Shops as Sites of Change and New Consumption Preferences
University of Amsterdam Graduate School of Social Sciences (GSSS) MSc Urban & Regional Planning
Shops as Sites of Change and New Consumption The Effect of Residential Gentrification on the Retail Supply in Four Amsterdam Neighborhoods.
Author: Jesse Eradus, MSc. Supervisor: Dr. H. Kok Second Reader: Dr. F. Savini Date: 11-06-2019 Word count: 20.007 Preface
2 This thesis is the end-product of the master’s program Urban & Spatial Planning at the University of Amsterdam. It’s subject: one of the most shaping forces within contemporary cities worldwide: gentrification. Since the term was coined by Ruth Glass in 1964, the scholarship of gentrification has developed itself greatly with many contributions. It is a process that unfolds within the socio-spatial fabric of urban areas, greatly affecting the societal and economic character of neighborhoods worldwide. Now as the framework of residential gentrification has been developed to a robust level, scholars are turning to other niches that are part of the process. One of these is commercial gentrification, the process of gentrification that unfolds in commercial establishments rather than in homes. It concerns the change in commercial structure by which old shops are replaced or ‘upgrade’ to cater to the change imposed by residential gentrification. Commercial spaces form the ‘third spaces’ behind the home and the workplace. They are places where we buy our daily and non-daily necessities. More importantly however, they also form sites of social interaction with peers.
Researching commercial gentrification was a process of reflection. The characteristics of gentrifiers include that they are highly-educated, relatively young, earning a higher income than their counterparts, and enjoying living in the heart of cities worldwide. This thesis shows that they exert influence upon the commercial space in which they live, making use of consumption spaces and giving meaning to identity through the purchase of goods or services. They are a group that enjoys going to a local coffee place, drinking a cappuccino with soy-milk whilst working on MacBooks. Or enjoying craft-beers in one of the café’s that the city of Amsterdam is rich. This raised the question: am I part of this process? I too have enjoyed a higher-education, will hopefully make a relatively stable income, and are younger than the average city- dweller. Furthermore, I only moved to Amsterdam for my studies, registering after my 18th birthday and enjoy living in the throbbing urban heart of Amsterdam. I’ve worked on my thesis in the coffee places that were the subject of my research. In the spare free-time that I had left, I would visit local popular cafés and drank craft-beers with my girlfriend or my friends. Have I become what has been the subject of my study?
3 The end-product of this period of interest into commercial gentrification is one that I am proud of. In the process, it has become abundantly clear how difficult researching not only residential gentrification is, but commercial gentrification as well. Conclusions require
I would like to thank Dr. Herman Kok for being my thesis supervisor and for the assistance during the process of writing this thesis. His guidance included timely meetings, sharp comments, and helpful insights for the development of the thesis. This included changing the location of our two-weekly meetings to a coffee-place that was close by my home, which included grilled cheese sandwiches and big mugs of coffee which I greatly enjoyed. He furthermore assisted by providing great connection that allowed this thesis to research the total retail supply in a quantitative manner. My gratitude extends to Prof. Dr. T. Tasan-Kok for inspiring me greatly in her class “Contemporary Approaches in Property-Led Urban Planning” that was able to sharply analyze the current state of the planning tradition and how it relates to the economy in which it operates. This was one of the many subjects that came to mind thanks to the framework that she provided in class. Furthermore, she helped guide me through the thesis preparation process including establishing a scope and a research question. Furthermore, thanks to Locatus for providing me with access to their rich data source on the commercial fabric. Without this, a statistical analysis of the retail landscape of Amsterdam would not have been possible.
A last word of gratitude is to my family, friends and especially my girlfriend for assistance during the process and helping me overcome the challenges associated with it.
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Table of Contents
List of Figures and Tables ...... 7
1. Introduction ...... 9
2. Theoretical Framework ...... 14
2.1. Context: Financialization ...... 15
2.2. Developments and Challenges of Retail within the Netherlands ...... 18
2.3. Location Theory and its Role in Retail Planning ...... 21
2.4. Residential Gentrification ...... 23 2.4.1. The Economic Aspect of Gentrification: Economic Revitalization ...... 24 2.4.2. The Social Aspect of Gentrification: Displacement ...... 25 2.4.3. The Role of various levels of Government ...... 26 2.4.4. Residential Gentrification in Amsterdam ...... 27 2.4.5. Operationalizing Gentrification: Three Types of Indicators ...... 28
2.5. Commercial Gentrification ...... 30 2.6.1. Operationalizing Commercial Gentrification ...... 32 2.6.2. Where does Commercial Gentrification occur? ...... 33
3. Methods ...... 35
3.1. Conceptual Framework ...... 35
3.2. Measuring Residential and Commercial Gentrification ...... 37
3.3. Case Selection ...... 39
3.4. Data Selection & Methods ...... 40
3.5. Hypotheses ...... 43
4. Gentrification in the Four Cases ...... 44
4.1. Social Indicators of Gentrification ...... 44
4.2. Physical Indicators of Gentrification ...... 48
4.3. Economic Indicators of Gentrification ...... 50
4.4. Classifying Gentrification in the Four Neighborhoods...... 52
5. Results ...... 54
5.1. Total Retail Supply Distribution...... 55
5.1.1. Share of Chain Stores ...... 57
6. Daily Goods Supply ...... 59
5 6.1 External Upgrading ...... 59
6.2. Quantitative Robustness Tests ...... 61
6.3. Internal Upgrading ...... 63
7. The Retail Supply ...... 65
7.1. Robustness Tests ...... 66
8. The Leisure Supply ...... 68
8.1. Distribution of the Leisure Supply ...... 68
8.2. Leisure Robustness Tests ...... 69
8.3. Popular Cafés and Restaurants: Qualitative Change ...... 71
8.4 High-End Fitness: Qualitative Change ...... 72
9. The Service Supply ...... 74
9.1. Robustness Tests & Geographic Distribution ...... 75
10. Conclusion & Discussion ...... Fout! Bladwijzer niet gedefinieerd.
10.1 The Effect of Gentrificiation on the Total Retail Supply ... Fout! Bladwijzer niet gedefinieerd.
10.2. Implications ...... Fout! Bladwijzer niet gedefinieerd.
10.3 Limitations ...... Fout! Bladwijzer niet gedefinieerd.
10.4. Recommendations ...... Fout! Bladwijzer niet gedefinieerd.
Literature ...... 81
6 List of Figures and Tables Figure 1: Map of Included Neighborhoods. Source: QGis, Municipality of Amsterdam...... 13 Figure 2: Development of Retail Space Prices in Amsterdam between 2005 and 2018. Source: Cushman & Wakefield, 2018...... 20 Figure 3: Development of the Revenue-index of F&B and Retail in Amsterdam. Source: CBS, 2017. 21 Figure 4: Central Places Theory. Source: Adaptation by Evers et al 2005; of Christaller, 1933...... 23 Figure 5: Rent Gap Theory. Source: Smith, 1987...... 25 Figure 6: Neighborhoods of Amsterdam classified alongside axis of upgrading. Source: Hochstenbach, 2017...... 27 Figure 7: Conceptual Framework...... 37 Figure 8: Indicators of Residential and Commercial Gentrification...... 38 Figure 9: Ethnicity in Amsterdam 2008-2018. Source: BBGA, 2019...... 46 Figure 10: Shares of "New City-Dwellers". Source: BBGA, 2019...... 47 Figure 11: Education Level 2010-2016. Source: BBGA, 2019...... 48 Figure 12: Tenure Distribution. Source: BBGA, 2019...... 49 Figure 13: Buildyear. Source: Gemeente Amsterdam, 2018...... 50 Figure 14: Procentual change in value per square meter 2014-2018 vs. MSA average. Source: BBGA, 2019...... 51 Figure 15: Amsterdam Neighborhoods per initial status (2004) and upgrading or downgrading. Source: Hochstenbach & Van Gent, 2014...... 53 Figure 16: Total Retail Distribution. Source: Locatus, 2019...... 56 Figure 17: Change in Total Retail Distribution. Source: Locatus, 2019...... 57 Figure 18: Share of Individually Owned Stores. Source: Locatus, 2019...... 58 Figure 19: Distribution of Specific Stores as Share of the Total Daily Supply. From top-left to bottom- right (1) delicatessen, (2) wine-stores, (3) mini-markets and (4) vegetables/fruit. Source: Locatus, 2019...... 62 Figure 20: Distribution of Differentiated Supermarkets. Source: Locatus, 2019...... 64 Figure 21: Distribution of Four Types of Retail Stores. From top-left to bottom-right: (1) second-hand clothing, (2) second-hand stores, (3) hobby stores and (4) living-stores. Source: Locatus, 2019...... 67 Figure 22: Distribution of F&B Instagram Popularity...... 72 Figure 23: Differentiation in Fitness Establishments. Source: Locatus, 2019...... 73 Figure 24: Distribution of a Selection of Service Stores on City-Level...... 77
Table 1: Retail Challenges and their Impact on Shopping Areas. Source: Platform 31, 2014...... 19 Table 2: Variables and Indicators of Gentrification...... 30 Table 3: Earlier work on commercial gentrification and where it might manifest. Source: Authors...... 34 Table 4: Quantitative and Qualitative Change in the Total Retail Supply...... 39 Table 5: Case Selection...... 40 Table 6: Hypotheses of the Effect of Residential Gentrification...... 43 Table 7: Population, Households, and Age. Source: BBGA, 2019...... 45 Table 8: Housing Supply and Change 14-18. Source: BBGA, 2019; CBS, 2017...... 49 Table 9: Value per square meter. Source: BBGA, 2019...... 51 Table 10: Income. Source: CBS, 2005; 2010; 2015; 2017...... 52 Table 11: Classification of Neighborhoods...... 54 Table 12: Selection of Observations of share of stores of total daily composition in 2019. Source: Locatus, 2019...... 60 Table 13: Selection of observations of change in the share of stores in the total daily supply 2005- 2019. Source: Locatus, 2019...... 61 Table 14: Spearman Correlation Test for the effect of Residential Gentrification on amount of selling points and store surface of selected daily stores. (* = significant at 0.010 level, ** = significant at 0.05 level, *** = signficant at 0.001 level). Source: Locatus, 2019...... 62 Table 15: In-Category Differntiation of Supermarkets. Source: Locatus, 2019...... 64 Table 16: Selection of Observations from Distribution of Retail Stores. Source: Locatus, 2019...... 65 Table 17: Selected Observations of Change in Retail Supply 2005-2019. Source: Locatus, 2019. .... 66 Table 18: Spearman correlation test for effect of residential gentrification on amount of selling points and store surface of selected daily stores (* = significant at 0.010 level, ** = significant at 0.05 level, *** = significant at 0.001 level)...... 67 Table 19: Selection of Observations in Leisure Supply. Source: Locatus, 2019...... 68 Table 20: Observations of Change in Leisure Establishments 2005 to 2019. Source: Locatus, 2019. 69
7 Table 21: Spearman Correlation Test for Effect of Residential Gentrification on Selected Leisure- Oriented Establishments. (* = significant at the 0.010 level, ** = significant at the 0.05 level, *** = significant at the 0.001 level)...... 70 Table 22: Results of Instagram Scraping. Source: Instagram, Phantombuster.com...... 72 Table 23: Selection of Observations in the Service Supply. Source: Locatus, 2019...... 75 Table 24: Selection of Changes in the Service Supply. Source: Locatus, 2019.Fout! Bladwijzer niet gedefinieerd. Table 25: Spearman Correlation Test for Relationship between Gentrification and The Service Supply. (* = significant at the 0.010 level, ** = significant at the 0.05 level, *** = significant at the 0.001 level). Source: Locatus, 2019...... 76
ABSTRACT The process of residential gentrification is increasingly shaping the socio-spatial fabric of neighborhoods in Western cities in Europe and America alike. The process instigates socio-
8 economic change, but this is not limited to the residential areas alone. A new niche within gentrification literature focuses on the role of business premises in the process, and outcome, of residential gentrification. These ‘third spaces’ can potentially play a role in forming amenities upon which household residential patterns are based, as well as providing novel consumption spaces. This research maps the effect of residential gentrification on business premises, through a quantitative multiple case study (N=4) of four Amsterdam neighborhoods that show various degrees of residential gentrification. It researches to what extent gentrification affects the daily goods, retail, food and beverages, and service supply. For each category, a frequency analysis is conducted, tested through a Spearman’s correlation test, and the distribution is shown geographically. For daily goods and leisure, extra analysis is conducted for in-category differentiation. We find that in Amsterdam, neighborhoods that are gentrified show concentrations of wine-stores, deli’s, secondhand clothing, massage parlors, and a decrease of grillrooms. In the selected gentrified neighborhoods, we see that there are more biological supermarkets, high-end gym establishments and popular cafés and restaurants, but we remain unable to test this on the city-level. Our conclusions indicate that residential gentrification does share a relationship with the total commercial supply of Amsterdam.
1. Introduction Residential gentrification, ‘the back-to-the-city movement’ or ‘the renaissance of the city’ is perhaps one of the most shaping forces behind the structure and character of contemporary Western cities (Doucet, 2013). The socio-economic process by which residential areas undergo economic upgrading and concurrent societal change has transformed urban inner-city neighborhoods that were characterized by a downturn into vibrant and popular places. Areas of economic disinvestment become sites of economic reinvestment, resulting in ‘upgraded’ housing stock in economic terms. Concurrently, a societal change occurs within the residents of these areas. De Pijp, a neighborhood within the urban area of Amsterdam, is a prime example of such a transformation which has changed from a no-go zone to a robust economic area within Amsterdam with many shops, restaurants, cafés and service providers being popular amongst mostly younger, higher-educated inhabitants of the city. It signifies a broader process however, by which urban neighborhoods previously deemed unworthy of living undergo a specific type of urban ‘renaissance’. The residential aspect of this development, with particular focus upon the societal changes through displacement and replacement of the original inhabitants, has been studied intensively in gentrification literature. Now scholars turn to other niches of gentrification that have a
9 part in both the process and the outcome of the development. One of such a niche is commercial gentrification that shifts focus upon the role of business premises within the process of gentrification.
Commercial spaces and business premises play a sustaining role within the urban structure and peripheral areas alike. It sustains daily necessities through supermarkets and pharmacies, offers consumer services such as clothing reparation or hairdressers, enables buying other products such as clothing and household goods, and provides consumption spaces destined for social interaction in the form of food and beverages establishments. These commercial spaces and the variety of services and consumer goods they provide might be considered to be a critical urban amenity, which are increasingly crucial within metropolitan areas as these are also centers of consumption, besides being a center of production (Glaeser, Kolko & Saiz, 2001). Other amenities include aesthetics and physical setting, good public services and speed which together provide an interesting theory for why the demand for living in cities has risen – noticeably from the increasing price of inner-city areas - beyond simple explanations of production. Commercial spaces as amenities supply the needs of the metropolitan inhabitants. Yet it remains challenging to make long-term assessments of the commercial space, especially when it comes to commercial dynamics by which some business premises leave or locate commercial property in specific neighborhoods. Research remains mainly limited to single time-points, which has resulted in the absence of analyses that aim to discern to the specific trend of commercial space dynamics on the neighborhood level, except for trend analysis. Seeing commercial spaces as a type of urban amenities forms a bridge between the residential and commercial gentrification processes.
Commercial gentrification, or “the gentrification of business premises, which leads to consumption spaces for the middle-classes, even if this group does not represent the entire neighborhood” is a niche within gentrification literature that shifts focus towards the role of commercial space within both the process and the outcome of residential gentrification (Bridge & Dowling, 2001; Lees et al., 2008; Ernst & Doucet, 2013). These business premises concern the “mixture of shops, restaurants, and services
10 that attract people to, and surround, the lifestyle of the gentrifiers” (Bridge & Dowling, 2001). Borrowing from residential gentrification literature, a distinction is possible between two types of business premises: (1) those that serve the original populace and (2) ‘upgraded’ premises that serve the material and social needs of the gentrifiers. Similar to the amenity’s perspective, commercial gentrification literature also acknowledges that gentrification is not solely a process of production (residential gentrification and workplaces), but also a story of consumption. After all, in the post- modern capitalist economy, consuming certain types of products and services play a role in the construction of identity (Bridge & Dowling, 2001). If commercial spaces are seen as urban amenities, they play a role within the process of residential gentrification as they can form a reason for the popularity of a specific metropolitan area for the incoming gentrifying populace. Through residential gentrification and the societal change accompanied by changing consumption practices, the commercial space might also be the subject of cultural, economic, and societal change. As an outcome, the commercial space and business premises would then show adaption to these now consumption practices that might become apparent from different compositions of the retail landscape. Commercial gentrification is, therefore, similar to residential gentrification, a narrative of various types of change and ‘upgrading’.
The observation that business premises tend to change through the gentrification induced socio-economic developments in geographic areas remains largely undisputed in commercial gentrification literature. Bridge & Downling (2001) were among the first to incorporate a focus upon the retail landscape as a form of consumption that was mostly absent in the debate of gentrification. They conduct a study of four Melbourne neighborhoods that show different “micro-geographies of retailing and gentrification” (2001). Zukin (2009) incorporates a focus upon the change in retail with a shift from ‘local retail capital’ to multinational and chain-forming within gentrified streets. Meltzler (2015) adds on this and researches the effect of gentrification on small-town businesses, finding that some cases illustrate retention while others show disruption. Since then, more recent publications concern the role of pubs in spaces of gentrification (Ernst & Doucet, 2013), the increase of family-related consumption spaces (Karsten, 2013), the role and use of local specialty stores and their effect on the
11 quality of the living environment (Verwaaijen, 2013). This literature offers a plethora of new, mainly qualitative, insights into the processes associated with commercial gentrification and helps broaden understanding of how and why these developments occur. It seems that a quantitative perspective is as of yet lacking in commercial gentrification literature, however.
