Examining the Effect of Squatter Settlements in the Evolution

International Journal of Geo-Information

Article Examining the Effect of Squatter Settlements in the Evolution of Spatial Fragmentation in the Housing Market of the City of Buenos Aires by Using Geographical Weighted Regression

A. Federico Ogas-Mendez * and Yuzuru Isoda

Graduate School of Science, Tohoku University, 6–3, Aramaki Aza Aoba, Aoba, Sendai 980-0845, Miyagi, Japan; [email protected] * Correspondence: [email protected]

Abstract: The spatial fragmentation in the housing market and the growth of squatter settlements are characteristic for the metropolitan areas in developing countries. Over the years, in large cities, these phenomena have been promoting an increase in the spatial concentration of poverty. Therefore, this study examined the relationship between the squatter settlement growth and spatial fragmentation in the housing market of Buenos Aires. By performing a spatiotemporal analysis using geographically weighted regression in the house prices for the years 2001, 2010, and 2018, the results showed that while squatter settlements had a strong negative effect on house prices, the affected areas shifted over time. Our findings indicate that it is not the growth of the squatter settlement that causes spatial fragmentation, but rather the widening income disparities and further segregation of low- income households. However, squatter settlements determined the spatial demarcation of fragmented housing market by attracting low-income households to surrounding low house price areas. Citation: Ogas-Mendez, A.F.; Isoda, Y. Examining the Effect of Squatter Keywords: spatial fragmentation; squatter settlements; geographical weighted regression; spatiotem- Settlements in the Evolution of poral analysis; segregation Spatial Fragmentation in the Housing Market of the City of Buenos Aires by Using Geographical Weighted Regression. ISPRS Int. J. Geo-Inf. 2021, 10, 359. https://doi.org/10.3390/ 1. Introduction ijgi10060359 In the past decades, many metropolitan areas have experienced the continuity of their traditional urbanization patterns, but in parallel, new trends associated with real Academic Editor: Wolfgang Kainz estate speculation have emerged [1]. From the 1990s, the “new urban policy” approach has promoted deregulation of private investments into the real estate market. This deregulation Received: 15 March 2021 led to an increased number of decisions in the private sector regarding urban change and Accepted: 20 May 2021 development [2,3]. This policy is related to large-scale redevelopment featuring commodity Published: 23 May 2021 housing, public services, and commercial and office space. It also includes the city center redevelopment in response to restructuring the processes associated with transforming of Publisher’s Note: MDPI stays neutral the production and demand in different scales combining physical upgrading with socio- with regard to jurisdictional claims in economic development objectives by replacing the more traditional redistribution-driven published maps and institutional affil- approaches [3]. The consequences of socio-economic changes deepened social disparities iations. and increased spatial fragmentation [4]. The term fragmentation is originally used in ecology to capture a loss of continuity in a particular kind of habitat regarding a loss of connectedness to the external environment [5]. In urban studies, spatial fragmentation addresses land use activities’ discordance and the physical properties of space. The spatial Copyright: © 2021 by the authors. fragmentation in the urban landscape can undermine integration and limit interactions Licensee MDPI, Basel, Switzerland. among urban dwellers. The fragmentation process is considered to be aligned with the This article is an open access article increase in inequalities [6]. The spatial econometric estimates suggest that house prices are distributed under the terms and negatively impacted by spatial fragmentation at low fragmentation levels, yet there is a conditions of the Creative Commons positive price relationship at high fragmentation levels [7]. The fragmentations expose the Attribution (CC BY) license (https:// rise of inequalities, suggesting breakup of neighborhood types, which culminates in the creativecommons.org/licenses/by/ spatial segregation of social groups. 4.0/).

ISPRS Int. J. Geo-Inf. 2021, 10, 359. https://doi.org/10.3390/ijgi10060359 https://www.mdpi.com/journal/ijgi ISPRS Int. J. Geo-Inf. 2021, 10, 359 2 of 18

