (2 points) and apparel (2 points) spending when families move up from the C to B class. 6) Population growth 1 to 4 points Theoretically, faster-growing cities are more attractive than their slow-growth peers. As such, in Exhibit 14 we list the top-20 cities to invest in according to our scorecard. The entire table (considering all the 66 cities with more than 350,000 people) is shown in the Appendix. It is interesting to note that 5 of the 20 cities are located in the State of São Paulo. Moreover, the ranking shows that the most attractive opportunities are well divided between cities with more than 500,000 people (65% of the sample) and a population between 350,000 and 500,000 people (35% of the list). Exhibit 14: Top-20 Cities Growth % of Household Budget Elasticity (B2 class x C2) # Malls GLA (sqm) GLA/'000 citzen GDP/Capita Rank City State Points Population Pace Mall Related Apparel Mall Related Apparel MANAUS AM 26 1,847, % 23,286 21% 6% , , GUARULHOS SP 26 1,238, % 24,994 18% 4% , , SAO BERNARDO DO CAMPO SP , % 35,680 18% 4% ,802 96, ANANINDEUA PA , % 6,416 22% 7% n.a. n.a. n.a. n.a. n.a. n.a. 5 DUQUE DE CAXIAS RJ , % 29,501 17% 4% ,000 32, PORTO VELHO RO , % 17,260 19% 6% ,899 29, BELEM PA 24 1,403, % 11,496 21% 7% , , FORTALEZA CE 23 2,480, % 12,688 20% 5% , , BETIM MG , % 57,009 18% 5% n.a. 2 n.a. 68,093 n.a TERESINA PI , % 10,841 22% 6% ,117 45, PIRACICABA SP , % 26,031 18% 4% ,804 27, FEIRA DE SANTANA BA , % 10,745 20% 5% ,816 23, SAO LUIS MA 22 1,027, % 15,382 18% 6% , , MACEIO AL , % 10,962 18% 6% , , SAO GONCALO RJ 22 1,011, % 9,699 17% 4% ,582 58, SANTOS SP , % 54,055 18% 4% ,245 10, SERRA ES , % 28,496 17% 5% ,275 29, JOINVILLE SC , % 26,834 17% 5% ,184 68, SAO JOSE DOS CAMPOS SP , % 35,751 18% 4% ,168 98, JABOATAO DOS GUARARAPES PE , % 10,279 22% 6% ,000 45, Source: Company data, Credit Suisse estimates. n.a. means that there are no malls in the city. Brazilian Shopping Malls 12

13 Growth Potential for our Sample When deciding in which city to invest, mall operators rely not only on the above analysis but also on the help of specialized consultancy groups. Among their tasks, consultants carry out an in-depth qualitative research in the selected cities in order to find the potential demand of the nearby population and the resilience of the existing retail floor space (basically by analyzing the strength of consumption in street stores). Accordingly, our analysis lacks both of these metrics since there is no data available on the existing retail floor. Moreover, our analysis does not include such an in-depth qualitative view of consumption from neighboring cities. Therefore, any calculation of the GLA potential for a city would be too aggressive and theoretical. Nevertheless, in order to gauge the expansion potential of our sample, we considered the growth strategies of each company by counting the number of cities that fit into their required parameters. The table is shown in Exhibit 15. Exhibit 15: Growth strategies and potential City Size Above 350,000 Above 500,000 Above 500,000 Above 500,000 Above 500,000 Region (Preference) Southeast, South n.a. n.a. Southeast, South Southeast, South Other criteria no no no Focus on improving core regions Large rich populated areas. Annual mall related expenditures of at least R$1bn. Predominance of class A and B population. # of available cities Source: IPC