Research on change and trends within the commercial structure of metropolitan areas remains limited mainly to broad trend analyses, which compose the meta level. This meta-focus is partially due to the absence of accurate data on the commercial space that enables taking a more in-depth focus upon changes on the micro level. This micro-level is where the societal and economic change manifests itself within residential gentrification. Gentrification occurs on the individual ground level, with economic reinvestment in property and subsequent societal change in the composition of households. In that sense, gentrification on the broader level remains a sum of its parts. The processes of gentrification and an analysis thereof is best studied on the level of small geographically bounded neighborhoods, streets, postal codes, or wards. Essential to the process of gentrification, and a question to which no definitive answer has as of yet been formulated is why some neighborhoods gentrify while others do not. Differentiation amongst urban inner city-area is, in this case, more rule than the exception within gentrification processes. Subsequently, as Bridge & Dowling (2001) also argue in their seminal work on commercial gentrification by showing the role of micro-geographies, the change and trends within commercial gentrification should also be studied on the micro-level of individual business premises and neighborhoods. Similar to how residential gentrification is a narrative of change on the household level, commercial gentrification is the same but on the business premises level.
This research contributes to the commercial gentrification literature by providing an empirical, quantitative, micro-level analysis of the effect of residential gentrification on the commercial structure in Amsterdam. We aim to answer the question “how does residential gentrification affect the daily, retail, leisure, and services supply in Amsterdam neighborhoods that show various degrees of gentrification dynamics?”. This is done through a mixed-methods multiple case study on four Amsterdam
12 neighborhoods that show various degrees of gentrification, displayed in figure 1. These neighborhoods are selected on showing various degrees of gentrification that allows for a comparison between the cases: De Pijp (gentrified), Rivierenbuurt (gentrified), Bos & Lommer (gentrifying) and Osdorp (no-gentrification). Data to analyze this has been provided by the Municipality of Amsterdam, the Central Bureau for Statistics (CBS), and retail data-specialist Locatus.
Figure 1: Map of Included Neighborhoods. Source: QGis, Municipality of Amsterdam.
The structure of this thesis follows a deductive approach. In chapter two, literature that is concerned with the financialization of property, the financialization of retail, residential gentrification, and commercial gentrification. Based on this literature, criteria of residential gentrification are established and expectations as to how they might affect the total retail supply in areas discussed. A conceptual framework is constructed, which is discussed in chapter three, amongst other methodological choices as well as formulating hypotheses that we test. In chapter four, the question to what extent residential gentrification is present in the four selected cases is discussed leading to a categorization of the various degrees of gentrification. The main research question composes of the effect of residential gentrification on four categories of the total retail supply: daily stores, retail stores, leisure establishments, and service stores. For each of these categories, the effect of residential gentrification is assessed in the
13 results section: chapter five to nine. In chapter ten, the results are reiterated the results from the conducted analysis and conclude the effect of residential gentrification on the various aspects of the retail supply. Chapter seven provides recommendations and directions for future research into the subject of commercial gentrification.
2. Theoretical Framework The question that is addressed within this research is related to spatially bounded retail spaces, and the changes of and within these spaces through processes of gentrification. To be able to answer to what extent gentrification has affected the
14 retail, food and beverage and services supply within specific neighborhoods, a framework is required that outlines various aspects. First, literature about the development, financialization, and professionalization of all types of commercial amenities is described to provide the broader context and account for the potential effects resulting from it. This describes the broader developments that have occurred within the various commercial markets and how the property market on which it is based has developed. Second, the Dutch method of planning and steering commercial spaces is discussed, which is primarily based on modern adaptations of the influential location’s theory of Christaller (1933). Third, literature concerning residential gentrification and displacement is disseminated to obtain a working definition of the concept. Such literature is focused on how and why these processes occur and why neighborhoods undergo change. This is followed by a literature review on the novel research and methodologies of commercial gentrification, as this allows a framework through which we can understand and research commercial space gentrification that also signifies the importance of the commercial space within residential neighborhoods. Before concluding the framework, the current state of gentrification within Amsterdam and the role of the state and municipality within it is provided. Together these insights provide the grander framework from which we aim to approach and research the changes within commercial spaces in Amsterdam.
2.1. Context: Financialization Both residential and commercial property markets are increasingly becoming financialized and thus gaining interest from both national and international investors. Since 1982, the amount of indirect real estate investments by Dutch institutional investors rose from being almost non-existent towards roughly 72 billion euros in 2012, a 17-fold increase (Van Loon & Aalbers, 2017:228). This is a relatively new development, as the Dutch property market historically was primarily dominated by large national ‘players’. The Dutch property market, similar to others, however, now shows a movement by which real estate is considered by investors to be just another investment asset that has liquidity. However, this is not entirely the case, as real estate’s asset categorization is limited by two reasons. One is that investments in real estate are spatially fixed. This is most likely due to the belief that real estate property
15 can be disinvested and invested in easily, which might only be the case for derivatives of these assets (Van Loon & Aalbers, 2017:234). Secondly, “real estate increasingly becomes the spatial fix for the over-accumulation of capital. It is one of the contradictions of capital that in order to find a fix, in the sense of a ‘solution’ to over- accumulation, real estate, the ‘spatial’ fix, needs to be treated as if it is not spatially fixed but liquid” (Van Loon & Aalbers, 2017:234). The authors note that this is problematic, as the intrinsic value of buildings and their inhabitants disappears in favor of measuring success through profits and increases in value (Van Loon & Aalbers, 2017:234). And as capital enters the ‘stage’ of the property markets, it also becomes susceptible to economic growth and downturn and it thus becomes speculative. Regarding both residential and commercial real estate increasingly as an investment asset, partially explains why increasingly investments occur within the property markets worldwide.
Increasingly, foreign investments are also able to find Amsterdam as a suited city to invest funds in. The property market in Amsterdam, as well as of other major cities on the global scale, is booming which is why foreign investors are increasingly interested in such cities for doing investments. Within Amsterdam, this phenomenon of increasing foreign investments is relatively new, leading Floor Milikowski to ponder the question ‘who owns the city’ in her influential book trying to explain recent issues within Amsterdam regarding the housing and property market. Medio 2012 was the turning point where the number of foreign investments caught up and overtook the number of national investments (PBL, 2016). In the period between 2009 and 2015, foreign investments increased to roughly 50% of all national property transactions (PBL, 2016). This is not strange as currently, Amsterdam ranks as 9th regarding the attractivity of the property investment markets due to a stable political-economy, relatively cheap property and a good perspective. (PWC, Milikowski, 2018:221). The affordability of property however is declining as investors increasingly find Amsterdam as a suitable investment. On the national level, investments in commercial property rose with 40% between 2016 and 2017. The retail market attracted 20%. For both of these markets, half of the investments are being done by foreign investors. More than 75% of these are located in the US, UK, Germany and France (NVM, 2017). Generally speaking, retail is not the category that attracts the most investments. This is primarily
16 office space (43%), followed by industrial/logistic spaces (14%), residential property (12%) and last retail (9%) (PBL, 2016). Most foreign investments however, roughly 30%, of the 50%, is situated within Amsterdam.
Both the financialization of the property market, the residential property market and the increased foreign investment seem to be the result of a new national policy that effectively promoted these types of investments within the Netherlands. After the crisis, at the time Minister Stef Blok of the department of housing strongly restructured the Dutch housing and property markets through a series of policies. With the ‘Woningwet 2015’, the minister aimed to reduce the social housing corporations back to their core task: that of persons with a low income being able to find suited and affordable housing, rather than adopting speculative projects beyond this (Rijksoverheid, 2015). Much of the social housing stock, especially in Amsterdam, has been sold off and therefore liquidated through selling to private investors in order to recover some debt that was accumulated during the financial crisis of 2008 (Hochstenbach et al, 2018). Furthermore, the policy incorporated more space for the market and less risk within the buying sector of the housing market (FD, 2017). Minister Blok advertised the Dutch property market on international property investment conferences for its stability, affordability and the shortage of the housing market (Milikowski, 2018:221). For his reforms in the housing market, the minister could use foreign investments (FD, 2017). In sum, the recent housing and real estate market policies spearheaded by a liberal minister (VVD) was aimed at internationally generating attention for the Dutch property market, easing restrictions for investments, and generating more foreign investments (Hochstenbach et al, 2018). Illustrative of this is the English version of the Dutch website of the Ministry of Internal Affairs, marketing the rental sector in the Netherlands as an ‘excellent investment’ (Hochstenbach et al, 2018).
17 2.2. Developments and Challenges of Retail within the Netherlands Retail spaces and property within the Netherlands are subject to several challenges that affect their spatial distribution. As consumer behavior is constantly changing, this significantly affects retail spaces which are therefore also subject to rapid change (Platform 31, 2014:3). This is also one of the reasons residential and commercial gentrification share a relationship, as through residential gentrification the population changes that brings with it a shift in consumer behavior of residents that affect the commercial spaces. In order to isolate the effect of residential gentrification on the retail landscape, an understanding of what other broader economic and consumer behavior changes have occurred and are expected to occur is necessary. In the publication Winkelgebied van de Toekomst of Platform31, eight challenges of the retail landscape are discussed and what their implications are for the shopping streets in Dutch municipalities. These are displayed in table 1. This table explains vacancy rates, increased chain forming of brands and stores, changes in zoning functions, increased attractiveness of urban areas, and new F&B concepts.
Challenge Relationship Impact on Shopping Areas Economic Retail developments move according to The bankruptcy of stores and Stagnation economic growth. The revenue of shops chains, resulting in vacant follows the growth and decline of the commercial spaces. economy and especially the native consumption statistics. Revenue-shifts in Retail revenue has declined with 9% from Increased chain brands and stores, Offline and Online 2008-2013. Shifts have occurred between especially in A1, A2 locations. Brands food and non-food shops, between Vacant buildings are assigned new chains, and between offline and online functions, such as F&B, pop-up stores. stores and small-scale services In international perspective, Dutch online retail purchases are relatively low. Increased Store Not autonomous but result of other Increased vacancy rates. Vacancy developments and among more mature markets. Since 2006 vacancy rates are increasing, which affects the functionality of shopping streets. Digitalization of The rise of online shopping is not Will affect orientation and Society autonomous, but part of the ongoing purchasing processes of digitalization of society. This entails consumers. Travel agencies, photo technological developments such as shops, music stores, video rentals usage of mobile internet and the creation and consumer electronic stores of big databases. have disappeared through this. Demographic Increased changes in age distribution, Presence of yuppies increases the Transitions knowledge drain in peripheral areas, demand for neighborhood facilities
18 households and inhabitants. In the such as public schools, playing ‘Randstad’, increasing growth of gardens, F&B and retail (Karsten, inhabitants. In Amsterdam, increasing 2014). capital powerful households defined as ‘yuppies’. Comeback of the Instead of focusing on the outer areas of Will positively affect retail in bigger Inner-city the city, increasingly developments occur urban areas (type ‘binnenstad’). For within existing urban areas. Residential smaller and more peripheral areas, public transport areas and inner- shopping areas it will become city industrial areas are being harder to compete. redeveloped. Developments in Increases in F&B supply, in contrast to a Demand for bigger surfaces for F&B decline in selling points of retail. Decline F&B in nightly shops such as clubs and bars, New F&B concepts that require but increase in day-shops (coffee fluid zoning plans. concepts, lunchrooms, ice-cream parlors). In areas with a decrease and/or ‘greying’ of the population, the amount of F&B is declining. Elderly are less likely to spend much in F&B. Table 1: Retail Challenges and their Impact on Shopping Areas. Source: Platform 31, 2014.
Similar to nation-wide trends, retail spaces within Amsterdam and their composition are largely being determined by the consumption practices of inhabitants. Within the developments of retail spaces within the city, a distinction can be made between the supply and demand side. Regarding the former, there are five relevant trends; (1) shops in Amsterdam show characteristics of scaling-up as the absolute amount decreases but the store surface increases, (2) vacancy rates have declined from 4,4% to 3,6% with the lowest rates in the Centrum district and the highest in the Zuid-Oost area, (3) some areas show pressure on the retail space rental prices, whilst others show increase, (4) the added value and revenue of retail has increased with 11% between 2013-2016, (5) the amount of jobs has increased with 7% between 2014-2016, and (6) there is a strong increase in tourist-oriented shops within the Centrum district. The development of rent of retail spaces between 2005-2018, development three, is displayed in figure 2. This distribution and change in the rent of retail spaces shows that most increases concentrate within the inner-ringroad areas, whilst decreases or no-change is mostly observed outside of this boundary.
Regarding the demand-side of retail, another trend is visible regarding ‘koopkrachtbinding’, or the binding of purchasing power. This is an instrument of
19 measuring the extent to which inhabitants do their purchases in their respective neighborhood, ward or city-region. This shows that: (1) koopkrachtbinding of non- daily purchases has declined from 78 to 70% with an increase in online from 11% to 20%, and (2) that the koopkrachtbinding of daily purchases has remained constant with 95%. (Municipality of Amsterdam, 2016). These conclusions show even with the increase of online shopping, the number of inhabitants purchasing within their own neighborhoods and city-regions is high. This in turn means that the social composition of neighborhoods is important for the purchases done within the retail landscape.
Figure 2: Development of Retail Space Prices in Amsterdam between 2005 and 2018. Source: Cushman & Wakefield, 2018.
The development of ‘horeca’ within Amsterdam, or F&B, show two important developments: (1) there is an increase city-wide in the number of F&B establishments and (2) the overall revenue of these establishments is increasing. Between the eight- year period of 2010-2018, the number of F&B establishments in Amsterdam increased with 44,9% to 5760 in total (BBGA, 2018). This includes hotels, restaurants, cafés and other F&B establishments. Simultaneously, between the period of 2011 and 2017 the
20 revenue index of F&B increased to 129,6 showing a steady increase especially from 2014 onward. The revenue index of Amsterdam based retail increased to 116,9, showing a dip up until and including 2013 but afterwards showing increases (CBS, 2017). This development is displayed in figure 3. With regards to the increase in F&B and their spatial distribution, Van der Groep (2016) notes how: “the extra F&B establishments have located themselves primarily within the 19th-century belt (+140 establishments) and the outer peripheral areas (+100). This fits with the image that neighborhoods such as the Westerpark, Oud-West, de Pijp and others have undergone a process of upgrading. The new inhabitants within these neighborhoods bring with them new lifestyle- and consumption patterns. (…) Where F&B and other novel shopping formulas thrive on these income-powerful inhabitants, this is not the case for older shops such as clothing and appliances stores. This is because a large share of the social and cultural capital is generated in the F&B, whilst a large share of the financial capital is being spent in online stores”. F&B is therefore highly interesting for analyzing commercial gentrification, as the decline of stores and the increase of F&B in specific neighborhoods might signify processes of gentrification.
Development Revenue-index F&B and Retail 130
120
110
100
90 2011 2012 2013 2014 2015 2016 2017
F&B Retail
Figure 3: Development of the Revenue-index of F&B and Retail in Amsterdam. Source: CBS, 2017.
2.3. Location Theory and its Role in Retail Planning The retail landscape and its spatial structure within the Netherlands, and the city of Amsterdam in particular, is largely planned on the concept of functional hierarchy that
21 is derived from the central place theory by Kristoffer Christaller in 1933 (Evers et al, 2005; Evers et al, 2011). With this influential theory, Christaller aimed to describe and explain the distribution of settlements in the southern part of Germany. However, this theory also has explanatory power for describing the distribution of retail landscapes. Naturally, some locations will emerge that have a central position and a large catchment area. Within this area, smaller locations and smaller catchment areas will emerge that service a smaller geographical area. This leads to a natural emerging hierarchy between these areas that resembles a beehive, displayed in figure 4 (Evers at al, 2011:58-60). Whilst initially only devised to describe reality, this theory was quickly within the Dutch retail planning tradition (Evers et al, 2011:59). Such a hierarchical structure is also evident within the city of Amsterdam, where the inner-city is the central location with several supporting hierarchical subordinate areas. The inner-city of Amsterdam is the central location, with various types of other supporting areas surrounding it that have different catchment areas for neighborhoods. Roughly all neighborhoods that we are interested in are either a ‘binnenstedelijke winkelstraat’ (De Pijp, Rivierenbuurt, Bos & Lommer) or ‘wijkcentrum klein’ (Osdorp). More on this later in the description of our cases. On the basis of this theory, a typology of commercial areas on size and function is possible that incorporates the hierarchical aspect. This typology is displayed in Appendix 2.3a. It shows by which standard shopping areas can be categorized according to this, as well as the nation-wide average of chains that are located there, to which we can compare the shopping areas in our cases.
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Figure 4: Central Places Theory. Source: Adaptation by Evers et al 2005; of Christaller, 1933.
2.4. Residential Gentrification Residential gentrification is a socio-spatial process by which neighborhoods undergo significant spatial, economic, and social restructuring (Sassen, 1991:225). Much research has gone into the causes, processes and outcomes of gentrification. Ruth Glass first coined the term when she described the “the movement of the gentry, affluent inhabitants, from the suburbs back to the city” (Glass, 1964:xviii-xix; Taggart, 2009). Other scholars have labeled gentrification a process of the “renaissance of the city” (Karsten, 2013). In practice, gentrification is the renewed interest in specific disinvested neighborhoods that receive economic stimulation through reinvestment, that results in a renewed attractiveness of the neighborhood through which there is an influx of new inhabitants and an outflux of the original populace. Gentrification is thus a simultaneous process of economic revitalization and societal change in a spatially bounded neighborhood. Both the economic and the societal change that occurs in gentrifying neighborhoods is limited to an upwards movement, by which buildings and rents significantly increase in value and the original populace is replaced by those with higher incomes and education levels (Boterman, 2016). In practice, gentrification is characterized but not limited to by four types of phenomena (Urban Displacement, 2019):
23 I. Real estate speculation, with investors flipping properties for large properties, high-end development, and landlords looking for higher- paying tenants (Smith, 1987). II. Increased investments in neighborhood amenities, like transit and parks (Zuk et al, 2017). III. Changes in land use, such as industrial to restaurants and storefronts (Hamnett & Whitelegg, 2007). IV. Changes in the character of the neighborhood, as community-run businesses are replaced by business catering to new residents (Chapple & Loukaitou-Sideris, 2017; Zukin et al, 2009).