One of the main reasons for this fragmentation is the perception of violence and crime inside the city, which is related to the deterioration of living conditions in certain parts of the city, typically described as squatter settlements and slums [8]. These settlements are multiple exclusion sites, both materially and socially, and they are stigmatized spaces, associated with criminal activities. Their association with illegal activities leads to negative perceptions of the squatter settlements as a no-go zone by a part of the population outside these enclaves and also being spatially segregated from the rest of the city. They are also vulnerable to communicative diseases including COVID-19 of the ongoing pandemic [9], posing a threat to public health. These perceptions influence the housing market, lowering the neighborhood’s property prices [10]. The consequence of spatial segregation leads to an uneven distribution of the city’s services, infrastructure, and investments. Although the spatial fragmentation exposes the rise of inequalities, the interaction between neighborhoods continues. However, in spatial segregation, this interaction is almost absent, particularly in squatter settlements. In many metropolitan areas in developing countries, these kinds of settlements have increased in number and size. In 2010, a total of 872 million people lived in squatter settlements and slums [11]. That growth is linked with the increase of spatial fragmentation and segregation within the city. Understanding the relational geographies and revealing the causes of the gradual proliferation of the spatial fragmentation and the evolution of the phenomenon allows us to define the possible responses to reduce these disparities. This article aims to contribute to this knowledge base by assessing how squatter settlements are one of the causes of socio-spatial fragmentation and their dynamic effect on house prices over time. This paper begins with a literature review concerning spatial fragmentation and segregation in the housing market, leading to our hypothesis that effects of squatter settlements on house prices promote gradual spatial fragmentation. Subsequently, we introduce our study area, the City of Buenos Aires (CBA), where rising disparities in house prices within the city exemplify spatial fragmentation. The methods section describes how geographical weighted regression (GWR) in multiple time slices is used to measure the different effects of squatter settlements across space and their changes over time. After presenting the results, the discussion section examines our hypothesis and identifies areas that the fragmentation has been concentrated concerning house prices.

2. Literature Review Fragmentation is a multidimensional concept with different characteristics [12]. Differ- ent terms and concepts are used as synonyms or notions closely related to fragmentation, such as spatial separation, spatial polarization, social-spatial exclusion, and disconnected cities [13]. Although fragmentation is repeatedly emphasized in urban studies, the concept is still in its infancy. This deficiency is mainly due to the term “fragmentation” being associated with several socio-spatial phenomena without a specific definition. Terms such as urban fragmentation, dual city, disconnected city, illegal and legal city, city of walls, and divided city are also used to describe the fragmentation in urban areas [6,14,15]. They explain that dualizing space and society in the metropolis increases the polarization between wealthy and poor social sectors in urban areas. The housing market is central in reproducing social inequalities [16]. This is strongly reflected in the housing and home-ownership concentration, which is becoming reserved for the wealthy social sectors [17]. In this context, housing markets are inherently spatial, and current housing-market developments reveal substantial variation between neighborhoods; these inequalities promote spatial fragmentation. The consequences of this fragmentation result in the proliferation of a dual urban space with well-equipped business and residential areas on the one hand and areas of the city that are ill integrated within the urban structure on the other. This spatial fragmentation is a dynamic process that involves a fracture, and social separation in space, which is reflected in the emergence of closed or similar neighborhoods located transversely in the city [18]. This process is outlined by increased social homogeneity in the local neighborhoods’ social composition and “frag- ISPRS Int. J. Geo-Inf. 2021, 10, 359 3 of 18

mented” cities where slums surround high-income neighborhoods. Taubenböck et al. [19] gave an example of this division in urban areas by estimating the digital access of informal areas of the city and poor areas by using remote sensing and Twitter data. The results from eight cities show that residents of the slums are digitally left behind compared to residents of formal settlements. Michael Janoschka [20] emphasized a break in the Latin American cities, which have been traditionally open and marked by public spaces, giving way to extremely segregated and divided cities. Although the concept of fragmentation has similarities with segregation, we should not confuse fragmentation with segregation. By socio-spatial segregation, we mean the geographical dimensions of social inequality, polarization, and exclusion [21,22], whereas with socio-spatial fragmentation, we mean the increase of the spatial inequalities, but this does not necessarily imply an exclusion or segregation of specific social groups. The segregation shaping the isolation of neighborhoods and the processes shaping that isolation and the reactions deriving from it are inherently and spatially represented in that configuration. Consequentially, communities such as squatter settlements are less able to physically interact with different social groups, which stigmatizes their inhabitants. This segregation highlights the differences resulting from an overlapping of different spaces and increased visibility to the differences, creating an archipelago society [23]. The concept of segregation brings us to the duality concerning the formal and informal city institutional domains. Sabatini et al. [24] identified a change in their geographical scale in Chilean cities’ residential segregation patterns. The authors identified two main scales: large-scale poverty areas and a noticeable agglomeration of high-income groups in the periphery; and small-scale poverty areas, consisting of homogeneous neighborhoods arranged alternately in urban space. The geographic scale of segregation is decreasing in the areas of largest private real estate dynamism, whereas it is increasing in areas where new low-income families are settling. In other words, the intensity of segregation decreases on an aggregated geographic scale and is intensified on a smaller scale. This duality is also represented in the formal and informal city’s institutional domains. Squatter settlements originate in certain segments of the population appropriating the space by squatting open lands, public or private, for personal use. From that moment, the inhabitants of squatter settlements re-signify the urban territory symbolically and physically. This re-signification enters into contradiction with the rest of the city. They become perceived as blighted areas, creating a gap within the city, as Hardoy and Sat- terthwaite [25,26] call them “legal and illegal city”. The illegal city is associated with a constructed territory that does not follow the rest of the city’s norms. Thus, the legal city promotes the segregation of the illegal city by exclusion and stigmatization. However, these two regions enter in contact, overlapping with different norms, where decisions are taken outside the formal regulation and are tolerated by the institution as a way of coexistence. One of the main factors that promote socio-spatial fragmentation is crime perception, which is strong and negatively associated with the neighborhood quality and is detrimental to the property values [27]. Buonanno et al. [28] performed a hedonic analysis using data from the housing market and victimization survey in the city of Barcelona from 2004 to 2006 to estimate the crime perception effect on house prices. The authors found that crime exerted hidden costs beyond its direct costs. The perceived level of security in the district had a positive impact on the district’s hedonic price. Meanwhile, crime perception was negatively correlated with it. Geoghegan, Wainger, and Bockstael [29] suggested that increasing diversity might lower property values by introducing negative visual and noise externalities. Yet, diversity may also provide convenient access to work, shopping, and recreational activities. However, many recent works enhance the role of the mixed-income developments, highlighting the affluence of middle income by ensuring equitable access to various neighborhood amenities and opportunities for assisted households to promote poverty deconcentration [30–32]. Increased fragmentation results in a more checkered landscape with potentially con- flicting neighboring land use activities. The fragmentation is not static but dynamic and ISPRS Int. J. Geo-Inf. 2021, 10, 359 4 of 18