Marketing, Company data, Credit Suisse estimates As can be seen, BR Malls stands out as having the largest growth opportunities in number of cities, followed by Aliansce and Sonae Sierra. Despite coming in fourth place, Multiplan s strategy relies mainly on improving its core areas (i.e. the real estate complexes of Barra Shopping, Morumbi Shopping, Ribeirão Preto, and Barra Shopping Sul), which can also generate important profits. Iguatemi comes in last place as its strategy of being focused on rich, largely populated areas imposes a significant limit to its growth potential (less than 20 cities). Accordingly, we tracked all the cities that fit into the parameters of Iguatemi and compared them with their existing portfolio. The result is shown in Exhibit 16. Brazilian Shopping Malls 13

14 Exhibit 16: Potential Cities According to Iguatemi s Parameters GLA/'000 citzen City State Population Class A* IGTA Malls** Sao Paulo SP 11,328, % Rio De Janeiro RJ 6,371, % Brasilia DF 2,620, % Belo Horizonte MG 2,389, % Curitiba PR 1,763, % Porto Alegre RS 1,414, % Guarulhos SP 1,238, % Campinas SP 1,092, % Sao Bernardo Do Campo SP 771, % Vila Velha ES 750, % Santo Andre SP 676, % Osasco SP 667, % Sao Jose Dos Campos SP 637, % Ribeirao Preto SP 616, % Sorocaba SP 596, % Joinville SC 524, % Juiz De Fora MG 524, % Londrina PR 513, % Caxias Do Sul RS 443, % Florianopolis SC 430, % Sao Jose Do Rio Preto SP 413, % Jundiai SP 375, % Sao Carlos SP 225, % Source: Company data, Credit Suisse estimates. * % of class A households in the city. ** both operating and greenfields. Looking at the table, one might think that Iguatemi s growth prospects are limited or that the company may change part of its strategy in the future. In our view, despite the limited number of not-so-underpenetrated cities, we expect Iguatemi to continue investing in its core areas, which are: the São Paulo metropolitan region and the south of Brazil. We believe its renowned luxury brand should be enough to attract a good store mix and solid traffic in order to gain market share and support its projects returns. Brazilian Shopping Malls 14

15 Final Thoughts Opportunities Still out There The opening of malls per year in the next decade seems very feasible, taking into account the retail growth potential and malls still low penetration in total sales. Besides, we believe our covered sample should be able to profit from that. In our view, despite being a very predictable and build-and-rent business, the mall sector requires attested expertise since dealing with tenants and building-up an attractive store mix is not a beginners task. This should support our positive view on the listed players regardless of the increasing competition in the industry. Looking forward, we expect companies to continue focusing on largely populated cities before expanding into mid-sized cities. From our conversations with players management, growth should come from the following, in order of importance: (1) greenfields in cities with more than 500,000 people; (2) expansion of existing malls since returns are high and risk is lower; and (3) new ventures in cities with a population ranging from 350,000 to 500,000 people. Despite pointing out that cities with more than 500,000 people already have a significant mall offer, we believe that there are a few large cities with a still underpenetrated