There are structural and material explanations for gentrification. Structural explanations focus on the logic underpinning gentrification that emphasize the broader political economy. These explanations place emphasis on in what conditions gentrification might occur. A material explanation however “places stronger emphasis on how gentrification processes unfold in space and over time, highlighting how different population groups shape and are shaped by gentrification” (Abbot, 2004; Hochstenbach, 2017:16). This approach places importance on issues such as “distinctive consumption practices, the value attached to residential spaces with an ‘authentic’ appeal, time-space management, and the preference to live amongst peers with similar consumption patterns” (Hochstenbach, 2017:17). This research is such an approach, as it is directly aimed at describing and explaining how residential gentrification processes have shaped the retail landscape in specific neighborhoods within Amsterdam, in order to distill the potential relationship between the two.
2.4.1. The Economic Aspect of Gentrification: Economic Revitalization Through economic reinvestment, neighborhoods often are revitalized through which buildings, public space, and neighborhood amenities are improved. The economic aspect of gentrification is explained by the rent-gap theory (Smith, 1987). This theory states that when the gap between potential rent when a building or area is upgraded and the current capitalized rent increases over time, this will spur investors to do these economic investments as to increase their capitalized rent over time (Smith, 1987). It is
24 therefore argued that gentrification is the result of a rule-free housing and land market (Smith, 2007). This theory is displayed in figure 5. Through these reinvestments, the value of the property and land increases which allows for higher rents than previously possible due to the state of the asset.
Figure 5: Rent Gap Theory. Source: Smith, 1987.
2.4.2. The Social Aspect of Gentrification: Displacement Gentrification is a janus-faced concept, as in contrast to the economic revitalization and neighborhood improvement is a societal change that can result in the displacement of the original populace that is often characterized as the downside of the process. Similar to gentrification itself, much research has gone into the social implications for spatial areas resulting from the process of economic revitalization. Many authors have placed emphasis on the negative consequences for primarily lower income and minority residents due to displacement (Sakizlioglu & Uitermark, 2014). The reinvestment in neighborhoods and economic revitalization results in land- or property-owners being able to increase their rents and property values up to their prospects as displayed in figure 5. The economic investment thus directly results in an increase of building value and accordingly the rent that is asked from tenants. The downside of this is that ‘current residents are forced to move because they can no longer afford to reside in the gentrifying neighborhoods’ (Freeman, 2005). This results in the displacement, primarily of working-class residents (Slater, 2006:740). Freeman (2005) adds to this that in-movers are more important than out-movers for the process as “people are likely to be more sensitive to neighborhood characteristics when choosing what neighborhood to move into rather than whether they should move at
25 all”. This process of displacement might be considered as the negative consequences of gentrification, with the economic improvement in neighborhood structure being the positive consequence.
Gentrifiers differ from the original populace in neighborhoods through several characteristics: (1) they generally are younger often under 40 years old, (2) they have consumption-oriented lifestyles, (3) ignoring gender gentrifiers relatively occupy professional-managerial occupations with high incomes, (4) they are highly educated with most having one university degree, (5) they are often unmarried and have no children and (6) they move into low-income housing in gentrifying neighborhoods which they renovate (Verwaaijen, 2013). Bridge & Dowling (2001) state how buying commodities is increasingly determinative for the identity that people have. Retailing plays an important role in consumption practices and reserves a role within the gentrification process. Specialty stores seem to reflect lifestyles and identities of gentrifiers (Bridge & Dowling, 2001). Especially through characteristics one, three and four the displacement resulting from residential gentrification can be seen in the societal structure of a neighborhood.
2.4.3. The Role of various levels of Government Public bodies that possess planning authority, in the case of Amsterdam the municipality, might strive and assist gentrification processes through various policies. Within Amsterdam, policymakers explicitly discuss gentrification as being a positive policy instrument that might assist with revitalizing specific neighborhoods (Hochstenbach, 2017:36). Some authors provide a more radical account of the process and conceptualize gentrification in the case of Amsterdam as “a means through which governmental organizations and their partners lure the middle classes into disadvantaged areas with the purpose of civilizing and controlling these neighborhoods” (Hochstenbach, 2017:12). Authorities can and do play an important role in the process, through various policy options. Some of these include (1) subsidizing middle class amenities, (2) upgrading or privatizing public space and (3) housing policies (Hochstenbach, 2017:21-22). The latter, housing policies, primarily occur through housing liberalization: the process of selling social or public housing
26 accompanied with regulatory reforms that are aimed to facilitate gentrification in specific authority-selected neighborhoods (Hochstenbach, 2017:21-22).
2.4.4. Residential Gentrification in Amsterdam Residential gentrification in Amsterdam occurs roughly alongside the boundary between Amsterdam’s gentrifying central city (Centre, East, West and South boroughs) and the urban peripheral boroughs (North, New-West and Southeast) Hochstenbach, 2017:42). This corresponds to a certain extent to the ring road A10 that runs through Amsterdam (Milikowski, 2018). A typology of neighborhoods characteristics and how this relates to gentrification processes might be classified alongside the distinction of downgrading and upgrading. Downgrading is when the growth of income level or real estate value is a standard deviation below from the average city level (Teernstra & Van Gent, 2012). Upgrading is a similar development, but with upwards growth in income level or real estate value. Within Amsterdam, high-status upgrading neighborhoods are located in the city centre and southern boroughs. Low-status upgrading is found in the nineteenth-century belt around the central city. Downgrading is found in the outer-ring neighbourhoods (cf. Hochstenbach, 2017). This division is displayed in figure 7.
Figure 6: Neighborhoods of Amsterdam classified alongside axis of upgrading. Source: Hochstenbach, 2017.
27 2.4.5. Operationalizing Gentrification: Three Types of Indicators Being a socio-economic spatial process of change, residential gentrification can be measured through three types of indicators: social, economic, and physical. A gentrification label might be applied when “the temporal change in a series of variables increases concurrently” (Tierney & Petty, 2018:452). As residential gentrification is a process of change, gentrification manifests itself in the developments within these indicators. Social indicators include age, ethnicity, and education level. Age is “often implicitly acknowledged in gentrification processes” and Hochstenbach & Boterman argue for the “emergence of gentrification as a multi-generational process” (2018:1). Different age cohorts (young people, families, and older people) are associated with different forms of gentrification and life-course transitions (studentification, rental gentrification, family gentrification and rural gentrification) (Hochstenbach & Boterman, 2018). The second social indicator is ethnicity, of which the relationship it has with gentrification remains ambiguous. Within literature, gentrification has often been described as a process whereby “white gentrifiers displace black and non-white ethnic minority populations” (Lees, 2016). Such a view however is stereotypical and surpasses nuances and the role of black and non-white ethnic minorities as agents of gentrification themselves (Lees, 2016:208). Moreover, the relationship between both is different in the Western European contexts. In the Dutch context, an interesting indicator that relates to gentrification is the share of “new city-dwellers” that each neighborhood has. This indicator measures the number of inhabitants with a Dutch or Western migration background (not including Eastern-Europe), aged 18 to 54, that have first registered within the municipality of Amsterdam after their 18th birthday (BBGA, 2018). Being highly selective, this measure is associated with gentrification which becomes clear from policy documents viewing higher shares of this population as positive for a neighborhood (DirkMJK, 2015). Education level has been and might be applied as surrogate for ‘class’, and gentrifiers typically have higher education levels than those whom they displace (Smith, 1987). Education level is therefore widely used as an indicator in analyses of gentrification processes (Wyly & Hammel, 2004; Freeman, 2006; Martin, n.d.)
28 Economic characteristics are perhaps mostly used within empirical studies of residential gentrification as they are indicative of economic revitalization and include value and income. Economic indicators can provide the nearest approximation of gentrification through statistics (Martin, n.d.). Dwelling values are indicative of reinvestment, attractiveness, and demand for houses in the neighborhoods they are located in. As neighborhoods gentrify, gaining attractiveness and demand, housing prices often increase. The second economic indicator is income and to some extent forms a proxy for economic class. Neighborhoods that gentrify often increase in value and through the process of displacement, higher-income replaces lower-income inhabitants. In synthesis, both indicators have been used for a typology of ‘upgrading’ and ‘downgrading’ neighborhoods, shown in figure 7 (Hochstenbach & van Gent, 2014; Hochstenbach, 2017).
Relating to economic revitalization of neighborhoods are physical indicators. These include tenure shifts and increasing or upgrading existing housing stock. Increases in the housing stock might be indicative of ‘new-buit-gentrification’, a form of gentrification that occurs through residential development (Davidson & Lees, 2009). In order for developments to qualify as such, it would entail that the new built dwellings are aimed to accommodate gentrifiers. The second physical indicator is ownership distribution and tenure shifts. On a policy level, these might be considered to be part of state-led gentrification (Hochstenbach, 2016). Historically Amsterdam had high shares of social housing within the tenure composition. Since 1990 however, urban policies that favor homeownership at the cost of social-renting have been a structural force, and often promoting gentrification (Hochstenbach, 2016). Especially in the more centrally located boroughs, housing-association dwellings are in decline (Hochstenbach, 2016).
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Gentrification Variables Indicators - Age Social Social - Ethnicity Displacement - Education Level - Dwelling Value Economic Economic - Income Revitalization - Building Developments/Upgrading Physical - Tenure Shifts Table 2: Variables and Indicators of Gentrification.
2.5. Commercial Gentrification Commercial gentrification is gaining novel scholarly attention and research into it is aimed at understanding the role of retail landscapes within both the process and outcome of residential gentrification (Bridge & Dowling, 2001; Zukin et al, 2009; Karsten, 2013; Ernst & Doucet, 2013; Hochstenbach, 2017). Commercial gentrification research recognizes that as a neighborhood gentrifies, the change is not solely limited to the residential aspects but also exerts change on the commercial structure (Ernst, 2013). Slater (2006:738) captures this shift nicely by signaling how gentrification is no longer only about residential aspects such as rent increases, landlord harassment and working-class displacement, but increasingly about commercial aspects such as trendy bars and cafés, social diversity and clothing outlets. The commercial and public spaces in a neighborhood form the so-called ‘third spaces’ in neighborhoods besides respectively the social environments of the home and work (Oldenburg, 1989; 1991). Such places are of significant importance to neighborhoods as much of life besides living and working occurs here which generates much of the social and cultural capital of areas (Oldenburg, 1991). The commercial space within neighborhoods are therefore strongly connected to the residential aspects, and local businesses are tied to their communities in physical, economic and cultural ways (Meltzler, 2015). Bridge & Dowling (2001) have shown how the structure of micro retail landscapes are of importance as they show what types of amenities are situated in specific locations and how this reflects demographic characteristics. Empirical evidence confirms that as demographics of an area change, so do the business that serve and operate within it (Metlzler, 2016). Commercial spaces are important as not only is gentrification a process of production (housing, amenities) but also consumption by the novel
30 populace. More affluent users and newcomers need new consumption spaces to supply the material needs, as well as also generating social and cultural capital (Bridge & Dowling, 2001:93; Zukin, 2009:47). Commercial gentrification is in this case a representation of the lifestyle and value of its users, the new populace (Bridge & Dowling, 2001; Zukin, 2009; Ernst & Doucet, 2013).
Commercial gentrification literature therefore acknowledges that the retail landscape takes up a central component within both the process and outcome and might be considered a niche within the broader residential gentrification literature. The retail landscape is a term used to describe the “mixture of shops, restaurants and services that attract people to, and surround, the lifestyle of the gentrifiers” (Bridge & Dowling, 2001). In the process, the retail landscape might act as a catalyzer through offering or upgrading their stores that is catered to providing amenities for the new residents. In the outcome, it is then expected that as neighborhoods are fully gentrified and have undergone socio-economic transformation, the stores will reflect this and the lifestyles of the new inhabitants. Thus as the demographics of an area change through residential gentrification, this will also affect the retail landscape in various ways which has often been labelled as commercial gentrification which is “the gentrification of business premises, which leads to consumption spaces for the middle-classes, even if this group does not represent the entire neighborhood” (Bridge & Dowling, 2001; Lees et al, 2008; Ernst & Doucet, 2013). It signifies the change and the upgrading of the commercial space to provide for the new social class that has manifested itself within a given neighborhood. It is about the change from a café to a coffee-corner, the cheap dining restaurants to delis and foreign cuisines, and the change from independent local cornerstones to chains.
As commercial gentrification is a novel field of study, it does not yet provide a thorough or refined methodological and theoretical that this study can borrow from. Nonetheless, reviewing what is already known about the subject and how we might approach our study assists in providing a more detailed account. The first of these is provided by Bridge & Dowling (2001), whom highlight the importance of ‘microgeographies’ of retailing and gentrification. Such geographies represent
31 “increasingly localized consumption landscapes in terms of the production relations of retailing”. In their Australian based research, they focus on the retailing fabric of four inner Sydney neighborhoods. In order to do so, they have provided selected socio- economic characteristics of the neighborhoods, documented the retail establishments in these cases, and classified term along type of service of retail that is provided. In the second part, they have gathered information on the menu details, food styles, shop names, advertising strategies and architectural styles to gather more insight into the symbolic dimension of commercial gentrification. Through doing so, they have been able to compare these microgreographies to the consumption practices of gentrifiers.
2.6.1. Operationalizing Commercial Gentrification The economic and societal change that is initiated by residential gentrification is expected to share a relationship with commercial gentrification. Especially the influx of new inhabitants, which are often richer and higher-educated, is likely to facilitate the need for new consumption spaces in the commercial fabric. This societal change in the neighborhood composition spurred by residential gentrification affects the commercial fabric, leading to “upgrading”. Commercial gentrification has been defined as “the gentrification of business premises, which leads to consumption spaces for the middle- classes, even if this group does not represent the entire neighborhood”. As of yet, this definition is not suited for analyses for two reasons: (1) it includes the word gentrification of which it remains unclear as to how the process of gentrification regarding business premises plays out and (2) it remains unclear what exactly defines a consumption space. A more fine-grained definition is necessary in order to be analyzed. Verwaaijen (2013) offers this through a synthesis of Zukin et al (2009) and Ernst (2011), stating that “as the result of the arrival of gentrifiers, trendy and authentic boutiques, pubs, restaurants and other leisure related supply are established (…) commercial gentrification focuses on services and facilities in the area of retail, pubs, restaurants and other leisure related supply, like gyms, cinemas and other cultural facilities” (Doucet, 2013; in Verwaaijen, 2013:3).
The consumption spaces and the change within it through residential gentrification are relevant as they cater to the demands of the gentrifiers since consumption is
32 increasingly important in the construction of identities (Bridge & Dowling, 2001; Verwaaijen, 2013). Gentrifiers differ from the original populace in neighborhoods through several distinctive characteristics: (1) they generally are younger often under 40 years old, (2) they have consumption-oriented lifestyles, (3) ignoring gender gentrifiers relatively occupy professional managerial occupations with high incomes, (4) they are highly educated with most having one university degree, (5) they are often unmarried and have no children and (6) they move into low-income housing in gentrifying neighborhoods which they renovate (Verwaaijen, 2013). Bridge & Dowling (2001) state how buying commodities is increasingly determinative for the identity that people have. Retailing plays an important role in consumption practices and reserves a role within the gentrification process. Specialty stores seem to reflect lifestyles and identities of gentrifiers (Bridge & Dowling, 2001).
A working definition that we use based on a syntheses of the theoretical framework and the above mentioned additional definitions is “the social, cultural and economic changes in the retail landscape - which includes retail, leisure, and services -, (1) changing the composition of stores, (2) that reflect new consumption patterns (3) which is reflected amongst others in price and reinvestment in amenities. The commercial space and change that occurs within it can be approached in a qualitative and a quantitative manner. The former change might manifest itself within establishments, in the upgrading to a new clientele, changing appearance and serving or delivering other ‘upgraded’ products. Measuring such a change would require a qualitative approach. The latter type of change manifests itself in the change in the absolute numbers of specific stores in the retail, services and food and beverages.
2.6.2. Where does Commercial Gentrification occur? Earlier work on commercial gentrification offers some directions as to where the process might manifest itself. This work, with their main recommendations as to the location of commercial gentrification is displayed in table 3. The first direction is that commercial gentrification likely focuses on individualized consumption, manifesting through a decline of chain-stores in favor of local independent-run stores (Bridge & Dowling 2001; Ernst & Doucet 2013; Chapple & Loukaitou-Sideris 2013; Verwaaijen,
33 2013). Karsten (2015) notes that chains might increase and so does Zukin (2009), but the latter adds to this that the increase in local specialty stores (boutiques) outpaces the increase of commercial stores. The second direction is that commercial gentrification within restaurants favor more ‘international cuisines’ (Bridge & Dowling 2001; Ernst & Doucet 2013). The expectation is that gentrified neighborhoods show relatively more restaurants and also a wider plethora of possible cuisines to eat from. The third direction we deduct is that leisure related establishments (pubs, bars, café’s, but also fitness and yoga) establishments are increasing (Bridge & Dowling 2001; Verwaaijen, 2013). Another earlier direction concerns the manifestation of ‘local specialty’ stores (Verwaaijen, 2013). In gentrified neighborhoods we expect that there are more wine stores, vintage stores or bio stores as share of the total supply.
Author Expectation Bridge & “ (…) the importance of restaurant eating and individualized rather than mass Dowling consumption (…) “ (p. 105). (2001) “ (…) the buying of Gourmet food to cook and/or reheat at home, and the importance of mind and body management (…)” (p. 105). Ernst & “(…) this is an environment characterized by small, independent ‘mom and pop’ Doucet stores, local shops, farmers markets (…)” (p. 190). (2013). “(…) gentrified commercial districts typically consists of restaurants specializing in different types of international cuisine, giving the area a more cosmopolitan feel” (p. 191). Zukin et al “strong growth in new entrepreneurial retail capital (boutiques), notable increase in (2009) corporate retail capital (chains); and deep decline in old, “local” retail stores” (p. 58). Chapple & “non-chain small business difference index: the change (…) in the share of non- Loukaitou- chain small business in the census tract. A loss of small businesses indicates Sideris commercial gentrification” (p. 1.) (2013) Karsten “the decrease in ‘ordinary’ consumption spaces and the rise in commercial chains in (2015). shopping streets (Deener 2007, Zukin 2009). The so-called convenience store is disappearing and, as a consequence, so is their supposed ‘authentic’ character” (p. 3). Verwaaijen “(…) manifested themselves in the establishment of local specialty stores (…). For (2013) instance, designer boutiques, interior design stores, wine stores, vintage stores and bio stores” (p. 1), “(…) commercial gentrification focuses on services and facilities in the area of retail, pubs, restaurants and other leisure related supply, like gyms, cinemas and other cultural facilities” p. 3. Table 3: Earlier work on commercial gentrification and where it might manifest. Source: Authors.