evolving. Coy [33] makes an explanation of the expansion of gated communities in Brazil- ian cities. The author explained that the rise of spatial fragmentation was during four phases associated with increased city violence. In that research, security and crime perception were stronger in the higher quantiles of the district hedonic price distribution. Insecurity causes a larger price reduction in more highly valued areas. Squatter settlements are major factors promoting socio-spatial fragmentation in the developing metropolitan areas. These types of settlements have a nuisance effect, which futher promotes this phenomenon. Hussain et al. [9] explored the negative impact of slum proximity on property prices and rentals and found that rent declined as one moved closer to the slums. Song and Zenou [34] investigated whether the proximity to urban villages in China negatively affected house prices and found that they increased as the nearest urban village’s distance increased. Chen and Jim [35] analyze the urban village influence on house price using the three-dimensional model (availability, accessibility, and visibility). Their results indicated that the nuisance effect differed depending on the dimension type, having a more substantial negative effect through visibility than through the availability of urban villages. Zhang and Zhao [36] showed the effect of the informal housing market in the urban villages in China, where the variances in ability between villages lead to heterogeneity of tenure security, thus creating price differentials in the market. In this context, higher tenure security means an increase in house prices. Henderson, Regan, and Venables [37] developed a model of the city’s built environment growing in Nairobi by distinguishing between the formal and slum construction. The formal sector is more dynamic and involves investment decisions based on expected future rents. In contrast, in case of the informal construction, the buildings are built low, with high-land intensity. The slums volume increases through time, not by building taller houses, but by increasing crowding and already high cover-to-area ratios. In this context, the model’s dynamic predicts that as house prices rise, there should be the ongoing conver- sion of older slums to formal sector use; meanwhile, new slum areas will be expanded in the city’s edge. Many empirical studies have theoretically suggested the effect of certain facilities in house price depreciation by conducting a spatiotemporal analysis [38–40]; however, there is a lack of studies that examine the spatiotemporal effect of squatter settlements on house prices and how to promote or discourage the spatial fragmentation in cities. In this research, we consider two types of the spatial division process. Spatial segregation is the territorial intra-urban structure defined between the squatter settlements and the rest of the city. Interactions with different neighborhoods do not characterize this segregation. In contrast, socio-spatial fragmentation is the spatial intra-urban structure marked by spatial inequalities, although fragmentation is defined as an urban social fracture, still retaining the interaction between neighborhoods. While theoretical debates over urban structures have existed in the literature review, no empirical studies have tested the dynamic role of squatter settlements in spatial fragmentation, to the best of our knowledge. Here, we examine the case of the CBA in the period 2001–2018 because the house price disparities increased tremendously during that period, with the rapid growth of squatter settlements. Our hypothesis is that the growth of squatter settlements increases the house price disparities. This research aimed to give a comprehensive description of the CBA’s socio-spatial fragmentation from 2001 to 2018 that associates house prices with the squatter settlements’ location.

3. Study Area This paper considers the case of CBA, with a population of 2.89 million, which makes it one of South America’s biggest cities. The city itself is divided into 15 comunas (communes) (Figure1). The CBA is characterized by de-industrialization and transformation into an administrative and business services center. This change in the economic organization has profoundly restructured the city’s labor market and urbanism projects that transform the city, changing the CBA’s social composition. ISPRS Int. J. Geo-Inf. 2021, 10, 359 5 of 18

3. Study Area This paper considers the case of CBA, with a population of 2.89 million, which makes it one of South America’s biggest cities. The city itself is divided into 15 comunas (communes) (Figure 1). The CBA is characterized by de-industrialization and transformation into an administrative and business services center. This change in the economic organi- ISPRS Int. J. Geo-Inf. 2021 10 , , 359 zation has profoundly restructured the city’s labor market and urbanism projects 5 that of 18 transform the city, changing the CBA’s social composition.