market. As shown in Exhibit 17, a few cities stand out with an alarming number of malls. Exhibit 17: Mall Penetration in Selected Cities with More than 350,000 People In GLA/ 000 citizens Porto Alegre Ribeirao Campinas Osasco Preto Santo Andre Maringa Cuiaba Londrina Sao Paulo 500K 350K- 500K Sao Goncalo Bauru Teresina Brazil Campina Feira De Duque De Sao Grande Santana Caxias Bernardo Do Campo Santos Nova Iguacu Source: ABRASCE, IBGE, Company data, Credit Suisse estimates The smaller city dilemma: is there any significant risk there? Before concluding the analysis, one might ask if there is any problem with developing projects in smaller cities. We believe there is not. In our view, the focus on larger cities seems to be much more a core strategy than necessarily the only option. Investing in smaller cities (from 350,000 to 500,000 people) does not inevitably imply lower returns or more complex operations. We agree that, at a certain point, these cities might translate into less resilient sales potential, since there is less consumption in absolute terms or lower occupancy rates when the malls start their operations. On the other hand, we argue that projects in less populated areas may provide attractive construction economies, such as (a) cheaper land (acquisition either in cash or through swaps); (b) cheaper construction (since the mall may have only one floor and a vertical parking lot does not need to be built). According to our calculations, greenfields returns are more sensitive to changes in the construction cost than changes in the performance of sales per sqm. As shown in Exhibit Brazilian Shopping Malls 15

16 18, a 15% drop in the Capex/sqm has a higher positive effect on the IRR than a 15% increase in sales per sqm. It is important to highlight that in this greenfield model we assumed a 13% increase in occupancy costs for satellite stores and a ~3% real growth in rents until the tenth year, with no further increases in real terms thereafter. Exhibit 18: Greenfield IRR Sensitivity Analysis in % CAPEX/sqm -30% -15% 7,000 15% 30% -30% 17.3% 13.9% 11.5% 9.8% 8.5% -15% 19.9% 16.0% 13.3% 11.4% 10.0% % 18.0% 15.1% 13.0% 11.3% 15% 24.7% 20.0% 16.8% 14.4% 12.7% 30% 27.1% 21.9% 18.4% 15.9% 14.0% Sales/sqm Source: Company data, Credit Suisse estimates Brazilian Shopping Malls 16

17 Appendix 1: Ranking our Covered Sample Portfolio and Pipeline We used the same parameters applied to rank the top-20 cities to invest in to rank the portfolio of our covered companies. We highlight that the total score was weighted by the size of the malls in the whole portfolio. Before presenting the final results of the analysis, we note that this exercise does not take into account the neighborhood of the exact location of the malls but the characteristics of their city. Therefore, the analysis may distort some of the malls traffic profile, hindering a more accurate score for comparison purposes. Even so, we believe that this study gives a good idea of the overall quality of each portfolio. As shown in Exhibit 19, we ranked the portfolios in the following order: 1) Sonae Sierra 21 points Presence in less penetrated markets with high GDP per capita and a large share of household income spent on malls and apparel. Highest scores coming from Manaus (AM - 26 points) and São Bernardo do Campo (SP 25 points). 