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3. Methods The research question concerns the relationship between residential gentrification and the retail supply. The research design that is employed is a case study that consists of four cases. Case studies are suited for questions such as that of this research as they are aimed to explore and investigate phenomena through a detailed analysis of a limited number of events or conditions and the relationships between them (Zainal, 2017:1-2). Furthermore, they aim at answering questions of why and how (Yin, 2002:23). To answer the research question, the study makes use of primarily quantitative methods. Secondary sources provided data, including the Central Bureau for Statistics (CBS), the Municipality of Amsterdam and retail-market data specialist company Locatus. From datasets that are publicly provided by the former two organizations, residential gentrification data on the neighborhood level within Amsterdam has been gathered and combined on the level of the cases. These indicators include social, economic, and physical variables such as ethnicity, age, building year, and value amongst others. These indicators aim to establish the presence of residential gentrification in the cases. Data on the economic characteristics are supplied by Locatus, which enables insight into the commercial supply of the selected neighborhoods. Locatus has collected data on the content and categorization of stores from 2005 onward. This analysis is supplemented by a review of the social media connectivity of the F&B establishments as a form of ‘qualitative' deepening of the quantitative story. In synthesis, with this data, the effect of residential gentrification is assessed on the total retail supply.
3.1. Conceptual Framework The conceptual framework that shows how this research approaches the relationship between residential and commercial gentrification is displayed in figure 7. This relationship composes of five elements. Residential gentrification consists of concurrent economic reinvestment and societal change. Economic reinvestment results in increased dwelling value and rents. The societal change mainly manifests itself in the relocation of higher incomes and higher education to neighborhoods that show residential gentrification. The economic aspect of residential gentrification
35 relates to theory on urban amenities, as these provide a logical explanation for the attractiveness of inner-city core neighborhoods as well as having shown that amenities affect the value of dwellings (Cheshire & Sheppard, 1995). The societal aspect of residential gentrification, the change within it, creates changing consumer preferences. Waldfogel has shown that the availability of food and beverage establishments strongly correlates to demographic characteristics such as ethnicity, education, and income (2008). On the other hand, commercial services might factor in the residential preferences of households that show the importance of amenities (Kolko, 2011). It illustrates the interrelationship between supply and demand for stores. Conjointly, these urban amenities and changing consumption preferences cause commercial gentrification. This type of gentrification manifests itself in external ‘upgrading' and internal ‘upgrading'. External ‘upgrading' is the change in store composition, the creation of new stores, that all cater to the new populace. It is quantitative, as it concerns a change in both absolute and relative frequencies of the commercial supply. Internal upgrading is instances of changing stock and aesthetics of existing commercial establishments to cater to the societal changes. Concurrently these developments form commercial gentrification.
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Figure 7: Conceptual Framework.
3.2. Measuring Residential and Commercial Gentrification The research question consists of an independent and dependent variable. The independent variable is residential gentrification. In the theoretical framework, this concept is operationalized as ‘a process of simultaneous economic and social upgrading, by which there is an influx of new inhabitants and an outflux of the original populace'. While the displacement resulting from gentrification is not within the scope of this research, the process of economic and social upgrading is. Economic upgrading has often been heralded as an important indicator of gentrification, especially when values and incomes within a specific neighborhood increase sharply in comparison to the national or the metropolitan statistical average (MSA) (Taggart, 2009:7; Hamnett, 1984). When the values of dwellings increase in a neighborhood, this will lead to societal change as some inhabitants are not able to cope with the increased price hikes. In general, indicators that are mostly used in the literature on and analyses of residential gentrification are (1) value of dwellings, (2) average income and (3) share of the population with higher education.
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Figure 8: Indicators of Residential and Commercial Gentrification.
Commercial gentrification is defined as "the gentrification of business premises, which leads to consumption spaces for the middle-classes, even if this group does not represent the entire neighborhood". Through economic and societal change, residential gentrification is expected to influence commercial gentrification. Especially the new inhabitants who often have more disposable income and being higher- educated, have other consumption preferences which should become visible as the neighborhood undergoes residential gentrification processes. The process of commercial gentrification is ought to manifest itself in both a quantitative and a qualitative way. The quantitative can be measured using the data provided by Locatus and concerns changes in the number of stores and their frequencies as a share of their respective categories. These developments form ‘external upgrading'. An example of such a type of upgrading includes the increase of a specific type of store as a share of their total. The qualitative method of approaching commercial gentrification concerns internal upgrading and entails the change within specific types of stores. It entails cases of existing types of stores that specifically cater to the consumption preferences of the incoming populace. Examples of these are supermarkets that sell merely biological products, fitness establishments that provide high-end personal training,
38 and the increase of food and beverage establishments that form consumption spaces for the gentrifiers. An oversight of the scope of qualitative and quantitative forms of upgrading is displayed in table 4.
Category Quantitative Change Qualitative Change Daily Goods - Biological Products - Blending (In-Store Meal Points e.d.) Retail - Authentic Fashion Frequency of Stores - High-End Retail Products Leisure - Popular Food & Beverages - High-End Leisure: Personal Training Services - Barber Shops Table 4: Quantitative and Qualitative Change in the Total Retail Supply.
3.3. Case Selection The cases that are selected for analysis consist of four Amsterdam neighborhoods, which show varying degrees of gentrification. The selection is limited to Amsterdam due to the accessibility of accurate data from Locatus that is limited to the Dutch retail landscape, as well as Amsterdam being a Dutch city that shows strong signs of gentrification dynamics. Gentrifying or gentrified neighborhoods might be classified by (1) a higher than average increasing property value, (2) a higher than average increasing median household income, (3) an increasing share of new city-dwellers, (4) a relatively higher percentage of inhabitants that has finished higher education, and (5) the upgrading of the physical housing stock. The rationale for choosing neighborhoods that show different degrees of gentrification is the fact that in multiple- case studies, cases should be selected in such a way that they either predict similar results or produce different results for predictable reasons (Yin, 2002:46). The above selection method is called literal replication and is used in this research. This selection has resulted in four cases: two of which are gentrified, one that is in the process of gentrification and a last case that shows little to no gentrification characteristics. These
39 cases are respectively de Pijp, Rivierenbuurt, Bos & Lommer, and Osdorp. An overview of the cases, their city-region, sub-neighborhoods, bureaucratic codes, and zip codes are displayed in table 5. Having two cases of gentrified neighborhoods allows for comparison for similar results. This establishes a baseline, to which the neighborhood that shows no gentrification is compared.
Case 1 2 3 4
Name De Pijp Rivierenbuurt Bos & Lommer Osdorp City- South South West New-West Region Nieuwe Pijp, Scheldebuurt, Erasmuspark, Osdorp-Midden, Neighb. Oude Pijp, Zuid- IJselbuurt, Landlust, De Osdorp-Oost, De Pijp Rijnbuurt. Kolenkit. Punt. Codes K24, K25, K26 K52, K53, K54 E37, E38, E39 F81, F82, F83
Expect. Gentrified Gentrified Gentrifying Not Gentrified Table 5: Case Selection.
3.4. Data Selection & Methods For the analysis, a dataset is constructed with various information provided by three different public and private organizations: (1) the BBGA from the municipality of Amsterdam, (2) Kerncijfers Regio's en Buurten by the CBS and (3) Retail-data provided by retail data-specialist Locatus. Variables from the BBGA include socioeconomic characteristics, variables about the physical composition, and some data about companies. Variables from the CBS include household income and the build year of the neighborhood. The information of Locatus includes data on the number of selling points (n = 9208) and store surface for our four cases on four different time points ranging from 2005 to 2019. Furthermore, it includes information on the number of selling points (n = 8037) and shop surface for each Amsterdam neighborhood (n = 97) in 2018. The retail data is categorized according to four groups: daily, retail, leisure, and services. All groups, their sub-groups and specific selling-points that are included are displayed in Appendix 3a. For each neighborhood, frequency tables are displayed to show differences between the four cases. For notable discrepancies between the cases in the retail landscape, a statistical test is performed. For the retail data, a Shapiro- Wilk test has been conducted which has shown that most of the data is distributed
40 non-normal. Therefore, to test for correlation, a Spearman's rho has been performed with the retail data and proxy gentrification variables. Spearman's Rho is a non- parametric alternative to the widely used Pearson's correlation.
Additionally, for food and beverages, additional quantitative data have been collected in the form of Instagram location mentions through a web scraping tool. Web scraping is data scraping that enables to extract data from websites that has been posted by users. In this case, Instagram has been scraped for a selection of food and beverage establishments in the four cases (n = 146). Users of this website can add their current location to their photos, including establishment names. Adding a location reference enables Instagram users to show where this picture is taken. Using Instagram location references has been used before in a study that aimed to show the ‘popular places' within Amsterdam, and can be considered "a filtering device that is a membrane over the surface of the city as it selects out certain parts of the urban landscape – the glamorous, the hip, the refined – and passes them through to users. Users serve as voluntary promotors of high-end consumption and accelerators of gentrification" (Boy & Uitermark, 2015). Instagram location references are used as a measure of ‘popularity' or ‘trendiness', supplied by the consumers that visit these establishments. For each, the number of references and the date of the first post have been gathered and added in chapter six. Dividing the number of references by the number of months since the first post allows for a measure of popularity that accounts for the time aspect. Higher numbers in such a measure indicate more popularity amongst Instagram users. To supplement this analysis, per neighborhood, photos are provided to illustrate ‘popular' as well as unpopular bars online.
New digital social media platforms such as Instagram might assist in measuring the differences amongst popularity between establishments as well as amongst which social groups these establishments are popular. This platform enables users to post photo's, add location references, tag other people, and like and comment on other photographs. It is a medium that is most popular amongst the 18-34 age cohort, which corresponds to the young character of incoming gentrifiers (Statista, 2019). Instagram use is also more prevalent amongst higher educated people, with 29% of high school
41 educated persons using it compared to 42% of college-educated individuals (Pew, 2018). This development is similar regarding the geographical area, showing that inhabitants of rural areas use Instagram less (25%) compared to those living in urban areas (42%) (Pew, 2018). Instagram use also seems to increase as household income increases as well, with 44% of those with an income under 30 thousand us dollars use the platform compared to 55-60 percent of those with an income above 70 thousand us dollars. The usage demographics of Instagram thus shows usage similar to that of the new middle-class or gentrifiers: higher-educated, relatively young, and with higher income. As such, what is visible on the platform might be accurate as to what places are favorite amongst gentrifiers and what commercial spaces are being used for consumption.
The basis for data selection was the selling-point document on the four neighborhoods of 2019. For each of these establishments, an Instagram search term was looked up manually through their website. The location for the Coffee Company on the Rijnstraat, for example, was ‘Coffee Company Rijnstraat', while that of the one on the Scheldestraat was ‘CC Scheldestraat'. As these locations are user-generated based on geolocation, some discrepancies between the establishment name and its name online exist. Searching for these locations manually strengthens the success rate of the scraping tool. Subsequently, to each location, we added the suffix ‘Amsterdam'. Adding this location guaranteed that it would look for this location in Amsterdam, rather than in other places. For establishment names as ‘Il Delfino Blu', this was necessary as such a name might be prevalent in other locations. For all 146 establishments, these search terms were added into the scraper which yielded a total of 31862 posts. If all establishments were equally popular, the expectation is that all neighborhoods should have an equal amount of posts. The scraping result produces a new spreadsheet that includes the date of the first post. This date has been added to the number of likes, calculating the number of months that the location is active on Instagram. While this is by no means an approximation of the actual existence of an establishment, it does proxy the first online Instagram interaction. Dividing the number of likes by the number of months active yields a ‘popularity grade'.
42 3.5. Hypotheses Based on earlier theory on the manifestation of commercial gentrification, the scope of the data of locates, and the additional option of a popularity analysis of F&B, five hypotheses are established through which the main research question is answered. Earlier work on commercial gentrification in various locations and by various authors form expectations and guide where to look in the data for manifestations of the concept. In table 3, these authors, their work, and their theories as to where commercial gentrification becomes most apparent have been discussed. The results from this theoretical review form hypotheses one to four. On the basis of the popularity analysis, an additional hypothesis five is established. These hypotheses relate to different aspects of the retail landscape. The hypotheses and to what aspect of the total retail supply they relate are displayed below in table 6.
H1 In gentrified neighborhoods, there are higher shares of non- Total chain stores for all types of retail. Retailscape H2 In gentrified neighborhoods, there are more ‘local specialty' Total stores such as wine-stores, vintage stores, bio-stores, and Retailscape interior design stores as a share of the total supply. H3 In gentrified neighborhoods, leisure-related establishments Leisure account for a bigger share of the total retail supply, and these are increasing. H4 In gentrified neighborhoods, the average popularity of F&B Leisure establishments is higher than that of non-gentrified neighborhoods indicating popularity amongst gentrifiers. Table 6: Hypotheses of the Effect of Residential Gentrification.
43
4. Gentrification in the Four Cases Residential gentrification manifests itself within the change in social, economic, and physical indicators that are discussed in-depth in the theoretical framework. These include age, ethnicity, education level, dwelling value, income, housing developments, and tenure shifts and are shown in table 2 in chapter two. Social indicators are illustrative of the displacement process. Economic and physical indicators relate to the economic revitalization and development within gentrifying neighborhoods. These indicators are therefore analyzed to develop an empirically based categorization of the extent to which residential gentrification is present in the selected cases. This analysis is supplemented with earlier research into how gentrification geographically unfolds within Amsterdam by Hochstenbach (2017). It results in a categorization, through which the changes in the total retail supply are approached and explained based on different observations regarding the residential gentrification variables.
4.1. Social Indicators of Gentrification The social indicators upon which the four cases are analyzed are age, ethnicity, and education level. The population, households, and age distribution of the four cases are
44 displayed in table 1. All four cases have a relative similar population and number of households, although Bos & Lommer and Osdorp show increases identical to or larger than the MSA. De Pijp and Rivierenbuurt are also growing regarding both population and households, albeit slower. These results might relate to more residential developments located to the edges of the city, while there are increasingly little extensive residential developments within the Rivierenbuurt and the Pijp. The distribution of age shows that all neighborhoods are relatively younger than the MSA, with Bos & Lommer being the youngest and Osdorp relatively the oldest in comparison to the other neighborhoods. Furthermore, de Pijp, Bos & Lommer, and the Rivierenbuurt have a notably higher share of inhabitants aged between 20-35 that the MSA, with Osdorp having less than the MSA.
Variable Amsterdam B&L Osdorp De Pijp Rivierenb. Population 854316 34502 38444 35361 29672 % 2008-2018 +14,3 +15,2 +25,7 +6,7 +10,1 Households 462584 18533 18778 22201 16985 % 2008-2018 +10,8 +14,8 +10,2 +4,1 +4,5 Age %0-19 19 20 24 13 15 %20-35 29 36 26 38 35 %35-49 21 23 20 22 20 %50-64 18 13 16 16 15 %65+ 12 8 15 11 14 Table 7: Population, Households, and Age. Source: BBGA, 2019.
45 70,0 Ethnicity 2008-2018 65,9 63,1 60,0 56,8 56,2 55,8 51,5 53,2 46,6 47,6 50,0 44,6 42,4 39,8 40,0 34,3 35,4 33,7 29,9 26,6 30,0 24,6 22,2 18,0 18,019,7 20,0 17,6 16,117,2 14,1 15,6 13,9 9,5 10,0 10,0
0,0 Amsterdam Bos & Lommer Osdorp De Pijp Rivierenbuurt % Western 2008 % Western 2018 % Native 2008 % Native 2018 % Non-Western 2008 % Non-Western 2018 Figure 9: Ethnicity in Amsterdam 2008-2018. Source: BBGA, 2019.
Ethnicity composition for the four cases is displayed in figure 3 and also reveals differences amongst the selected neighborhoods. Bos & Lommer and Osdorp respectively have notable higher shares of inhabitants with a non-western migration background (44,6 and 56,2) compared to the MSA (35,4) while simultaneously showing lower than average percentages of western and native Dutch migration backgrounds. These neighborhoods have historically been characterized by higher than average shares of non-western migrants (Uitermark & Duyvendak, 2004). De Pijp and de Rivierenbuurt show a reverse relation to the MSA: these neighborhoods have prominent higher percentages of native and western migration backgrounds. On the other hand, they have lower shares of inhabitants with non-western migration backgrounds. Regarding ethnicity, de Pijp and de Rivierenbuurt show relatively similar compositions and contrast with the relatively identical compositions of Osdorp and Bos & Lommer. Another interesting observation of ethnicity is the share of "new city- dwellers" that each neighborhood has. Figure 5 shows the distribution of this population group amongst the four cases. It indicates that the Rivierenbuurt (43,4) and de Pijp (41,9) have higher shares of this population group than the MSA (29,1). To a lesser extent, Bos & Lommer also has a higher than average percentage (34,3) which is unusual in combination with the previous analysis of ethnicity in which Bos & Lommer showed higher than average shares of non-western migration backgrounds. Osdorp (10,9) shows significantly lower than average shares of this population group.
46 Amsterdam in general, de Pijp, and the Rivierenbuurt show slight increases between 2008-2018. Bos & Lommer shows a notable increase of 13 points. Osdorp shows only a 0.2 pp increase.
Share of New City-Dwellers
50,0 39,8 41,9 40,2 43,4 40,0 34,3 27,4 29,1 30,0 21,3 20,0 10,7 10,9 10,0 0,0 Amsterdam E Bos & Lommer F Osdorp K De Pijp K Rivierenbuurt
2008 2018
Figure 10: Shares of "New City-Dwellers". Source: BBGA, 2019.