FigureFigure 1. 1. CadastralCadastral map map of of the the CBA CBA..

SSquatterquatter settlements settlements started started emerging emerging in in CBA CBA during during the the 1930s. 1930s. Most Most of of the the population population thenthen were were internal internal immigrants, immigrants, stigmatized stigmatized for for their their rural rural background background [ [4411]].. Those Those informal informal settlementssettlements were were tolerated tolerated until until the themilitary military regime regime,, which which came cameinto power into powerin 1976, in initial- 1976, izedinitialized city’s socio city’s-economic socio-economic restructuring. restructuring. This process This includes process the includes relocation the of relocation productive of productive activities and squatter settlements outside the CBA [42]. This “cleansing” of the activities and squatter settlements outside the CBA [42]. This "cleansing" of the city forcedly city forcedly evicted an estimated 208,783 squatter settlement dwellers [43]. In 1983, with evicted an estimated 208,783 squatter settlement dwellers [43]. In 1983, with the return of the return of democracy, squatter settlements’ population resumed growing (Table1). democracy, squatter settlements’ population resumed growing (Table 1). Table 1. Population in the squatter settlements in CBA, 1960–2010. Data from INDEC [44,45], DGEyC [46], and TECHO [47]. Table 1. Population in the squatter settlements in CBA, 1960-2010. Data from INDEC [44,45], DGEyC [46], and TECHO [47]. Squatter Settlement CBA Share of Squatter Settlement Population to Year Growth Rate (%) Squatter(Persons) Settlement CBA(Persons) Share of Squatterthe Total Settlement Population Population in CBA to (%) the Total Year Growth Rate (%) 1960(Persons 34,430) -(Persons 2,966,634) Population in 1.2 CBA (%) 19601970 101,00034,430 193.3- 2,966,634 2,972,453 1.2 3.4 19701980 37,010101,000 193.3−66.2 2,972,453 2,922,829 3.4 0.9 1991 52,608 42.4 2,965,403 1.8 1980 37,010 −66.2 2,922,829 0.9 2001 107,422 104.1 2,776,138 3.9 1991 52,608 42.4 2,965,403 1.8 2010 170,054 58.3 2,890,151 5.9 20012018 292,732107,422 * 104.1 72.1 2,776,138 3,068,043 *3.9 9.5 2010 170,054 58.3 2,890,151 5.9 * Estimate values. 2018 292,732 * 72.1 3,068,043 * 9.5 Meanwhile, the* policies Estimate implemented values. during the 1990s aggravated almost all socio- economic indices, such as income disparity, real incomes, unemployment, and underem- ployment [48]. During this period, the squatter settlement population increased by more than 104%, having a signiﬁcant inﬂow of international migration from bordering countries. In 2001, Argentina experienced an intense economic and political crisis, which brought a total collapse of the economic and political system. Since 2001, the squatter settlement population has increased by approximately 10,000 inhabitants every year. In this crisis, the decrease in the banking system’s stability and the loss of trust in the monetary market opened the gates to real estate speculation. ISPRS Int. J. Geo-Inf. 2021, 10, 359 6 of 18

After a fall in house prices in 2001, the housing market experienced a rapid increase in house prices due to the asset boom [49] and widening house price disparities within the city. From 2003, the country’s economy began to recover at an annual growth rate of 8% [45,47]. Along with economic growth, there was a boom in the real estate sector that sharpened prices (see Table2), impeding low-income sectors from accessing the formal housing market. In this context, the real estate market became attractive with potentially high rent, low construction cost, and low interest rates [50]. The investment has been concentrated in the CBD, and extended to the north and historic city center in the southeast. These new projects and revitalized neighborhoods in the often-degraded city centers also increased the land value. Urban redevelopment projects such as Puerto Madero built over the abandoned port area near the CBD are an example. In addition, blighted areas such as Barrio de la Boca and San Telmo were re-evaluated through urban redevelopment projects based on their historical heritage and centrality. This investment in the real estate market has increased rapidly, especially in neighborhoods in the city’s northern area where the real estate market is dynamic. These neighborhoods, occupied predominantly by high- and middle-income inhabitants, cover the area between two axes that advance from the center to the northwestern and northwestern areas of the city.

Table 2. Average land price in the CBA, 2001–2018. Data from DGEyC [48].