2) Iguatemi 20 points Largest GDP per capita exposure with a high percentage of class A households and a bulky share of household income spend on malls and apparel. Footprint in well-served markets weighed negatively on the final result. Highest scores: São Carlos (SP 21 points) and Brasilia (DF 21 points). 3) BR Malls 19 points Cities with lowest population (3.2mn inhabitants, on average) and low GDP/ capita (R$22,500 per year) despite the good footprint in underpenetrated areas. Highest scores: Manaus (AM 26 points) and Cotia (SP 26 points). We highlight that BR Malls portfolio was negatively affected by Londrina (PR 15 point) and Maringa (PR 15 points), both largely penetrated cities with low population and GDP/capita. 4) Aliansce 19 points Focus on low GDP per capita cities but with low penetration of malls (124 GLA/ 000 per capita in 2011 and 148 by year-end 2013). Good percentage of income spending related to malls. Highest scores: Belem (PA 24 points) and Duque de Caxias (RJ 24 points). 5) Multiplan 19 points Similar to Iguatemi: good exposure to high GDP per capita cities and a significant share of class A households. Highest presence in wellserved areas (187 GLA/ 000 per capita by the end of 2011). Highest scores: Brasilia (DF 21 points) and Curitiba (PR 21 points). Brazilian Shopping Malls 17

18 Exhibit 19: Portfolio Ranking Points Avg Population 3,251,582 3,954,191 4,142,927 3,688,738 3,490,435 Avg GDP/capita 22,574 30,154 31,445 21,846 28,851 % of Household Expenses Discretionary 17.3% 17.0% 17.5% 17.8% 18.3% Apparel 4.4% 4.3% 4.2% 4.4% 4.5% Elasticity (B2 class x C2) Discretionary Apparel GLA/'000 citizens Current Class A 6.5% 8.2% 8.0% 6.0% 6.5% Sales/sqm (9M11 Avg) 1,039 1,280 1, NOI/sqm (9M11 Avg) Source: IPC Marketing, Company data, Credit Suisse estimates Greenfield Pipeline Ranking the Growth The same analysis can be made for the announced pipeline of greenfield malls. The ranking is shown on the following table. This time, Aliansce stands out among peers with 21 points thanks to its projects in Belem (PA 24 points) and Maceio (AL 23 points). In second place was BR Malls (20 points), followed by Iguatemi and Sonae Sierra (tied with 19 points). Multiplan stood in last place (18 points) mostly due to its projects in Rio de Janeiro (RJ 17 points). Exhibit 20: Greenfield Pipeline ranking # Malls GLA/'000 citzen % of Household Budget GDP/capita Done Own GLA City State Population Points Discretionary Apparel Aliansce 81, , % 5.1% Parque Shopping Belém 2Q12 16,375 Belem PA 1,403, , % 6.5% Parque Shopping Maceió 2Q13 18,203 Maceio AL 947, , % 5.9% Boulevard Vila Velha 4Q12 21,000 Vila Velha ES 750, , % 4.3% Shopping Nações Bauru 4Q12 26,250 Bauru SP 346, , % 4.2% BR Malls 147, , % 4.5% Shopping BH 2Q12 21,902 Belo Horizonte MG 2,389, , % 4.7% São Bernardo 4Q12 25,961 Sao Bernardo do Campo SP 771, , % 4.1% Londrina Norte Shopping 4Q12 22,821 Londrina PR 513, , % 4.6% Catuaí Shopping Cascavel 4Q13 21,103 Cascavel PR 290, , % 4.6% Shopping Vila Velha 4Q13 31,430 Vila Velha ES 750, , % 4.3% Shopping Contagem 4Q13 24,500 Contagem MG 609, , % 4.9% Iguatemi 150, , % 4.2% JK 2Q12 17,623 Sao Paulo SP 11,328, , % 4.1% Ribeirão Preto 2Q13 35,293 Ribeirao Preto SP 616, , % 4.1% Jundiaí 4Q13 23,700 Jundiai SP 375, , % 4.1% São José Rio Preto 2Q14 30,448 Sao Jose do Rio Preto SP 413, , % 4.2% Votorantim 3Q13 43,853 Sorocaba SP 596, , % 4.2% Sonae