The final social variable that is analyzed is the education level measured between 2010- 2016 and is displayed in figure 5. Similar to ethnicity and the share of new city- dwellers, the degree of education level also illustrates differences amongst the neighborhoods. In general, the share of inhabitants that have completed or partake in higher education has grown in favor of those with lower or middle education. De Pijp and de Rivierenbuurt have low shares of lower education (17 and 13) in contrast to high shares of population with higher education (54 and 59). The share of lower and middle education is declining in favor of the shares of higher education. Bos & Lommer follow these trends and also shows declining shares of population with lower education (-12 points) in favor of population with higher education (+14 points). While Bos & Lommer scored sub-average on the city level in 2010, in only six years, this has reversed with Bos & Lommer having slightly higher shares of higher-educated inhabitants than the MSA. Osdorp has higher than average population shares of lower and middle education levels (37 and 41 percent). The share of inhabitants with higher education levels has increased with 4 points to 22 percent but still remains only half the level of the MSA of 44 percent.
47 Education Level 2010-2016 70 59 60 54 50 50 44 45 45 41 41 41 38 38 37 40 34 33 33 32 31 30 31 29 28 26 28 30 24 22 22 18 17 18 20 13 10 0 Amsterdam E Bos & Lommer F Osdorp K De Pijp K Rivierenbuurt
% Low 2010 % Low 2016 % Middle 2016 % Middle 2016 % High 2010 % High 2016
Figure 11: Education Level 2010-2016. Source: BBGA, 2019.
4.2. Physical Indicators of Gentrification The physical indicators include the developments in the housing supply and ownership distribution. The housing supply and developments between 2014 and 2018 are displayed in table 5. Developments in the housing supply might be indicative of residential gentrification if the dwellings are built for the incoming populace. In the case of Bos & Lommer, which shows the highest percentage of increase in housing supply, such a form of gentrification might be prevalent. In the Kolenkitbuurt, social rental corporation Eigen Haard is currently in the process of demolishing the current social dwellings and replacing them with newly built dwellings in the period 2014-2019 (Eigen Haard, 2019). Another private building project by the name of BLOOM advertises with how Bos & Lommer is ‘buzzing' and will result in 150 apartments that can be bought (BLOOM, 2019). Although these developments are not within the scope of this research, it would be interesting to take a more in-depth look into what exactly is built in the coming years and how it is advertised. Nonetheless, the increasing developments in the neighborhood are indicative of economic reinvestment, which is so central in the beginning phases, and at the heart of, gentrification.
Physical Indicators Housing Supply % 14-18 Amsterdam 432632 +5,84 Bos & Lommer 17457 +10,42 Osdorp 17133 +1,38 De Pijp 21377 +2,54 Rivierenbuurt 15870 +2,12
48 Table 8: Housing Supply and Change 14-18. Source: BBGA, 2019; CBS, 2017.
Tenure Distribution 2012-2018
70 60,0 60 50,6 51,4 44,7 47,9 46,9 44,8 50 42 43,2 42,8 33,9 36,4 40 28,4 26,8 33,4 32,7 33,6 26,929,6 30,0 29,0 30 25,1 24,3 25,2 23,3 20,4 18,721,7 15,2 20 11,0 10 0 Amsterdam Bos & Lommer Osdorp De Pijp Rivierenbuurt
% Social 2012 % Social 2018 % Private 2012 % Private 2018 % Owner 2012 % Owner 2018
Figure 12: Tenure Distribution. Source: BBGA, 2019.
The tenure distribution for the four neighborhoods is displayed in figure 6. In line with city-wide developments, each neighborhood has seen a decline in the share of social housing while seeing increases in private rental and homeownership. Only the Rivierenbuurt has roughly 9 percent less social housing than the MSA, with the other cases showing higher shares. Homeownership in the Rivierenbuurt and de Pijp is below average, while private rental is above. The distribution of the construction year of the dwellings, shown in figure 7, illustrates the historical character of some of the neighborhoods. This distribution shows that respectively in ascending order that de Pijp is the oldest neighborhood with much of the dwellings built before 1930, followed by de Rivierenbuurt of which a large share was built before 1960, Bos & Lommer which was primarily built in the 1931-1960 period and Osdorp which is a relatively new neighborhood built primarily between 1961-2010. The build year seems to relate to the geography of gentrification in Amsterdam, as it is a process that has started in the city- center moving outwards toward the Ring A10 boundaries (Hochstenbach, 2017; Taggart, 2009; Tieleman, 2013; Milikowski, 2018). Relating to the previously mentioned new-built gentrification, Bos & Lommer shows a high share of new built-dwellings (+15,35%) that have been built after 2010.
49 Buildyear 100% 9% 16% 30% 26% 6% 80% 42% 7%
60% 21% 25% 32% 40% 85% 21% 53% 58% 51% 20% 18% 1% 0% Amsterdam Bos & Lommer Osdorp De Pijp Rivierenbuurt
< 1919 1919-1945 1946-1990 > 1990
Figure 13: Buildyear. Source: Gemeente Amsterdam, 2018.
4.3. Economic Indicators of Gentrification As potentially the most explanatory variables of gentrification, economic variables are indicative of economic upgrading due to the gentrification process. The indicators used for assessing the economic situation are limited to dwelling value and income. The dwelling value is measured as the value per square meters, as this accounts for building-size and provides a better aggregation of the value in a neighborhood as a whole. Table 9 shows the value per square meter for each of the selected neighborhoods as well as an index calculated against the MSA. The change relative to the MSA increase is also displayed in figure 14. Regarding the value in the neighborhoods, the index of Bos & Lommer and Osdorp was below the MSA in 2014, in contrast to de Pijp and the Rivierenbuurt. In descending order, however, Bos & Lommer, the Rivierenbuurt and de Pijp show above-average increases. Osdorp has shown a sub-average increase of 24,6 percent resulting in a lower index than it had before. In four years, Bos & Lommer has closed the gap between the MSA and the value per square meter is now only slightly higher. De Pijp and the Rivierenbuurt historically already had higher than average values, and this has only increased in the 2014-2018 period.
Value p/m2 Amsterdam Bos & Lommer Osdorp De Pijp Rivierenbuurt Value ‘18 4437 4476 2580 5850 5589
50 Value ‘14 2888 2674 2071 3583 3366 % 14-18 +53,6 +67,4 +24,6 +63,3 +66,1 Index ‘18 100 100,1 58,2 131,9 126,0 Index ‘14 100 92,6 71,7 124,1 116,6 Table 9: Value per square meter. Source: BBGA, 2019.
Figure 14: Procentual change in value per square meter 2014-2018 vs. MSA average. Source: BBGA, 2019.
The development in individual income, the indexes against the MSA average and the shares of lower and higher-income quintiles in the neighborhoods are displayed in table 10. Both Bos & Lommer and Osdorp have incomes lower than the MSA, while de Pijp and the Rivierenbuurt have incomes higher than the MSA. Bos & Lommer however, shows above-average increases, while Osdorp shows increases below-average. The change in the share of lower-income groups shows that these are decreasing in Bos & Lommer and de Pijp while increasing sharply in Osdorp and only slightly in the Rivierenbuurt. The share of higher income groups in all neighborhoods shows increases. In Osdorp, this increase is below the MSA. The Rivierenbuurt performs only slightly better than the MSA, while Bos & Lommer and de Pijp show substantial increases (9,1 and 6,5 points respectively.
51 Income x1000 Amsterdam Bos & Lommer Osdorp De Pijp Rivierenbuurt 2010 22,9 17,6 18,1 24,5 26,0 2017 28,7 23,3 20,0 30,1 32,5 10-17 +25,3 +32,3 +10,5 +23 +25,2 Index 10 100 77,9 79 107 113,5 Index 17 100 81,2 79 104,9 113,2 % Low-Income 40,5 42,7 46,9 40,2 35,6 05-17 +2,5 -1,9 +4,2 -1,8 +0,6 % High-Income 23,4 18,7 13,6 24,5 28,6 05-17 +2,4 +9,1 +1,2 +6,5 +2,6 Table 10: Income. Source: CBS, 2005; 2010; 2015; 2017.
4.4. Classifying Gentrification in the Four Neighborhoods. Analyzing social, physical and economic characteristics within the four neighborhoods allows for classification and locating them in the spectrum of gentrification. Such a classification is ideal-typical but helps placing the selected neighborhoods according to their characteristics and the degree to which they show gentrification. Gentrification can be classified according to three categories: (1) no gentrification, (2) gentrification in process and (3) gentrified. Classifying gentrification occurs according to the degree of upgrading that is part of the definition, often measured through an increase in real estate and income values within the values (see Hochstenbach & Van Gent, 2014). Nonetheless, the social indicators that are analyzed show higher than average degrees of ‘new city-dwellers' in each neighborhood except for Osdorp. Furthermore, these neighborhoods show increases in new city-dwellers as well with the increases the highest in Bos & Lommer. It indicates a process of societal change going on in the neighborhood. The physical indicators illustrate how de Pijp and de Rivierenbuurt are pre-war neighborhoods. Bos & Lommer and Osdorp are neighborhoods that have been built between 1960-90. However, Bos & Lommer is situated far closer to the city-center than Osdorp is. Geographical distance has often been described as being an important determinant of gentrification, with neighborhoods closer to the city-center showing stronger and faster processes of gentrification (Tieleman, 2013). Another part of the physical indicators is housing development. This indicator shows that 15,43% of Bos & Lommer has built after 2010, indicating that economic reinvestment in this neighborhood is currently taking place.
52
Moving on to the economic characteristics, these show how Bos & Lommer especially and de Rivierenbuurt and de Pijp to a lesser extent show above-average increases in both WOZ-value and incomes. Such indicators have also been included in a neighborhood classification by Van Gent & Hochstenbach (2014) shown in figure 15. This figure shows how de Pijp and de Rivierenbuurt especially traditionally already had a high socio-economic status that has increased in the period 2004-2011. Bos & Lommer, on the other hand, started from a low status but is also upgrading. Osdorp was low-status and has shown downgrading developments since 2004. Based on the indicators mentioned earlier, the classification that is used to analyze the commercial characteristics is shown in table 5.
Figure 15: Amsterdam Neighborhoods per initial status (2004) and upgrading or downgrading. Source: Hochstenbach & Van Gent, 2014.
Status Neighborhood Indicators Gentrified Rivierenbuurt, - High WOZ-value De Pijp - High Income - Above average increases
53 - Above average shares of new city-dwellers - More than 50% of high education levels Gentrifying Bos & Lommer - Housing Developments since 2010 - Increasing amount of new city-dwellers - Above average WOZ-value increase - Increasing share of higher educated inhabitants No- Osdorp - Low Socio-Economic Status Gentrification - Sub-average increases in income and WOZ- value - Sub-average shares of new city-dwellers - Relatively low higher education in comparison to the MSA. Table 11: Classification of Neighborhoods.
5. Results After analyzing the social, economic, and physical characteristics upon which the selected neighborhoods show differences and are classified alongside the extent of
54 gentrification, we now use this categorization to assess how it affects the retail supply. The previous chapter has shown to what extent residential gentrification is present in these neighborhoods, as well as what their socio-economic and physical characteristics are. The resulting categorization offers a lens through which differences in the total retail distribution are analyzed. This overall retail distribution is subdivided into four sub-categories which form the parts of their sum: daily goods, retail, leisure, and services. The effect of residential gentrification is tested on the city-wide scale through proxy variables of value, income, and education level. For significant relationships, maps are shown that illustrate the geographic distribution. The effect of gentrification can manifest itself in two forms: either quantitatively (external upgrading) through changes in frequencies and shares, and qualitatively (internal upgrading) that includes in-store category differentiation and store upgrading. For the four sub-categories, external upgrading is analyzed. For the daily goods and leisure categories, a perspective into internal upgrading is included.
5.1. Total Retail Supply Distribution The distribution of the four categories of the total retail supply in the four cases and for the city are displayed in figure 16. The overall frequencies and corresponding percentages are included in Appendix 5a. City-wide, vacancy accounts for 2,9 of the city-wide amounts of selling points. The share of vacancy is relatively similar in de Pijp and the Rivierenbuurt. It is lower in Bos & Lommer and Osdorp. The share of daily goods in the total retail supply city-wide is 18,3. The Rivierenbuurt, Bos & Lommer and Osdorp have shares similar to that of the MSA. In de Pijp, there are relatively fewer daily stores than in other neighborhoods. On the city-wide level, roughly 29,8 percent of all stores are retail stores. This share is relatively lower in the Rivierenbuurt and Bos & Lommer, to a lesser extent in de Pijp, and slightly higher in Osdorp. Leisure accounts for 28,9 percent of all stores on the MSA level. In the Rivierenbuurt and Bos & Lommer, this is marginally higher, while it is notably higher in de Pijp. In Osdorp, relative to the MSA, there are less leisure related establishments. Services account for 20,2 percent of all stores, and only the Rivierenbuurt differs from this, with 26,9 percent of all stores in this neighborhood being service related.
55 Total Retail Distribution 100 20,2 20,5 26,9 21,1 20 80 24,4 28,9 31,1 60 36,6 29,5
40 32,2 29,8 21,2 22,4 27,9 20 18,1 18,3 19,4 7,4 18,2 2,9 12,3 2,8 3,1 5,2 0 Amsterdam (N=97) De Pijp Rivierenbuurt Bos & Lommer Osdorp
Vacancy Daily Goods Retail Leisure Services
Figure 16: Total Retail Distribution. Source: Locatus, 2019.
The changes in the time period 2005-2019 for each of the four cases are displayed in figure 17. The overall change in frequencies and shares is included in Appendix 5b. Marginal increases in vacancy rates in de Pijp and the Rivierenbuurt are observed, and stronger increases are visible in Bos & Lommer and Osdorp. Daily goods are slightly in decline in all the four cases while retail declines are stronger, except for Bos & Lommer. The decrease in retail and an increase in vacancy is explained by the trend of online shopping, which has affected retail stores most of all. On the other hand, leisure establishments are increasing most strong in Bos & Lommer, followed by Osdorp. It is also explained by the broader trend of increases in F&B establishments in Amsterdam and other cities nationally. Regarding services, increases are observed in three cases except for Bos & Lommer, which shows a 4-point decrease in the 14-year span.
56 Change in Overall Shares of Total Retail Distribution
15 12,3 11,7
10 6,7 5,5 4,7 5 3,4 2,4 1,5 1,3 0,1 0,8 0 -0,7 -0,3 -0,3 -5 -2,6 -3,7 -4 -10 -7,5 -10,4 -15 -12,5 De Pijp Rivierenbuurt Bos & Lommer Osdorp
Vacancy Daily Goods Retail Leisure Services
Figure 17: Change in Total Retail Distribution. Source: Locatus, 2019.
5.1.1. Share of Chain Stores Figures X to X show the change between 2005 and 2019 in the share of individually operated stores in the four neighborhoods. Daily stores are characterized by low shares of independently operated, which is likely due to the fillialisation of supermarkets. The amount of individually operated daily stores is the lowest in the Rivierenbuurt. Regarding retail stores, the share of independently operated stores (IOS) remains relatively constant, and no signs of a notable down or uptrend become visible. In Osdorp, relatively more retail stores are part of chains. However, if this was due to gentrification processes, the observation would have been that Bos & Lommer should start low but show significant increases. Looking at the shares of IOS in leisure-related establishments, we see that these have traditionally been characterized by being individually operated. This is however in decline. The only uptrend that we can observe regarding IOS is in service stores. After a small decline, these seem to be increasingly individually operated especially in the Rivierenbuurt and De Pijp. Hypothesis one is that in gentrified neighborhoods, there are higher shares of non- chain stores for all categories of the total retail supply. The alternative hypothesis is that there is no relationship between gentrification and the percentage of chain stores in Amsterdam neighborhoods. Based on the observations in figure 17, the alternative hypothesis is accepted. There seems to be no observable relationship between gentrification and the share of chain stores in these neighborhoods.
57 % IND. LEISURE % IND. SERVICE
De Pijp Rivierenbuurt De Pijp Rivierenbuurt Bos & Lommer Osdorp Bos & Lommer Osdorp 100% 100%
90% 90%
80% 80%
70% 70%
60% 60%
50% 50% 2005 2010 2015 2020 2005 2010 2015 2020
% IND. DAILY % IND. RETAIL
De Pijp Rivierenbuurt De Pijp Rivierenbuurt Bos & Lommer Osdorp Bos & Lommer Osdorp 100% 100%
90% 90%
80% 80%
70% 70%
60% 60%
50% 50% 2005 2010 2015 2020 2005 2010 2015 2020
Figure 18: Share of Individually Owned Stores. Source: Locatus, 2019.
58 6. Daily Goods Supply Daily goods stores include all stores that sell products for home-consumption and include bakers, supermarkets, butchers, mini-markets- delicatessen stores, amongst others. The whole list of stores included in the daily goods category is displayed in Appendix 3a. Quantitative developments include the change in shares of the specific type of stores as part of the total daily goods supply. Based on the theoretical discussion that provided directions as to where to look for commercial gentrification in all categories, the expectation is that it will likely manifest itself in ‘local specialty' stores such as wine-stores and delicatessen stores. In spite of these expectations however, a quantitative analysis has been conducted on the total frequencies and shares of all stores within this category. This analysis is displayed in Appendix 6a. On the basis of this analysis, a selection of observations is presented in table 12. Qualitative change, or internal upgrading, can manifest itself in product upgrading or store upgrading. Product upgrading includes the shift towards more biological and locally sourced products, whilst store upgrading includes the establishment of a sushi- or meal-point in existing supermarkets.
6.1 External Upgrading Table 12 shows a selection of notable differences between the shares of the specific type of stores in the total daily supply in 2019, compared to the MSA. This leads to four observations for which we test their relationship. The first is that de Pijp and the Rivierenbuurt have wine stores that account for respectively 4,7 and 3,2 percent of all daily stores. Bos & Lommer and Osdorp have no wine-stores, whilst the MSA average of wine-stores is 3,8 percent. This is in line with the theoretical expectation that there are more specialty wine-stores located in gentrified neighborhoods. The second observation is that de Pijp (5,5) and the Rivierenbuurt (6,7) also have relatively more delicatessen stores in comparison to the MSA (5,0), whilst Bos & Lommer (2,2) and Osdorp (1,8) have fewer delicatessen stores as a share of the total supply. As the second type of specialty store, this is also expected. The third observation is that there are relatively more mini-markets in Bos & Lommer (11,1) and Osdorp (12,3) compared to the MSA (8,6). In contrast, there are relatively less in de Pijp (3,1) and the Rivierenbuurt (0). The last observation regarding the current distribution is that de
59 Pijp and the Rivierenbuurt have more vegetables and/or fruit stores than both Bos & Lommer and Osdorp.
Daily Goods MSA (N=90) De Pijp Rivierenbuurt Bos & Osdorp Lommer Total Daily 1126 127 62 45 57 % Individual - 65.4 (83) 62.9 (39) 77,9 (35) 68,4 (39) Wine Stores 3,8 (43) 4,7 (6) 3,2 (2) - - Delicatessen 5,0 (56) 5,5 (7) 6,5 (4) 2,2 (1) 1,8 (1) Mini-Market 8,6 (97) 3,1 (4) - 11,1 (5) 12,3 (7) Vegetables/Fruit 3,6 (40) 3,1 (4) 8,1 (5) - 1,8 (1) Table 12: Selection of Observations of share of stores of total daily composition in 2019. Source: Locatus, 2019.