Year U$S/M2 Annual Change Rate % Index to the Price 2001 % 2001 891 - 100.0 2002 505 −43.3 −43.3 2003 602 19.2 −32.4 2004 813 35 −8.8 2005 915 12.5 2.7 2006 1117.0 22.1 25.4 2007 1300.0 16.4 45.9 2008 1599.1 23 79.5 2009 1692.4 5.8 89.9 2010 1783.8 5.4 100.2 2011 2168.2 21.5 143.3 2012 2322.4 7.1 160.7 2013 2213.5 −4.7 148.4 2014 2320.5 4.8 160.4 2015 2234.5 −3.7 150.8 2016 2487.7 11.5 179.2 2017 2795.3 12.3 213.7 2018 2560.7 −8.3 187.4

In contrast, the residential areas located outside this zone, particularly the southern area, are dominated by housing affordable to low- and middle-income residents. The southern area is historically associated with manufacturing, and accommodates a large proportion of squatter settlements. The decline of industrial activities and the worsening housing conditions compared to the rest of the city are the leading cause for the lack of investment, unlike the northern areas of the city, which are historically related to residential and commercial activities. Looking at the distribution of squatter settlements in CBA, it is evident that they are concentrated in the southern area. These urban environments describe a spatial pattern that presents a marked differentiation between northern and southern urban regions in land prices and income distribution (Figure2). ISPRS Int. J. Geo-Inf. 2021, 10, 359 7 of 18

investment, unlike the northern areas of the city, which are historically related to residential and commercial activities. Looking at the distribution of squatter settlements in CBA, ISPRS Int. J. Geo-Inf. 2021, 10, 359 it is evident that they are concentrated in the southern area. These urban environments7 of 18 describe a spatial pattern that presents a marked differentiation between northern and southern urban regions in land prices and income distribution (Figure 2).

FigureFigure 2. 2. AverageAverage family family income income in in (a ()a 2010) 2010,, and and (b ()b an) an average average price price of ofa 3 a-room 3-room apartments apartments per perm2 min2 (cin) 2010 (c) 2010,, and and(d) 2018(d) 2018 in CBA in CBA.. Data Data from from DGEyC DGEyC [46]. [ 46].

The rising housing costs due to real estate speculation and the lack of housing policy affects the most vulnerable population in the city. The share of immigrants, mainly from neighboring countries, in squatter settlements have been increasing. Based on the national ISPRS Int. J. Geo-Inf. 2021, 10, 359 8 of 18

census [44], the share of immigrants was 22.0% in 1991, 40.9% in 2001, and 47.7% in the last census year, 2010. It is expected that the share will continue to rise toward the next census. The growth of squatter settlements exemplify a process of spatial segregation. Simul- taneously, these settlements may explain the increase of socio-spatial segregation between the northern and southern areas in the city in the last 20 years. This process is associated with an increase in the city segregation levels, thus establishing a social and spatial distance between one area of the city and the rest [51].

4. Method and Data 4.1. Hedonic Prices The negative externalities emanating from nuisance facilities are typically measured using the hedonic price model. This model assumes that property comprises a bundle of individual components where each one has an implicit rate. The components are the inherent features of a property ( e.g., an area, age of the building, the number of rooms) and extrinsic aspects of a property (e.g., neighborhood characteristics, proximity to the CBD, stations, schools, nuisance facilities) [52]. A vast body of literature explores house prices’ determination using this model, highlighting the positive, negative, or both externalities [53–55]. However, the traditional hedonic price analysis has used global regression models, the conventional regression assuming the same coefﬁcients everywhere in a study area, which do not consider spatial dependency and heterogeneity [56]. This traditional analysis assumes that the effect of explanatory variables is uniform across space.

4.2. Geographical Weighted Regression We consider using a local regression model GWR to demonstrate the socio-spatial fragmentation. This model allows regression coefficients to vary over space [57]. This technique can identify spatial heterogeneity through geographically localized regression outputs, typically displayed on a map of estimated local coefficients [58]. Recent works on price determination applied GWR and have shown that it is necessary to consider the spatial heterogeneity [59,60]. However, time is also an essential dimension related to the housing market. A temporal analysis can provide valuable information concerning the consolidation of urban fragmentation in the city. Several spatiotemporal models have been developed to incorporate the spatial and temporal variation into hedonic house price analysis [61–63]. Bo Huang et al. [64] integrated temporal effects in the GWR model. Comparing geographically and temporally weighted regression (GTWR) outperforms both GWR and time-weighted regression (TWR) in the sample data’s model accuracy. The results showed that there were substantial benefits in modeling both spatial and temporal non-stationarity simultaneously. Yao and Fotheringham [65] applied GWR to investigate both global and local relationships between house prices and associated influencing factors and their variations over time. Using 10 years of house prices in Fife, Scotland, the study found that the relationships vary spatially and that spatially varied relationships changed over time. For this study, as we could only retrieve the house price associated variables at several time slices, we conduct an independent GWR analysis for each time slice. Following the GWR model formulation, a house price model can be expressed as follows:

= ( ) + ( ) + log yi βio ui vi ∑j βij ui vi xij εi, (1)

where yi is the price of the ith house; the intercept term βio (ui vi) and the coefficients βij of the jth explanatory variables xij are expected to be spatially varying, dependent on geographical coordinates ui and vi of each individual property. εi is the i.i.d. Gaussian error term for the ith observation. In the global regression, the intercept and the coefficients are constants, that is, βio (ui vi) = βo and βij(ui vi) = βj, but with the GWR, each observation has different intercept and coefficients capturing the spatial dependency in the error term and the ISPRS Int. J. Geo-Inf. 2021, 10, 359 9 of 18

spatial heterogeneity in the effects of the explanatory variables, respectively. To estimate the varying parameters, the GWR model assumes that the parameters vary smoothly over space and applies locally weighted regression to estimate every single observation parameter in the sequence. Locally weighted regression estimates parameters with a geographically weighted kernel centered on each regression point. The weighting kernel assigns smaller weights to the observations away from the regression point following Tobler’s ﬁrst law of geography [66]. Common weighting kernel functions are Gaussian and bi-square weights [58]. We chose the Gaussian kernel weighted function in our analysis because it generally produced a better ﬁt. In the Gaussian weighting function, the weight of observation j for a regression point i wj(ui, vi) is represented by the following formula:

" 2# 1 dij w (u , v ) = exp − (2) j i i 2 h

where bandwidth h controls the size of the weighting neighborhood to estimate the local coefficients, and dij is the distance between the regression point and the observation. The bandwidth of a kernel can either be fixed in terms of distance or adaptive to include the same number of observations within the bandwidth. In this research, we use fixed bandwidth to produce estimates comparable between periods. Initially, we optimize the bandwidth using the golden section search to minimize the second-order Akaike Information Criteria (AICc) for each time period. Then we choose a single bandwidth within the range of optimized bandwidth for direct comparison of the results over time. The bandwidth search, variable selection, and parameter estimation are conducted using GWR4 [67]. The GWR model provides improved explanatory power to explore the spatial differences in the accessibility to squatter settlements. We calculate the spatial fragmentation using cross-sectional data for 2001, 2010, and 2018 in the CBA. An increase in the accessibility to squatter settlements over time implies a process involving the breakup of contiguous neighborhood types and an increase of spatial fragmentation.

4.3. Data: Autonomous City of Buenos Aires Time Series Real Estate Price and Hedonic Data Real estate data for September of 2001, 2010, and 2018 were collected by web scraping methods using the Python script from the largest real estate portals in Argentina ‘Mercado Libre’ and ‘Zonaprop.’ We used the data for 3-room apartments because they are well distributed across the city and are suitable for families. Furthermore, families are more likely to have more substantial concerns about the neighborhood quality. The sample sizes are summarized in Table3, along with the average house price and the average salary from the ofﬁcial data of the government of the CBA. The number of houses in the market in 2001 is similar to that in 2018. However, in 2010, the number of houses in the market declined substantially during an economic expansion, without having relation to the purchasing power.

Table 3. Sample size and house prices in the CBA. Data from DGEyC [48] and compiled by the author of data from ‘Mercadolibre’ and ‘Zonaprop.’

Year Number of Observations House Price Average (US$) Average Salary (US$) 2001 3654 52,945.97 1504.23 2010 1887 145,252.27 1250.50 2018 4334 234,511.34 1079.05

The deﬁnition of the explanatory variables is summarized in Table4. Explanatory variables are divided into two groups. On the one hand, intrinsic characteristics such as the ﬂoor area and the existence of parking and of multiple bathrooms are included. The age of the properties was not available for 2001, and thus is not included in the analysis to allow for direct comparison. On the other hand, extrinsic characteristics consist of the ISPRS Int. J. Geo-Inf. 2021, 10, 359 10 of 18

distances from each house sample to the following facilities: CBD, police station, subway station, green areas (>1 hectare), and the proximity/separation to squatter settlements. The effects of smaller business centers would be captured by spatially varying intercepts as we have relatively dense data points for house prices. The variables of intrinsic characteristics were included in the real estate data. The variables of the extrinsic characteristics were assigned to each property based on the geographic coordinates of the properties using ArcGIS 10.8 [68]. The Euclidean distance to the closest of these services in meters in the natural logarithm was used. The locations of subway stations, train stations, police stations, CBD, and squatter settlements in 2001 and 2010 were obtained from the General Directorate of Statistics, Surveys, and Censuses of CBA [69], and for the squatter settlements in the year 2018 from the nonproﬁt organization TECHO [47].

Table 4. Deﬁnitions of the explanatory variables.