Sierra 162, , % 4.5% Uberlandia 3Q12 43,600 Uberlandia MG 611, , % 4.8% Boulevard Londrina 4Q12 40,400 Londrina PR 513, , % 4.6% Passeio das Águas Goiania 4Q13 78,100 Goiania GO 1,324, , % 4.2% Multiplan 117, , % 4.1% Jundiai Shopping 4Q12 35,820 Jundiai SP 375, , % 4.1% Village Mall 4Q12 25,580 Rio de Janeiro RJ 6,371, , % 3.6% Parkshopping Campo Grande 4Q12 37,889 Rio de Janeiro RJ 6,371, , % 3.6% Shopping Maceió 2Q13 18,203 Maceio AL 947, , % 5.9% Source: Company data, Credit Suisse estimates Brazilian Shopping Malls 18

19 Appendix 2: Ranking of Cities with More than 350,000 People Exhibit 21: Ranking of cities with more than 350,000 people Growth % of Household Budget Elasticity (B2 class x C2) # Malls GLA (sqm) GLA/'000 citzens GDP/Capita Rank City State Points Population Pace Discretionary Apparel Discretionary Apparel MANAUS AM 26 1,847, % 23,286 21% 6% , , GUARULHOS SP 26 1,238, % 24,994 18% 4% , , SAO BERNARDO DO CAMPO SP , % 35,680 18% 4% ,802 96, ANANINDEUA PA , % 6,416 22% 7% n.a. n.a. n.a. n.a. n.a. n.a. 5 DUQUE DE CAXIAS RJ , % 29,501 17% 4% ,000 32, PORTO VELHO RO , % 17,260 19% 6% ,899 29, BELEM PA 24 1,403, % 11,496 21% 7% , , FORTALEZA CE 23 2,480, % 12,688 20% 5% , , BETIM MG , % 57,009 18% 5% n.a. 2 n.a. 68,093 n.a TERESINA PI , % 10,841 22% 6% ,117 45, PIRACICABA SP , % 26,031 18% 4% ,804 27, FEIRA DE SANTANA BA , % 10,745 20% 5% ,816 23, SAO LUIS MA 22 1,027, % 15,382 18% 6% , , MACEIO AL , % 10,962 18% 6% , , SAO GONCALO RJ 22 1,011, % 9,699 17% 4% ,582 58, SANTOS SP , % 54,055 18% 4% ,245 10, SERRA ES , % 28,496 17% 5% ,275 29, JOINVILLE SC , % 26,834 17% 5% ,184 68, SAO JOSE DOS CAMPOS SP , % 35,751 18% 4% ,168 98, JABOATAO DOS GUARARAPES PE , % 10,279 22% 6% ,000 45, BRASILIA DF 21 2,620, % 50,438 17% 4% , , DIADEMA SP , % 25,066 18% 4% ,332 29, CURITIBA PR 21 1,763, % 24,720 16% 5% , , UBERLANDIA MG , % 25,484 17% 5% ,200 98, SOROCABA SP , % 24,272 18% 4% , , MACAPA AP , % 12,769 22% 7% n.a. 1 n.a. 46,338 n.a JOAO PESSOA PB , % 12,301 18% 5% ,350 96, CAMPOS DOS GOYTACAZES RJ , % 45,117 17% 4% ,032 33, CAXIAS DO SUL RS , % 30,499 17% 5% ,564 63, MAUA SP , % 15,750 18% 4% ,000 40, FLORIANOPOLIS SC , % 20,305 17% 5% ,694 71, RIO BRANCO AC , % 12,542 25% 7% ,497 27, APARECIDA DE GOIANIA GO , % 9,009 17% 4% ,000 40, GOIANIA GO 20 1,324, % 16,682 16% 4% , , CAMPINAS SP 20 1,092, % 29,732 18% 4% , , RECIFE PE 20 1,548, % 15,903 22% 6% , , PORTO ALEGRE RS 20 1,414, % 26,312 17% 5% , , JUIZ DE FORA MG , % 14,094 17% 5% ,239 59, RIBEIRAO PRETO SP , % 26,084 18% 4% , , NOVA IGUACU RJ , % 11,047 17% 4% ,630 18, CONTAGEM MG , % 24,641 18% 5% , , CARAPICUIBA SP , % 7,813 18% 4% n.a. n.a. n.a. n.a. n.a. n.a. 43 SAO PAULO SP 19 11,328, % 35,272 18% 4% ,818,697 2,021, BAURU SP , % 18,906 18% 4% ,552 54, VILA VELHA ES , % 14,609 16% 4% , , SALVADOR BA 19 2,701, % 10,949 19% 5% , , MOGI DAS CRUZES SP , % 20,552 18% 4% ,001 44, NATAL RN , % 12,862 19% 5% , , OLINDA PE , % 6,547 22% 6% n.a. n.a. n.a. n.a. n.a. n.a. 50 CAMPINA GRANDE PB , % 10,147 19% 5% ,241 17, OSASCO SP , % 43,994 18% 4% , , BELFORD ROXO RJ , % 8,280 17% 4% n.a. n.a. n.a. n.a. n.a. n.a. 53 JUNDIAI SP , % 47,396 18% 4% ,633 99, ARACAJU SE , % 12,994 21% 5% , , BELO HORIZONTE MG 18 2,389, % 18,183 17% 5% , , SANTO ANDRE SP , % 21,844 18% 4% , , CAMPO GRANDE MS , % 15,422 17% 5% , , RIO DE JANEIRO RJ 17 6,371, % 22,103 17% 4% ,017 1,197, SAO JOSE DO RIO PRETO SP , % 18,776 18% 4% , , NITEROI RJ , % 22,530 17% 4% ,000 62, MONTES CLAROS MG , % 10,503 18% 5% ,717 40, SAO JOAO DE MERITI RJ , % 8,514 17% 4% ,223 36, CUIABA MT , % 17,831 16% 5% , , CARIACICA ES , % 10,534 17% 5% n.a. 1 n.a. 32,000 n.a LONDRINA PR , % 17,396 16% 5% , , MARINGA PR , % 21,711 16% 5% ,747 78, Source: Company data, Credit Suisse estimates Brazilian Shopping Malls 19