How has the distribution of daily goods stores changed between 2005 and 2019? Table 13 shows a selection of observations of change within the whole daily goods distribution included in Appendix 6b. The frequencies show that total daily stores are increasing in de Pijp but in decline in the other three cases, and moreover that the share of individual operated stores within the daily goods category is also in decline which indicates chain-forming. The first observation of change is in wine-stores. Following from the previous observation that wine-stores are present in the Pijp and Rivierenbuurt, it appears that they have been a product from after 2005. More specifically, in 2010, no wine-stores were present in both de Pijp and de Rivierenbuurt. In 2015, three wine-stores had opened in the Pijp. Now it appears that between 2015- 2019 the number of wine-stores in de Pijp has doubled, and two have opened in the Rivierenbuurt. This indicates that they are a more recent type of store that have opened in these neighborhoods. In comparison, they might be considered to be the ‘upgraded' version of the traditional liquor store, specializing in wines. The second observation of change is that mini-markets have been in decline since 2005 in all cases except for Osdorp. In this neighborhood, since 2005 three more mini-markets have opened in Osdorp accounting for a 5,7 percent increase in their share within the total daily store distribution.
60 Change De Pijp Rivierenbuurt Bos & Lommer Osdorp Total Daily +16,5 -11,4 -21,1 -6,6 % Individual -9,9 -7,1 -1,2 -7 Wine-Stores +4,7 +3,2 - - Mini-Market -6,9 -8,6 -13,5 +5,7 Table 13: Selection of observations of change in the share of stores in the total daily supply 2005- 2019. Source: Locatus, 2019.
6.2. Quantitative Robustness Tests Based on the previous analysis, we test correlation with proxy variables of residential gentrification for wine-, delicatessen-, fruit/vegetable stores, and for mini-markets. In order to assess if the distribution of these types of store correlates with residential gentrification, a Spearman's non-parametric correlation test has been conducted using Stata on the MSA-level (N=97). The results of this test are displayed in table 14. Much of the correlation is either not significant, very weak (.00-.19) or weak (.20-.39). Regarding vegetables and fruit, it seems that only dwelling value has some effect on the number of shops and the square meters that they have, although this correlation is weak. Second, regarding delicatessen, it appears there is a moderate relationship between dwelling value and the number of delicatessen stores with a Spearman's rho of 0.40 significant at alpha = 0.001. This is shared by the higher education variable that has a rho of 0.378, which is also significant at the 0.001 level. On the other hand, household income does not seem to have a significant relationship with this variable. The results are constant if we measure the square meters of delicatessen stores, through which the correlation slightly increases to be both moderate at the 0.001 level. The third result regards the mini-markets, which shows that only household income seems to significantly correlate with both the amount and surface of mini-markets in Amsterdam neighborhoods. This correlation is weak however, but significant at the 0.05 level. The last type of store is wine-stores, which show statistically significant and varying relationships with the selected proxy variables for residential gentrification. Dwelling value especially (0.62 and 0.60 at the 0.001 level) and share of higher education (0.55 and 0.549 at the 0.001 level) have a moderate to strong correlation with the amount and surface of wine stores. Household income has a weak influence with 0.29 and 0.31 at the 0.05 level.
61
Spearman’s Rho Woz per sq/M Household Income % Higher Ed. Selling Points Vegetables/Fruit 0,22** 0.04 0.18* Delicatessen 0,40*** 0.11 0.378*** Mini-Market 0,27 -0.295** -0.029 Wine Store 0,62*** 0.29** 0.55*** Square Meter Vegetables/Fruit 0.21** 0.05 0.169 Delicatessen 0,44*** 0.12 0.415*** Mini-Market 0.06 -0.31** -0.082 Wine Store 0.60*** 0.31** 0.549*** Table 14: Spearman Correlation Test for the effect of Residential Gentrification on amount of selling points and store surface of selected daily stores. (* = significant at 0.010 level, ** = significant at 0.05 level, *** = signficant at 0.001 level). Source: Locatus, 2019.
Figure 19: Distribution of Specific Stores as Share of the Total Daily Supply. From top-left to bottom- right (1) delicatessen, (2) wine-stores, (3) mini-markets and (4) vegetables/fruit. Source: Locatus, 2019.
62 The distribution of the store types for which we have tested is displayed on the map in figures X. In these maps, these types of stores as a share of the total daily goods supply per Amsterdam neighborhood are displayed. The ring-road A10 is added as the ‘boundary' of gentrification in Amsterdam. Regarding the top two maps, of delicatessen and wine-stores, we see strong concentration patterns. Almost all instances of these types of stores are within the boundaries of the ring A10. For deli's, some are located on the southern side of the ring road, which is the affluent south and the prominent South-Axis. There are three instances of delicatessen stores in neighborhoods not located within the road boundary. The same goes for wine-stores, which shows even stronger concentration. For mini-markets, we see that there is no clear pattern. We see that although de Pijp and the Rivierenbuurt show little to no instances of mini-markets, there are many instances of mini-markets within the ring road boundary. Vegetables and fruit also show some extent of concentration, yet there are too many instances of vegetables and fruit stores outside of the gentrified neighborhoods.
6.3. Internal Upgrading Whilst the daily goods category of commercial space might change quantitatively through more delicatessen and wine-stores, ‘upgrading' will likely concentrate within the products and target audience of supermarkets. The daily category of commercial space might be the one to which consumers are the most ‘loyal'. In Amsterdam Zuid, 92 percent of respondent's state that they do groceries within the Southern district. In West and New-West, this percentage is 83 percent. This is opposed to 32 (Zuid), 34 (New-West) and 25 (West) percent for the non-daily groceries (Gemeente Amsterdam, 2018). Therefore, the distance between daily commercial establishments and the neighborhood is relatively shorter than for non-daily goods. Within the category, there is already much differentiation between various selling points which captures the profoundly different characters of each nicely. Within the daily category, therefore, the expectation is that most upgrading will take place within the products, target audience, and aesthetics of supermarkets. Based on the data, supermarkets can be categorized into three categories: chain stores, biological supermarkets, and
63 supermarkets that sell different products. The distribution of these amongst the four cases and the change between 2010-2019 is displayed in table 15. Supermarkets De Pijp Rivierenbuurt Bos & Lommer Osdorp Chains 64 (9) 75 (9) 50 (6) 60 (6) Foreign 14 (2) 8 (1) 20 (2) 30 (3) Biologic 21 (3) 17 (2) 10 (1) 10 (1) Total 2019 14 12 9 10 Chains 87,5 (7) 87,5 (7) 83,3 (5) 75 (6) Foreign 12,5 (1) 12,5 (1) 16,7 (1) 25 (2) Biologic 0 0 0 0 Total 2010 8 8 6 8 Change % Chains -23,2 -12,5 -27,8 -15,0 % Foreign +1,8 -4,2 +3,3 +5 % Biologic +21,4 +16,7 +10 +10 Table 15: In-Category Differntiation of Supermarkets. Source: Locatus, 2019.
Figure 20: Distribution of Differentiated Supermarkets. Source: Locatus, 2019.
Looking at table 15 and figure 20, we see that every neighborhood has at least one biological supermarket. In all neighborhoods, these have located themselves in their respective locations in the time period van 2010-2019. Chains still dominate the overall supermarket distribution; however biologic supermarkets have increased most in the Pijp (+21,4) and Rivierenbuurt (+16,7). Markets that sell foreign products are mostly observed in Osdorp (30 percent) and Bos en Lommer (20 percent). In Osdorp, relative to the total amount of supermarkets, foreign supermarkets have increased the most (+5 percent) in comparison to other types of supermarkets. Nonetheless, there are
64 roughly the same amounts of biologic supermarkets in Bos & Lommer and Osdorp, which show comparable increases.
7. The Retail Supply The retail landscape includes stores that fall in the Locatus categories of ‘fashion & luxury’, ‘spare time’, ‘in/around house’ and ‘other retail’. Stores that are included are shown in Appendix 1a and include department stores, shoe stores, opticians, antiquarians but also hobby and electronic stores amongst others. The amount of differentiation between the products and type of stores is perhaps the greatest in this subsection of the total store supply. Measuring internal upgrading within this category is perhaps amongst the most difficult, as there is no baseline of comparison. Change will most likely manifest itself within the price that stores ask for their products. The analysis of the retail supply is therefore limited to a quantitative analysis. A selection of the observations included in Appendix 7a is displayed in table 16.
Retail MSA (N=99) De Pijp Rivierenbuurt Bos & Osdorp Lommer Total 2229 288 82 67 124 % Individual - 84,7 81,7 88,1 60,5 Clothing & 18,2 (405) 30,2 (87) 13,4 (11) 13,4 (9) 22,6 (28) Fashion Hobby 3,9 (87) 5,9 (17) 3,7 (3) 1,5 (1) 1,6 (2) Secondhand 6,1 (135) 4,2 (12) 7,3 (6) 4,5 (3) 1,6 (2) Table 16: Selection of Observations from Distribution of Retail Stores. Source: Locatus, 2019.
Three observations are drawn from the frequency table regarding the retail distribution. The first is that the clothing and fashion category accounts for 30 percent of the retail stores in de Pijp, and 22,6 in Osdorp. This is relatively high in comparison to the MSA (18,2). The Rivierenbuurt and Bos & Lommer show similar shares of clothing stores which is below the MSA. The second observation is that hobby stores are relatively less present in Osdorp and Bos & Lommer compared to the MSA. On the other hand, they are around the MSA or more present in respectively the Rivierenbuurt and the Pijp. The last observation regards the presence of secondhand stores, for which we see that these are relatively less present in Osdorp. On the other hand, the Rivierenbuurt has relatively more in contrast to the MSA, whilst de Pijp and
65 Bos & Lommer show shares just under the MSA. A selection of the observations of change in the 2005 to 2019 period, included in Appendix 7b, is displayed in table 17. We see that shoes and leather is slightly increasing in the gentrified or gentrifying cases, whilst decreasing in Osdorp. The opposite trend is observed for stores in the household and luxury category.
Change De Pijp Rivierenbuurt Bos & Lommer Osdorp Total -27,1 -26,1 -1,5 -27,9 % Individual +3,2 -3 +4,2 +1,8 Shoes & Leather +0,9 +1,3 +1,5 -1,3 Household & -1,5 -3,5 -1,4 +3,1 Luxury Table 17: Selected Observations of Change in Retail Supply 2005-2019. Source: Locatus, 2019.
7.1. Robustness Tests Subtracting from the analysis of the composition of the neighborhoods the variables that are checked for robustness and correlation are hobby stores, secondhand stores and shoes and leather stores. Table 18 shows how the dwelling value correlates significantly with the selected selling points, to different extents. For shoes and leather, hobby, and secondhand stores there seems to be a significant moderate correlation. If we measure per store surface, the extent of correlation decreases to weak for shoes and leather, and secondhand stores. The effect remains moderate for hobby stores. The household income variable has no statistically significant relationship with any of these stores. For the share of higher education in a neighborhood the effect for the first three types of stores is statiscally significant but weak. This observation remains the same if we measure per store surface.
Spearman’s Rho Woz per sq/M Household Income % Higher Ed. Selling Points Shoes & Leather 0.446*** 0.054 0.335*** Hobby 0.446*** -0.057 0.331** Secondhand 0.45*** 0.017 0.361*** Square Meter Shoes & Leather 0.381*** 0.012 0.285** Hobby 0.419*** -0.03 0.316** Secondhand 0.337*** 0.045 0.304**
66 Table 18: Spearman correlation test for effect of residential gentrification on amount of selling points and store surface of selected daily stores (* = significant at 0.010 level, ** = significant at 0.05 level, *** = significant at 0.001 level).
Figure 21: Distribution of Four Types of Retail Stores. From top-left to bottom-right: (1) second-hand clothing, (2) second-hand stores, (3) hobby stores and (4) living-stores. Source: Locatus, 2019.
67 8. The Leisure Supply Leisure includes all food and beverages, culture and leisure related establishments. It is a relevant category for the effect of residential gentrification as the incoming populace have a different consumption pattern that is lifestyle and leisure oriented. As a result, leisure-related amenities will locate themselves in these areas, such as pubs, restaurants, gyms, and cultural facilities (Doucet, 2013). Similar to daily goods, commercial gentrification might manifest itself within this category of commercial space in two ways: (1) through internal upgrading, by which existing food & beverages, leisure or culture selling points cater to the needs of the new populace or (2) through replacement, by which old establishments make place for new ‘more trendy’ ones. Similar to retail however, the former is difficult to establish. The latter is analyzed through data on the leisure-supply provided by Locatus. In this section, a quantitative analysis is conducted on the frequencies and shares of various types of leisure establishments in the total distribution per neighborhood. The full frequency table is included in Appendix 8a, of which a selection of the observations is displayed in table 19.
Leisure MSA De Pijp Rivierenbuurt Bos & Osdorp (N=99) Lommer Total 2114 378 114 93 94 % Individual - 92,1 87,7 91,4 84 F&B 80 (1691) 93,1 (352) 92,1 (105) 90,3 (84) 89,4 (84) Grillroom 4,2 (89) 1,9 (7) 0,9 (1) 10,8 (10) 8,5 (8) Café-Resto 10,4 (219) 11,1 (42) 9,6 (11) 7,5 (7) 5,3 (5) Restaurant 11,6 (246) 28,8 (109) 36 (41) 15,1 (14) 17 (16) Table 19: Selection of Observations in Leisure Supply. Source: Locatus, 2019.
8.1. Distribution of the Leisure Supply Looking at the frequency table of leisure establishments, the first observations is that there is a low share of franchising of only around 10 percent. The share of food and beverages in the total leisure supply is highest in de Pijp (93,1) and lowest in Osdorp (89,4). Grillroom establishments are relatively more present in Bos & Lommer (10,8) and Osdorp (8,5), whilst being less present in de Pijp (1,9) and de Rivierenbuurt (0,9). Fourth, in descending order around 10 percent of commercial space are café- restaurants, with the lowest in Osdorp of around 5 percent. The last observation is that
68 28,8 to 36 percent of leisure space are restaurants in the gentrified neighborhoods whilst shares of 15,1 to 17 percent are observed in Bos & Lommer and Osdorp. The change in the period between 2005-2019 is displayed in included in Appendix 8b, with notable observations displayed in table 20. From this table, we can draw some extra observations: (1) the total leisure establishments is increasing and the strongest in Osdorp, (2) the share of individual leisure establishments is declining in all neighborhoods, (3) coffeehouses are declining in the gentrified neighborhoods whilst increasing in the other, (4) the same trend is visible for grillrooms, (5) whilst the opposite goes for fastfood establishments, (6) café-restaurants are increasing, and (7) fitness is increasing except for in Osdorp.
Leisure Change De Pijp Rivierenbuurt Bos & Lommer Osdorp Total +17,8 +29,5 +66,1 +91,8 % Individual -4,8 -12,3 -5 -9,8 Coffeehouse -1 -1,1 +1,1 +1,1 Fast-food +2,9 +0,7 -14,2 -1,4 Grillroom -2,8 -2,5 +1,8 +6,5 Café-Resto +11,1 +9,6 +7,5 +5,3 Fitness +1,8 +2,1 +2,5 -0,9 Table 20: Observations of Change in Leisure Establishments 2005 to 2019. Source: Locatus, 2019.
8.2. Leisure Robustness Tests Drawing from our observations, we will test the correlation between the established proxies of gentrification with (1) share of food and beverages, (2) number of grillroom/shoarma restaurants, (3) share of café-restaurants, (4) share of restaurants, (5) fastfood and (6) fitness. Correlation is measured on the neighborhood level (N=97) using a spearman’s test as it concerns non-parametric data. What this analysis shows is that there is a significant relationship between the amount of food and beverages and the value per square meter and share of higher education, respectively a moderate and weak correlation. As the median household income increases, the amount and surface of grillroom/shoarma establishment will decline in a moderate correlation, relevant at alpha = 0.001. Café-Restaurants and Restaurants also share a moderate correlation with the value per square meter, and for Restaurants this is diminished to weak when measured per square meters. Nonetheless, both are relevant at the 0.001 level. There is a weak correlation relevant at alpha 0.05 between all residential gentrification proxy
69 variables and the amount of fitness establishments. There seems to be a weak or no correlation between the established variables and the amount and surface of fast-food establishments.
Spearman’s Rho Value/m2 Household Income % Higher Ed. Selling Points Food & Beverages 0.522*** 0.070 0.391*** Grill/Shoarma 0.005 -0.425*** -0.099 Café-Restaurant 0.518*** 0.174* 0.427*** Restaurant 0.459*** 0.125 0.389*** Fastfood 0.219** -0.072 0.112 Fitness 0.297** 0.293** 0.309** Store Surface Food & Beverages 0.516*** 0.043 0.408*** Grill/Shoarma -0.013 -0.412*** -0.094 Café-Restaurant 0.416*** 0.102 0.345*** Restaurant 0.534*** 0.142 0.43*** Fastfood -0.006 -0.267** -0.157 Fitness No data No data No data Table 21: Spearman Correlation Test for Effect of Residential Gentrification on Selected Leisure- Oriented Establishments. (* = significant at the 0.010 level, ** = significant at the 0.05 level, *** = significant at the 0.001 level).