Variable Names Description Units Intrinsic Characteristics Area_NL The total area of the property M2 In a natural log Multi_Bathrooms 0 = 1 bathroom; 1 = more than 1 bathroom Dummy Parking 0 = none; 1 = one or more Dummy Extrinsic Characteristics NL_CBD Distance to the CBD Meters in natural log NL_Subway Distance to the closest train station Meters in natural log NL_Police Distance to the closest subway station Meters in natural log NL_Green Distance to the closest green area Meters in natural log NL_Squatter Distance to the closest squatter settlement Meters in natural log ACC_Squatter_A Area-weighted accessibility to squatter settlements See the text ACC_Squatter_Pop Population-weighted accessibility to squatter settlements See the text

The proximity/separation to squatter settlements is the focus of our inquiry to measure squatter settlements’ effects on house prices. In addition to the log distance to the closest squatter settlement, we prepared two alternative measures using gravity-based accessibility [67] deﬁned as follows:

−2 Ai = ∑ wj . dij , (3)

where Ai is the accessibility to squatter settlements of the ith property, dij is the distance (in meters) from the ith property to the jth squatter settlement, and wj is the weight that can be per area or population of a squatter settlement. These alternative measurements consider the sizes of squatter settlements and the effects of multiple squatter settlements that would simultaneously affect house prices.

5. Results The dependent variable is natural logarithm of the house price. Models 1, 2, and 3 use all the variables listed in Table3, except for the three alternative measures of squatter settlement separation/proximity. In Model 1, the log distance to the closest squatter settlement is used. In Model 2, area-weighted accessibility to squatter settlements is used. Model 3 includes population-weighted accessibility. For each model, there are versions that use the generalized linear model (GLM), estimating the global (constant) parameters, and the GWR, estimating the local spatially varying parameters for each of the three time periods. The preliminary GWR analysis for each time slice in each model resulted in an optimized bandwidth ranging between 391 and 8808 m. We adopted the bandwidth of 1500 m for all GWR estimates for a direct comparison of the results. The model performances are summarized in Table5. ISPRS Int. J. Geo-Inf. 2021, 10, 359 11 of 18

Table 5. Model summary.

Model 1 Model 2 Model 3 GLM (global) 2001 2010 2018 2001 2010 2018 2001 2010 2018 N 3654 1887 4334 3654 1887 4334 3654 1887 4334 AIC −1111.0 806.5 1468.2 −1164.3 859.9 1447.0 −1150.5 861.9 1429.3 AICc −1111.0 806.8 1468.3 −1164.2 860.2 1447.1 −1153.4 862.2 1429.4 CV 0.042 0.099 0.080 0.043 0.098 1012.870 0.043 1.998 0.311 R-squared 0.224 0.664 0.609 0.224 0.675 0.610 0.043 0.672 0.609 Adj. R-squared 0.222 0.661 0.608 0.223 0.672 0.609 0.224 0.621 0.608 GWR (local) 2001 2010 2018 2001 2010 2018 2001 2010 2018 N 3654 1887 4334 3654 1887 4334 3654 1887 4334 AIC −2106.9 −248.8 −1473.4 −2150.6 −257.5 −1492.6 −2009.6 −255.7 −1130.3 AICc −2103.47 −231.5 −1462.3 −2147.2 −231.4 −1482.4 −2095.9 −229.0 −1180.0 CV 0.033 0.084 0.040 0.033 0.083 64.399 0.033 0.232 0.140 R-squared 0.428 0.840 0.812 0.429 0.844 0.814 0.406 0.826 0.812 Adj. R-squared 0.412 0.823 0.803 0.412 0.825 0.805 0.406 0.756 0.802 Bandwidth (Fixed) 1500 m

Comparing the GLM and the GWR versions of the model, the GWR versions out- performed the GLM versions in terms of AICc in all cases, suggesting that spatial heterogeneities exist in house price determination. When comparing the three models, the GWR version of Model 2 has the smallest AICc. During 2001–2018, squatter settlements have grown by 173% in terms of its population but have expanded only by 8% in terms of area. The model using area-weighted accessibility to squatter settlements (Model 2) outperforming population-weighted accessibility (Model 3) might be suggesting that squatter settlement growth is not causing the house price disparities. Nevertheless, we adopt Model 2 that uses area-weighted accessibility to squatter settlements and examine its coefficients. The coefficient estimates of the GLM version of Model 2 have expected signs and are statistically significant at 5%, except for a few variables (Table6). The house prices rise with floor area, but less than proportionately. Multiple bathrooms and parking space raise prices. House prices decline as distances to the CBD increase although not in 2001, possibly because of the financial crisis in 2001. The distances to other facilities generally lowered house prices, but the distance to the police station was statistically significant at 5% only in 2001, and the distance to green spaces was not statistically significant in 2010. The accessibility to a squatter settlement was negative for all three periods, which means a decrease in the house price with proximity to squatter settlements. Comparatively, the effect of squatter settlements was much greater in 2010 than in other years, and it was particularly small in 2018. The coefficients for GWR vary over space; therefore the median, minimum, and maximum for each period are shown in Table7. The coefficient estimates will be shown later in the form of maps. Most parameter estimates range between positive and negative values, depending on the location; the accessibility to a squatter settlement is no exception. The table includes geographical variability test values to explore whether coefficients vary over space [70]. The Diff is a geographical variability test statistic that tests the change in AICc from the model that fixes the coefficient of a variable to a constant one by one. The coefficient is considered spatially varying if the test value is <−2, while values <−4 substantially improve the model [67]. The test statistic showed that all variables are spatially heterogeneous, except for multiple bathrooms in 2001 and 2010, and parking in 2001. ISPRS Int. J. Geo-Inf. 2021, 10, 359 12 of 18

Table 6. Coefﬁcient estimates of the global Model 2.