20 Exhibit 22: Multiplan corporate template R$ in millions, unless otherwise stated BASICS Sector Real Estate - Shopping Malls Ticker MULT3 Price (R$) 38.6 Target (R$) 41.0 Target (US$) 24.0 Recommendation NEUTRAL Mkt. cap. (R$ mn) 6,919 POSITIVES Free Float (R$mn) 2,691 SHAREHOLDING STRUCTU ON PN TOTAL J. Peres / M. Peres 56, % 0 0% 56, % Ontario Teachers P. Plan 40, % 11, % 52, % Treasury 1, % 0 0% 1, % NEGATIVES Free Float 69, % 0 0% 69, % Possible slowdown in credit Operating margins still below peers Underleveraged capital structure TOTAL 167, % 11, % 179, % COMPANY DESCRIPTION Multiplan is one of the leading developers, owners and operators of Shopping Centers in Brazil. As of December, their own GLA was of 410k m², composed of 14 shopping malls in the South and Southeast regions of Brazil. 30 Years of experience in the sector High quality portfolio Expertise in greenfield and expansion development Experienced management team Owned GLA - '000 m2 OWNERSHIP STRUCTURE % J. Peres / M. Peres Ontario Teachers P. Plan Treasury 2008A 2009A 2010A 2011E 2012E 2013E 29% Free Float FINANCIAL METRICS (R$ m 2008A 2009A 2010A 2011E 2012E 2013E OPERATING METRICS 2008A 2009A 2010A 2011E 2012E 2013E Net revenues ,027 GLA(YE) Gross Profit GLA (AVERAGE) Gross margin 77.8% 78.6% 80.3% 79.9% 79.5% 80.3% FFO EBIT FFO margin 60% 40% 43% 55% 47% 50% EBIT margin 24% 52% 55% 63% 63% 67% NOI (comparable calc.) EBITDA NOI per sq. meter 1,182 1,166 1,435 1,500 1,501 1,472 EBITDA margin 65% 60% 63% 72% 73% 76% EBITDA per sq. meter ,018 1,205 1,285 1,369 Adj. EBITDA Net financial expenses 4 (5) (50) (56) LEVERAGE 2008A 2009A 2010A 2011E 2012E 2013E Taxes (20) (88) (119) (121) (122) (177) Net debt / Equity 12% -10% -5% 11% 24% 18% Reported Net income Net debt / Ebitda 0.9x -1.0x -0.4x 0.7x 1.4x 0.8x Net margin 19% 36% 38% 45% 39% 42% Net debt / Total assets 0.1x -0.1x 0.0x 0.1x 0.1x 0.1x # shares ('000) Capex / Operat.Cash Flow n.a. 0.0x 0.0x 0.0x 0.0x 0.0x EPS (R$) Total Assets / Equity 1.3x 1.3x 1.4x 1.5x 1.6x 1.6x NOPAT (before &A) Depreciation RETURN / YIELD 2008A 2009A 2010A 2011E 2012E 2013E Amortization ROIC 11.3% 10.9% 12.7% 13.0% 13.7% 18.6% FCFE (249) 660 (33) (305) WACC Dividends Cost of Equity (ke) 10.5% 10.5% 10.5% 10.5% 10.5% 10.5% Total assets 2,583 3,671 3,986 4,420 5,215 5,566 ROE 11.8% 6.7% 8.4% 11.4% 11.4% 15.2% Cash FFO Yield 3.6% 5.0% 5.3% 7.5% Debt ,412 1,493 Dividend Yield 1% 2% 2% 3% PP&E 1,573 2,022 2,515 3,186 3,804 3,855 Net debt 228 (274) (151) VALUATION 2008A 2009A 2010A 2011E 2012E 2013E Book value 1,931 2,823 2,943 3,029 3,186 3,411 EV / Adj. EBITDA 18.6x 16.0x 13.7x 9.7x Invested capital 2,192 2,601 2,866 3,490 4,094 4,171 P/FFO 28.0x 20.0x 19.0x 13.4x Market cap. 6,919 6,919 6,919 6,919 6,919 6,919 P/E 31.9x 24.2x 23.3x 15.9x EV 7,147 6,645 6,768 7,251 7,684 7,520 P/BV 2.4x 2.3x 2.2x 2.0x Source: Company data, Credit Suisse estimates Brazilian Shopping Malls 20

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