The geographic distribution of the shares these stores account for in the total leisure supply is shown in figure 22. For café-restaurants, fitness and restaurants we see no clear concentration patterns. It does not seem that these concentrate either within the ring-road boundary or the affluent south and the south axis. The contrary is observed for grillroom restaurants. For these type of stores a concentration in the New-West city-ward is observed, including in Osdorp. These type of restaurants become more frequent moving away from the city-center towards to peripheral located areas. The high percentages of grillroom as part of the total retail supply in the Western part of the map might be explained by the high shares of inhabitants with a non-western immigration background.
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Figure 22: Geographic Distribution of Selected Leisure Stores. Source: Locatus, 2019.
8.3. Popular Cafés and Restaurants: Qualitative Change Residential gentrification in the leisure category does not manifest itself solely within the frequencies and shares of different types of food and beverage establishments. These are also expected to occur within the categories of ‘café’, ‘restaurant’, ‘café- restaurant’ amongst others. A brown café differs from a specialty-beer café, and fine- dining restaurants differ from their more low-budget counterparts. To assess for this in-category differentiation, the popularity of F&B establishments is measured using location references of Instagram. This shows that especially de Pijp is popular amongst users of Instagram. To a lesser extent this is also the case for the Rivierenbuurt and Bos & Lommer. In Osdorp, F&B establishments have relatively a lesser amount of posts than the other three neighborhoods. Based on the popularity measure, displayed in figure 22 for each of the neighborhoods, we observe that even in the gentrified neighborhoods there are establishments that are not popular amongst the Instagram users. Although the average popularity is the highest in De Pijp, it is relatively similar for Bos & Lommer and the Rivierenbuurt. It seems that there are certain ‘hotspots’ in these neighborhoods that attract most of the gentrified populace, which are
71 significantly more popular than their unpopular counterparts. In Osdorp, almost all establishments fall in the popularity category of 0-5 posts per month. This is indicative that at least on Instagram, F&B establishments in Osdorp are less popular. This can be signifying of the fact that in Osdorp the ‘leisure culture’ is less prominent or underdeveloped.
Neighborhood N Locat. References Average Popularity De Pijp 59 18507 314 7.67 Rivierenbuurt 47 9486 202 4.91 Bos & Lommer 33 6413 194 4.81 Osdorp 24 1421 59.2 1.93 Total 163 35827 219.8 6 Table 22: Results of Instagram Scraping. Source: Instagram, Phantombuster.com.
Figure 23: Distribution of F&B Instagram Popularity.
8.4 High-End Fitness: Qualitative Change The second category of leisure for which upgrading is assessed in the fitness category. Within fitness as a category of leisure, similar to cafés and restaurants, the setting and character of establishments begin to matter. Whilst the practice is relatively the same, a chain low-budget fitness distinguishes itself from a personal training studio that serves locally-roasted beans in terms of both the character of the establishment and
72 the product that is sold to consumers. Looking at the quantitative amounts of fitness, we see an overall trend of increase in the period between 2010-2019. In 2010, respectively the four cases had either one or two fitness establishments. In de Pijp this has increased to eight in 2019, the Rivierenbuurt has five fitness establishments, Bos & Lommer four, and Osdorp three. These are increases that range from sevenfold to a 0.5 increase. Roughly, these establishments can be divided into low-budget (ranging from 0 to 25 euro’s a month), mid-range (25-50), high-end (50+), and personal training (only tailored programs, mostly high-end). The fitness establishments in the four cases according to this categorization is displayed in figure 23. The geographical distribution of fitness establishments differs strongly per neighborhood. It especially shows how the Pijp with a total of eight has five personal training studios’, and only one low- budget fitness. Both the Rivierenbuurt and Bos & Lommer have at least one personal studio, whilst Osdorp has none. Primarily low-budget fitness is located in Osdorp.
Figure 24: Differentiation in Fitness Establishments. Source: Locatus, 2019.
73 9. The Service Supply The final category of the total retail supply is services and includes rental, craftmanship, financial, and consumer services. This includes service stores such as smiths, key-repair, hairdressers, massage parlors, photographers and traveling agencies. The service store frequencies and shares of the total retail supply for the four neighborhoods is included in Appendix 9a. A selection of observations is displayed in table 23. Regarding the 2019 distribution, we observe that all neighborhoods have more than average (55) shares of craftmanship related services, being the strongest in the de Pijp followed by the Rivierenbuurt and Osdorp. The second observation is that financial services are sub-average in the gentrified neighborhoods, but higher than average in both Bos & Lommer (9,5) and Osdorp (18,2). Regarding individual selling points, we see that (1) clothing repair stores are relatively more frequent in Osdorp, (2) electronic repair is slightly more frequent in the Pijp, (3) framers are more frequent in the gentrified neighborhoods, (4) and that massage parlors are above the MSA in the gentrified neighborhoods but below-average in both Bos & Lommer and Osdorp. Looking at the changes between 2005 and 2019 we observe that services and individually operated stores have increased in all neighborhoods except for Bos & Lommer. Craftmanship is also increasing in all four cases, whilst financial services are in decline or remain stagnant in the gentrified neighborhoods. No notable changes are observed except (1) the increase of framers in all cases, especially in de Pijp, and (2) an increase in massage parlors.
Change De Pijp Rivierenbuurt Bos & Lommer Osdorp Total +13,4 (25) +6,1 (6) -16 (-12) +6,9 (+5) % Individual +4.1 +10,5 -3,1 +13,7 Craftmanship +14,5 (44) +4,3 (8) +3,6 (-4) +16,7 (15) Framer +6,4 +1,9 +1,6 +1,3 Massage Parlor +6.1 +8,7 +1,6 +1,3 Table 23: Selection of Changes in the Service Supply. Source: Locatus, 2019.
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Service MSA De Pijp Rivierenbuurt Bos & Osdorp Lommer Total 1511 212 104 63 77 % Individual - 89,2 95,2 88,9 83,1 Categories Rental 5,8 (87) 2,8 (6) 0 (0) 6,3 (4) 2,6 (2) Craftmanship 55 (831) 67,5 (143) 62,5 (65) 55,6 (35) 59,7 (46) Financial 6,8 (103) 4,2 (9) 3,8 (4) 9,5 (6) 18,2 (14) Consumer 32,4 (490) 25,5 (54) 33,7 (35) 28,6 (18) 19,5 (15) Stores Clothing Repair 5,8 (88) 3,8 (8) 4,8 (5) 3,2 (2) 7,8 (6) Electro. Repair 2,3 (34) 3,8 (8) 1,9 (2) 1,6 (1) 1,3 (1) Framer 1,1 (17) 1,9 (4) 1 (1) - - Massage Parlor 5,2 (79) 6,1 (13) 8,7 (9) 1,6 (1) 1,3 (1) Table 24: Selection of Observations in the Service Supply. Source: Locatus, 2019.
9.1. Robustness Tests & Geographic Distribution The robustness tests for these observations show that the value of dwellings per square meter significantly correlates to a moderate extent with the presence of frame makers (0.446 & 0.432), craftmanship stores (0.417 and 0.401) and strong with massage parlors (0.572) significant at the 0.001 level. It shows no significant relationships or those which are weak with clothing repair, electronic repair and financial stores. The household income in a neighborhood has a weak negative significant relationship with clothing repair stores with a rho of -0.278 and -0.293. The degree of higher education has a positive correlation with beauty parlors of 0.275 at the 0.001 level. A moderate correlation is found for the share of higher education and massage parlors (0.489) significant at the 0.001 level. It has moderate correlation with frame makers and craftmanship stores significant at the 0.05 level. It seems that beauty parlors, frame makers, craftmanship stores and massage parlors do tend to have a correlation with residential gentrification. Figure 25 shows the geographic distribution of a selection of the correlation tested type of stores and the shares they account for in the total service supply. For beauty parlors, craftmanship stores and financial services there are no clear observation patterns observed. For massage parlors a concentration pattern is observed with instances of massage parlors in only two neighborhoods outside of the
75 ring road boundary. The other instances concentrate within the boundary as well as the affluent south and south-axis.
Spearman’s Rho Woz per sq/M Household Income % Higher Ed. Selling Points Beauty Parlor 0.327** -0.022 0.275*** Clothing Repair 0.088 -0.278** -0.0006 Electronic Repair 0.186* -0.08 0.067 Frame Maker 0.446*** 0.084 0.331** Craftmanship 0.417*** -0.062 0.312** Financial 0.179* -0.12 0.11 Massage Parlor 0.572*** 0.121 0.489*** Store Surface Beauty Parlor 0.237** -0.103 0.1804* Clothing Repair 0.126 -0.293** -0.0047 Electronic Repair 0.194* -0.072 0.075 Frame Maker 0.432*** 0.095 0.317** Craftmanship 0.401*** -0.085 0.282** Table 25: Spearman Correlation Test for Relationship between Gentrification and The Service Supply. (* = significant at the 0.010 level, ** = significant at the 0.05 level, *** = significant at the 0.001 level). Source: Locatus, 2019.
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Figure 25: Distribution of a Selection of Service Stores on City-Level.
10. Conclusion & Discussion The question that this research set out to answer is how residential gentrification affects the daily, retail, food and beverages, and services supply in Amsterdam neighborhoods that show various degrees of gentrification dynamics. This research question has been divided into four sections of the total retail supply: daily goods, retail stores, leisure establishments, and service stores. For each of these categories the effects of residential gentrification has been assessed through a process consisting of three components: (1) frequencies and share analysis of stores in the four neighborhoods, (2) testing correlation for selected observations using a Spearman’s correlation test and (3) mapping and analyzing the geographic distribution of these stores. For the daily goods and leisure categories, a limited in-category analysis was done that shows some differentiation in the four selected neighborhoods. We reiterate the results for each category before discussing the implications of our research.
10.1 The Effect of Gentrification on the Total Retail Supply First, for the total retail supply, we find no differences regarding the presence of chain stores between the gentrified and the non-gentrified cases. Therefore, hypothesis one, which expected increases in individually operated stores in gentrified neighborhoods, is rejected. Second, in the supply of the daily goods, an increased presence of wine- stores and deli’s is observed on the city-wide level. Regarding in-store differentiation, we observe more biological and less foreign-products supermarkets in the selected gentrified cases. These observations relate to hypothesis two, which expects an increase in local specialty stores such as wine-, bio-, and deli-stores. Second, regarding retail, an increased presence of second-hand clothing stores are observed. We find that in general, secondhand does not seem to be more present in gentrified neighborhoods. This extends to other stores such as hobby and craftmanship stores. No instances of in- store differentiation have been tested for the retail category. Third, for leisure, in gentrified neighborhoods a decreased presence of grillrooms is observed. The share of grillrooms increases moving towards the boundaries of the ring-road A10. In general,
77 an increase of F&B within leisure is observed, which relates to hypothesis three. We can however neither confirm nor reject this, as the increase in F&B is a broader trend in Amsterdam. For other types of stores, we find no concentration patterns. Regarding in-store differentiation, more popular cafés and restaurants are located in the selected gentrified neighborhoods. This confirms hypothesis four, which expected that more popular F&B establishments were to be located in gentrified neighborhoods. Furthermore, in these neighborhoods the share of personal training locations as part of the total fitness establishments also is higher. Both claims have not been tested on the city-wide level. Lastly, regarding the service supply, increased shares of massage parlors are observed in gentrified neighborhoods. There are only a few instances of such service provision in neighborhoods outside of the ring-road A10. Similar to the retail category, no instances of in-category differentiation have been researched.
10.2. Implications The results of the research have several implications for theory-building around both residential and commercial gentrification. It further confirmes that residential gentrification as a process does at some extent shape the contents of the commercial spaces in urban neighborhoods. In gentrified neighborhoods, different compositions of the retail space are observed than in their non-gentrified counterparts. Although much of the total retail supply seems unaffected, some types of shops are more frequent within gentrified neighborhoods. Besides simple quantitative change, more change also seems to locate itself in product and store differentiation between the various categories of the total retail supply. Both in the qualitative and quantitative change it exerts, these shaping properties illustrate how residential gentrification is not merely limited to households. It seems to also spill-over to the commercial spaces in neighborhoods, showing signs of both internal and external upgrading. The renewed economic interest in gentrifying neighborhoods in that sense also manifests itself in the reinvestment in commercial spaces. The rental prices map in Amsterdam shown in chapter two is illustrative of this: within the ring-road boundary, the increasing rental prices are indicative of increased interest in these locations. The commercial spaces in neighborhoods are therefore not only part of the process of residential gentrification,
78 but the internal and external upgrading are also part of the outcome of the process. It strengthens the claim for the importance of including the role of the commercial spaces and retail supply in analyses of residential gentrification.
10.3 Limitations This research has aimed to contribute to commercial gentrification literature through conducting an empirical, quantitative, micro-level analysis of the effects of residential gentrification on the retail supply in Amsterdam. This has provided an answer to the question of how residential gentrification affects the daily, retail, leisure and services supply especially in the four selected cases but also on the level of Amsterdam. For some type of stores within each category of the total retail supply, we find that these are more present in gentrified neighborhoods. Our analysis is however limited due to three reasons: (1) the absence of longitudinal data on the level of Amsterdam, (2) the lack of in-depth insights into the causal relationships, (3) the broad character of the total retail supply and (4) the inability to test for multiple cities.
The research has not been able to collect longitudinal data on the city-wide level for multiple years. This has therefore remained limited to 2019. With access to such longitudinal data, additional analysis into the change on the city-wide level would have become possible. The second limitation is the absence of in-depth insights into causal relationships. The research has shown instances of change in the four neighborhoods between 2005 and 2019 and related this to the broader total retail supply of Amsterdam in 2019 through statistical tests and geographical distributions. Yet, even with the availability of data on the individual store level, analyzing instances of how commercial gentrification exactly unfolds on the individual level remains difficult. Analyses of commercial gentrification are more suited on the aggregated level by which changes in frequencies can be explained. The third limitation concerns the broad scope which it has adopted. Although this has resulted in an analysis of the total commercial fabric of neighborhoods within Amsterdam and especially the four cases, it resulted in the inability of explaining how stores change within their respective categories. A qualitative component was hard to incorporate in the already extensive
79 qualitative research. A specific focus on one of the four categories, most likely leisure, would have enabled a more in-depth account of internal upgrading at the cost of analyzing external upgrading for the three other categories. The final shortcoming relates to the inability to compare results between multiple cities. If such an account were to be included this would greatly enhance the generalizing properties of the study. Now, we are reserved with making claims about the process as a whole, as our data has been limited to Amsterdam.
10.4. Recommendations Based on the conducted research and our limitations, we can provide some directions and recommendations for further research. Similar to what Bridge & Dowling (2001) argue, we would like to reiterate the importance of the micro-level of analysis of processes of commercial gentrification. Similar to its residential counterpart, the change within urban neighborhoods manifests itself in change on the level most close to the street: that of individual households and stores. The neighborhood level seems to be the most suited level to analyze these changes in urban areas. Strengthening the quantitative aspect of commercial gentrification literature would require more (1) longitudinal and (2) comparative analysis of geographic distributions. Both would require accurate data that allows such accounts of commercial gentrification analysis, which will remain a challenge for future research. Enhancing our understanding of the internal upgrading aspect of commercial gentrification would require more qualitative accounts. What makes stores locate themselves in specific neighborhoods and to what extent does displacement of stores occur in these? Further work could also be located in refining the potential of the Instagram tool for analysis of food and beverage establishments, through studies in other major urban areas to see for similar results.
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Appendix I. A. Stores and their Categorization in the Locatus Data
Category Sub-category Type of Store Vegetable/Fruit Baker Pies Toko Chocolate Coffee/Tea Delicatessen Cheese Mini-supermarket Night-Store Nuts .010 Daily Goods Poulterer Butcher 11. Daily Reform Liquor Store Wine-Store Supermarket Tobacco/Lecture Specialty Tobacco Fish Sweets Other Druggist/Health Store Perfume .020 Personal Care Hair Products Pharmacy .030 Department Store Department Store Bridal Clothing Ladies Fashion Unisex Fashion Mens Fashion Childrens Fashion .040 Fashion & Clothing Leather Fashion 22 – Fashion & Luxury Nightwear Accessories Textile-Market Fashion Department Store Leather Store .050 Leather & Shoes Shoe Store .060 Jewelry & Optician Jewelry
88 Optician Glass/Terracotta Home .070 Home & Luxury Linnen Gifts Cooking Store 080 Antique & Art Antique Toys Model-Building Sporting Store .100 Game & Sport Fishing Store Watersport Store Sport Specialty Electronics Photo/Film Craftmanship Wool/Hand-working .110 Hobby Coins/Stamps Music Instruments Sowing Machines Fabric Bookstore Software/Games .120 Media Office Supplies Book & Office Ink-filling Flowers/Plants .130 Flower & Animals 35 – Spare Time Animal Care Radio & TV Computers .150 Brown & White Goods Telecom Kitchen Appliances Electronics Car Materials .160 Transport Bikes Scooters Building Market Building Materials Wood .170 DIY Iron & Tools Sanitary Painting Baby Living Store Sleeping Room Kitchens .180 Living Furniture Bathing Room Light
89 Floors Decoration Living Textile Curtains Secondhand Diverse Secondhand Clothing Secondhand Books Army Dump Party Paramedical 38 Other Retail .200 Other Retail Hearing Aids Smartshop Growshop Odd-Shop Furnaces Nature Stone Non-Food Other Café Coffee Hoouse Sex/Nightclubs Fastfood Delivery/Take-Away Grillroom/Shoarma Hotel .210 Food & Beverages Hotel-Restaurant Ice Cream Parlor Lunchroom Café-Restaurant Restaurant Partycentre Other F&B Cinema 59 – Leisure Gallery .220 Culture Art Loan Museum Theatre Amusement Hall Pool Indoor Playing Area Bowling Fitness .230 Leisure Climbing Sauna Tanning Swimming Pool Trampoline Other Amusement 65. Services .250 Rental Services Car Rental
90 Bike Rental Tool Rental Other Rental Smith Key Repair Hairdresser Tattoo Parlor Beauty Parlor Clothing Repair Upholstery .260 Craftmanship Services Clothing Creation Electronics Repair Copyshop Animal Trimmer Photographer Frame Maker Other Craftmanship Financial Intermediary Insurances .280 Financial Bank Gold Buy/Sell Other Calling/Internet Realtor Car Wash Car Polish .290 Other Massage Parlor Dry-cleaner Traveling Agency Work Agency Other Services
Appendix II.
91 2.3A: Division of types of shopping areas within the Netherlands. Source: Platform 31 (2014); Evers et al (2011). % of Shopping Area Description/Typology which Chains Central Shopping Area Most Important Shopping Area in a City 35 >400 shops, top 17 shopping areas of the Netherlands, city- 31 Binnenstad centre of the bigger cities. Hoofdwinkelgebied (groot) 200-400 shops, city-centre of medium-large cities. 39 Hoofdwinkelgebied (klein) 100-200 shops, city-centre of smaller cities. 41 Kernverzorgend 35 50-100 shops, centrum of big towns. winkelgebied (groot) Kernverzorgend 29 5-50 shops, centrum of big towns. winkelgebied (klein) Kernverzorgend 45 3-4 shops, one supermarket >500m2 supermarktcentrum Ondersteunend One or more supporting shopping areas besides one 33 Winkelgebied centrally emplaced area. >50 shops. Co-exists with the inner-city or 61 Stadsdeelcentrum hoofdwinkelcentrum. Mostly planning-based development. Binnenstedelijke >50 shops, supporting shopping streets in larger cities, not 21 Winkelstraat developed through planning. Wijkcentrum (groot) 25-50 shops 32 <25 shops. Shopping areas with 10-25 shops, or shopping 35 Wijkcentrum (klein) areas with 5-10 shops and 2 supermarkets. Buurtcentrum 5-9 shops, with or without a supermarket. 33 Supermarketcentrum 3-4 shops, including 1 supermarket with >500m2 surface. 50 Overige Winkelgebieden 54 Concentratie Grootschalige >5 shops, surface >500m2, of which >50% big products: 56 Winkels furniture etc. Shopping areas surrounding train stations or with a specific 46 Speciaal Winkelgebied theme.
5a: Selling Points for the four neighborhoods and the MSA. Source: Locatus, 2019. Selling Points Amsterdam De Pijp Rivierenbuurt Bos & Osdorp
92 2019 (N=97) Lommer Total 7242 1034 387 299 385 % Individual - 87,4 81,7 84,8 72,8 Vacancy 216 29 12 22 20 Daily Goods 1369 127 75 54 70 Retail 2229 288 82 67 124 Leisure 2163 378 114 93 94 Services 1511 212 104 63 77 As % of total Vacancy 2,9 2,8 3,1 7,4 5,2 Daily Goods 18,3 12,3 19,4 18,1 18,2 Retail 29,8 27,9 21,2 22,4 32,2 Leisure 28,9 36,6 29,5 31,1 24,4 Services 20,2 20,5 26,9 21,1 20
5b: Change in the total distribution and the categories in the four cases. Source: Locatus, 2019.
Change 05-19 De Pijp Rivierenbuurt Bos & Lommer Osdorp Total -31 -4 27 14 Vacancy 1 3 14 13 Daily Goods -7 -10 -11 -1 Retail -107 -29 -1 -48 Leisure 57 26 37 45 Services 25 6 -12 5 As % of Total Vacancy +0,1 +0,8 +4,7 +3,4 Daily Goods -0,7 -2,6 -3,7 -0,3 Retail -10,4 -7,5 -0,3 -12,5 Leisure +5,5 +6,7 +12,3 +11,7 Services +2,4 +1,5 -4 +1,3
6a: Frequency table for daily selling points in the four cases in 2019. First number in column is share of percentage, second number is the absolute amount. Source: Locatus (2019). 2019 Daily Goods De Pijp Rivierenbuurt Bos & Osdorp Amsterdam Lommer Total Daily 127 62 45 57 1126
93 Individual Store 65,4 (83) 62,9 (39) 77,9 (35) 68,4 (39) - Vegetables/Fruit 3,1 (4) 8,1 (5) - 1,8 (1) 3,55 (40) Baker 13,4 (17) 14,5 (9) 28,9 (13) 19,3 (11) 12,5 (141) Chocolate 0,8 (1) 1,6 (1) - 1,8 (1) 2,8 (31) Delicatessen 5,5 (7) 6,5 (4) 2,2 (1) 1,8 (1) 5,0 (56) Cheese 1,6 (2) 4,8 (3) - - 3,8 (43) Night-Store 1,6 (2) 1,6 (1) 2,2 (1) 5,3 (3) 2,8 (32) Nuts 0,8 (1) 1,6 (1) 2,2 (1) 1,8 (1) 1,1 (12) Poulterer 0 (0) 1,6 (1) - - 0,6 (7) Butcher 7,1 (9) 8,1 (5) 8,9 (4) 8,8 (5) 6,2 (70) Liquor Store 7,1 (9) 6,5 (4) 2,2 (1) 7 (4) 7,6 (85) Wine Store 4,7 (6) 3,2 (2) - - 3,8 (43) Supermarket 11 (14) 19,4 (12) 20 (9) 17,5 (10) 13 (146) Tobacco 10,2 (13) 14,5 (9) 11,1 (5) 7,0 (4) 13,1 (147) Fish 3,9 (5) 6,5 (4) 6,7 (3) 8,8 (5) 6,8 (76) Toko Shop 4,7 (6) - 4,4 (2) 3,5 (2) 2,8 (31) Coffee/Tea 2,4 (3) - - - 1,8 (20) Mini-Market 3,1 (4) - 11,1 (5) 12,3 (7) 8,6 (97) Reform 0,8 (1) 1,6 (1) - 1,8 (1) 1,8 (20) Other 1,6 (2) 1,6 (1) - 1,8 (1) 0,9 (10)
6b: Change in the share of daily selling points in the four cases 2005-2019. Source: Locatus (2019). Change 05-19 De Pijp Rivierenbuurt Bos & Lommer Osdorp Total Daily +16,5 -11,4 -21,1 -6,6 Individual Retail -9,9 -7,1 -1,2 -7 Vegetables/Fruit +2,2 -3,4 -3,5 -8,1 Baker -5,9 +0,2 +11,3 -2 Chocolate +0,8 +0,2 -1,8 +0,1 Delicatessen - +2,2 +2,2 +1,8
94 Cheese -2,1 -2,3 0 -1,6 Night-Store +1,6 +0,2 +2,2 +5,3 Nuts -0.1 +0,2 +2,2 +0,1 Poulterer -0,9 +0,2 -1,8 -0,0 Butcher -4,8 -3,4 -1,6 -2,7 Liquor Store -2,1 +0,7 -1,3 +2,1 Wine Store +4,7 +3,2 - - Supermarket +3,7 +7,9 +7,7 +4,4 Tobacco +0,1 +0,2 -8,2 -2,8 Fish -0,7 +3,6 +4,9 +0,6 Toko Shop -7,2 - +0,9 -1,4 Coffee/Tea +1,4 - - - Mini-Market -6,9 -8,6 -13,5 +5,7 Reform -1 +0,2 - +0,1 Other -1,2 +0,2 - -1,5
7a: Frequency table for retail selling points 2019. First number in column is share of total retail selling points, second is absolute numbers. Source: Locatus, 2019. Retailscape MSA (N=89) De Pijp Rivierenbuurt Bos & Lommer Osdorp Total 2229 280 79 61 113 % Individual - 84,7 81,7 88,1 60,5 Clothing & Fashion 18,2 (405) 31,1 (87) 13,9 (11) 14,8 (9) 24,8 (28) Shoes and Leather 3,5 (77) 5,4 (15) 5,1 (4) 1,6 (1) 7,1 (8) Jewelers & Opticians 5,9 (131) 5,7 (16) 7,6 (6) 4,9 (3) 9,7 (11) Household & Luxury 6,4 (143) 5,0 (14) 3,8 (3) 4,9 (3) 7,1 (8) Antique & Art 2,5 (56) 0,7 (2) - - - Sport & Games 5,5 (123) 4,6 (13) 5,1 (4) 6,6 (4) 4,4 (5) Hobby 3,9 (87) 6,1 (17) 3,8 (3) 1,6 (1) 1,8 (2) Media 5,1 (113) 3,2 (9) 3,8 (3) 3,3 (2) 0,9 (1) Plant & Animal 7,5 (168) 3,9 (11) 17,7 (14) 8,2 (5) 4,4 (5) Brown & White 4,9 (110) 5,4 (15) 2,5 (2) 6,6 (4) 10,6 (12) Goods Car & Bike 6,8 (152) 4,3 (12) 11,4 (9) 14,8 (9) 6,2 (7) DIY 3,6 (80) 2,9 (8) 5,1 (4) 4,9 (3) 2,7 (3) Home 11,8 (264) 8,2 (23) 10,1 (8) 16,4 (10) 14,2 (16) Secondhand 6,1 (135) 4,3 (12) 7,6 (6) 4,9 (3) 1,8 (2) Other Retail 8,3 (185) 9,3 (26) 2,5 (2) 6,6 (4) 4,4 (5) 7b: Change in the share of retail selling points 2005-2019. Source: Locatus, 2019. Change in promille De Pijp Rivierenbuurt Bos & Lommer Osdorp Total -27,1 -26,1 -1,5 -27,9 % Individual +3,2 -3 +4,2 +1,8 Clothing & Fashion +5,7 -3,7 +4,6 -1,3 Shoes and Leather +0,9 +1,3 +1,5 -2,9 Juwelers & Opticians +1,5 +2,8 -1,4 +3,1 Household & Luxury -1,5 -3,5 -1,4 +3,1 Antique & Art -1,1 -3,6 - - Sport & Games +1,2 +2,2 +0.1 - Hobby -1,7 -1,7 -4,4 -1,3
95 Media -1,4 +1 -1,4 -4,4 Plant & Animal - +2,7 -2,8 -1,2 Brown & White Goods -1,9 -3 +0,1 +3,9 Car & Bike +0,6 +4,7 +7,6 +1 DIY - -0,5 +1,5 +0,1 Home -1,9 +2,5 -2,7 +0,7 Secondhand -0,1 +1,9 +3 +1 Other Retail +1,7 -2,1 -2,9 -2,9 Automotive -2,1 -1,2 -1,4 +2,3 Fuel +0,1 +0,3 - +0,2
8a: Frequency table for leisure-related selling points 2019. Source: Locatus, 2019. MSA De Pijp Rivierenbuurt Bos & Lommer Osdorp (N=99) Total 2114 378 114 93 94 Individual SP. - 348 100 85 79 % Individual - 92,1 87,7 91,4 84 F&B 80,4 93,1 92,1 (105) 90,3 (84) 89,4 (84) (1740) (352) Culture 8,2 (178) 4,2 (16) 0 (0) 1,1 (1) 2,1 (2) Leisure 11,3 (245) 2,6 (10) 7,9 (9) 8,6 (8) 8,5 (8) Food & Beverages Café 7,8 (164) 10,3 (39) 7 (8) 9,7 (9) 5,3 (5) Coffeehouse 0,9 (19) 0,3 (1) 0 (0) 1,1 (1) 1,1 (1) Coffeeshop 4,6 (97) 4,5 (17) 2,6 (3) 1,1 (1) 1,1 (1) Fastfood 9,1 (192) 8,2 (31) 5,3 (6) 10,8 (10) 14,9 (14) Takeaway 7,9 (167) 9,5 (36) 9,6 (11) 14,0 (13) 13,8 (13) Grillroom/Shoarma 4,2 (89) 1,9 (7) 0,9 (1) 10,8 (10) 8,5 (8) Hotel 4,8 (101) 3,4 (13) 3,5 (4) 2,2 (2) 3,2 (3) Ice Cream Parlor 4,1 (86) 1,6 (6) 4,4 (5) 1,1 (1) 2,1 (2) Lunchroom 9,5 (201) 12,7 (48) 13,2 (15) 15,1 (14) 14,9 (14) Café-Resto 10,4 (219) 11,1 (42) 9,6 (11) 7,5 (7) 5,3 (5) Restaurant 11,6 (246) 28,8 36 (41) 15,1 (14) 17 (16) (109) Other 1,6 (33) 0,8 (3) 0 (0) 2,2 (2) 2,1 (2) Leisure Fitness 5,6 (118) 2,1 (8) 4,4 (5) 4,3 (4) 3,2 (1)
8b: Change in leisure-related selling points 2005-2019. Source: Locatus, 2019. Change De Pijp Rivierenbuurt Bos & Lommer Osdorp Total +17,8 +29,5 +66,1 +91,8 % Individual -4,8 -12,3 -5 -9,8 F&B -0,02 -4,49 -4,32 -0,43 Culture -2,0 - -0,7 -4,0 Leisure +2,1 +4,5 +5 +4,4 Subcategorized in Café -12,4 -10 -22,5 -11 Coffeehouse -1 -1,1 +1,1 +1,1 Coffeeshop -2,4 -0,8 -2,5 -1,0
96 Fastfood +2,9 +0,7 -14,2 -1,4 Takeaway -1,4 +4 +5 -2,5 Grillroom/Shoarma -2,8 -2,5 +1,8 +6,5 Hotel +1,6 +0,1 +2,2 -0,9 Ice Cream Parlor +1,3 +2,1 +1,1 +2,1 Lunchroom +7,4 +2,9 +9,7 +10,8 Café-Resto +11,1 +9,6 +7,5 +5,3 Restaurant -4,2 -7,2 +4,3 -5,4 Other +0,8 - +2,2 +2,1 Leisure Fitness +1,8 +2,1 +2,5 -0,9
9A: Frequency table for service related selling points in 2019. Source: Locatus (2019). Services MSA (N=99) De Pijp Rivierenbuurt Bos & Lommer Osdorp Total Services 1511 212 104 63 77 Rental 5,8 (87) 2,8 (6) 0 (0) 6,3 (4) 2,6 (2) Craftmanship 55 (831) 67,5 (143) 62,5 (65) 55,6 (35) 59,7 (46) Financial 6,8 (103) 4,2 (9) 3,8 (4) 9,5 (6) 18,2 (14) Consumer 32,4 (490) 25,5 (54) 33,7 (35) 28,6 (18) 19,5 (15) Craftmanship Smith 1,5 (23) 0,5 (1) 1 (1) 0 (0) 0 (0) Key Repair 3,4 (51) 1,9 (4) 3,8 (4) 0 (0) 2,6 (2) Hairdresser 15,5 (234) 28,8 (61) 31,7 (33) 33,3 (21) 33,8 (26) Tattoo Parlor 2,5 (38) 1,9 (4) 0 (0) 0 (0) 0 (0) Beauty Parlor 10,3 (156) 11,3 (24) 11,5 (12) 11,1 (7) 11,7 (9) Clothing Rep. 5,8 (88) 3,8 (8) 4,8 (5) 3,2 (2) 7,8 (6)
97 Upholstery 1,1 (16) 2,8 (6) - - - Electr. Repair 2,3 (34) 3,8 (8) 1,9 (2) 1,6 (1) 1,3 (1) Copy-Shop 2,3 (35) 1,4 (3) 0 (0) 1,6 (1) 0 (0) Photographer 1,5 (22) 0,5 (1) 1,9 (2) 3,2 (2) 1,3 (1) Animal Trim 0,7 (10) 0 (0) 1,1 (1) 0 (0) 0 (0) Framer 1,1 (17) 1,9 (4) 1 (1) 0 (0) 0 (0) Other Craftman 4,8 (72) 8,0 (17) 2,9 (3) 1,6 (1) 1,3 (1) Consumer Calling/Internet 1,9 (28) 1,4 (3) 0 (0) 4,8 (3) 2,6 (2) Realtor 6,3 (95) 5,7 (12) 11,5 (12) 1,6 (1) 6,5 (5) Car Wash 0,8 (12) - - - 1,3 (1) Car Polish 0,6 (9) - - 1,6 (1) - Massage Parlor 5,2 (79) 6,1 (13) 8,7 (9) 1,6 (1) 1,3 (1) Drycleaner 6,3 (95) 4,7 (10) 7,7 (8) 6,3 (4) 2,6 (2) Traveling Agcy. 3,6 (54) 2,4 (5) 2,9 (3) 6,3 (4) 2,6 (2) Work Agcy. 3,2 (48) 3,3 (7) 1,9 (2) 3,2 (2) 1,3 (1) Other Consumer 3,8 (58) 1,9 (4) 1 (1) 3,2 (2) 1,3 (1)
9A: Change in service selling points 2005-2019. Source: Locatus, 2019. Service Change De Pijp Rivierenbuurt Bos & Lommer Osdorp Total Services +13,4 (25) +6,1 (6) -16 (-12) +6,9 (5) Rental -2,5 (4) -4,1 (4) -0,3 (-1) -4,3 (-3) Craftmanship +14,5 (44) +4,3 (8) +3,6 (-4) +16,7 (15) Financial 0 (1) -7,4 (-7) +2,9 (1) +7,1 (6) Consumer -12 (-16) +7,1 (9) -6,1 (-8) -19,4 (-13) Craftmanship Smith -1,1 (-2) -0,1 (0) -1,3 (-1) 0 (0) Key Repair 0,3 -1,3 -1,3 -0,2 Hairdresser +4,2 (15) -1,9 (0) +2,7 +12,9 Tattoo Parlor +0,8 -1,0 - - Beauty Parlor +7,6 +7,5 +9,8 +7,5 Clothing Rep. -0,5 +1,7 -0,8 +5,0 Upholstery -2 -2 -2,7 0
98 Electr. Repair +3,2 +1,9 +0,3 +1,3 Copy-Shop +0,3 - +0,3 -2,8 Photographer - +0,8 +3,2 -0,1 Animal Trim +1,9 +1 - - Framer +6,4 +1,9 +1,6 +1,3 Other Craftman -1,8 -2,0 +0,8 +1,2 Consumer Calling/Internet -5 - -7,2 -3 Realtor +2,5 +2,4 +1,6 +0,9 Car Wash - - - - Car Polish - - +1,6 - Massage Parlor +6,1 +8,7 +1,6 +1,3 Drycleaner -0,1 +2,6 +1 +1,2 Traveling Agcy. -8,3 -2,2 -1,7 -5,7 Work Agcy. -6,9 -3,2 -4,8 -11,2 Other Consumer -0,3 -0,1 +1,8 -1,5
99