2001 2010 2018 Variable Estimate T-Value Estimate T-Value Estimate T-Value Intercept 8.224 ** 60.114 8.789 ** 74.533 10.061 ** 100.542 Intrinsic Characteristics Area_NL 0.689 ** 21.855 0.739 ** 40.068 0.696 ** 45.423 Multi_Bathroom 0.139 ** 9.826 0.214 ** 14.877 0.252 ** 25.949 Parking 0.211 ** 15.310 0.264 ** 14.414 0.263 ** 18.292 Extrinsic Characteristics NL_CBD 0.013 ** 3.800 −0.034 ** −6.379 −0.051 ** −14.869 NL_Subway −0.024 ** −6.534 −0.042 ** −5.208 −0.013 ** −2.660 NL_Green −0.014 ** −4.067 0.001 0.102 −0.013 ** −2.707 NL_Police −0.014 * −2.434 −0.013 −1.116 0.003 0.421 ACC_Squatter_A −0.008 * −2.004 −0.066 ** −4.709 −0.003 ** −3.252 Notes: The dependent variable is house prices in the natural logarithm. Model summary is given in Table5. *, ** Statistical signiﬁcance at 5% and 1%, respectively.

Table 7. Coefﬁcient estimates of the GWR Model 2.

2001 2010 2018 Variables Median Min Max Diff Median Min Max Diff Median Min Max Diff Intercept 8.166 4.663 30.070 −3014.3 9.443 −8.102 28.08 −1339.9 9.963 −6.857 25.022 −6711.3 Intrinsic Characteristics Area_NL 0.706 −1.414 0.863 −7848.7 0.693 0.470 1.042 −62.8 0.711 0.326 0.973 −2122.7 Multi_Bathroom0.184 −0.004 0.261 3.7 0.217 0.023 0.280 7.3 0.194 −0.102 0.347 −35.6 Parking 0.124 −0.150 0.205 2.1 0.126 −0.698 0.224 −6.3 0.185 0.114 0.332 −37.9 Extrinsic Characteristics NL_CBD −0.001 −1.530 0.504 −524.5 −0.106 −2.813 2.761 −32.4 −0.092 −1.874 2.137 −2295.9 NL_Subway −0.015 −0.266 0.245 −1901.0 0.016 −1.123 0.914 −1116.6 0.036 −0.336 0.412 −266.2 NL_Green −0.008 −0.070 0.035 −141.7 0.002 −0.067 0.037 −13.6 −0.023 −0.073 0.078 −62.3 NL_Police −0.018 −0.159 0.076 −942.8 −0.026 −0.139 0.106 −301.2 0.009 −0.343 0.086 −1430.5 ACC_Squatter_A−0.010 −1.507 0.222 −43.6 −0.026 −2.710 0.030 −19.6 0.000 −15.768 0.080 −21.7 Notes: The dependent variable is house prices in the natural logarithm. Model summary is given in Table5.

Before examining the squatter settlements’ influence on the housing market, it is necessary to confirm the widening house price disparities controlling for the houses’ structural characteristics. The controlled house price was estimated using Equation (4), which represents the house price of a house with an average floor area, one bathroom, and no parking, based on the GWR version of Model 2:

0 yˆi = yˆi − βi(AreaNL) + βi AreaNL , (4)

Figure3 shows an increase in house prices affecting the whole city, but with different intensities. The areas with higher house prices are clustered in the city’s northeast axis, and they expand there more than in the rest of the city. Meanwhile, the house prices in the southern areas are stable with a moderate or no increase in prices. Concerning the west–east axis, the house prices are more dispersed. However, the limitation of this analysis is that the house age is not controlled because there was no such variable for 2001. ISPRS Int. J. Geo-Inf. 2021, 10, 359 13 of 18

ISPRS Int. J. Geo-Inf. 2021, 10, 359 13 of 18 and they expand there more than in the rest of the city. Meanwhile, the house prices in ISPRS Int. J. Geo-Inf. 2021, 10, 359 the southern areas are stable with a moderate or no increase in prices. Concerning13 the of 18 andwest they–east expand axis, the there house more prices than are in more the rest dispersed. of the city. However, Meanwhile, the limitation the house of pricesthis anal- in theysis southern is that the areas house are age stable is not with controlled a moderate because or no there increase was no in such prices. variable Concerning for 2001. the west– east axis, the house prices are more dispersed. However, the limitation of this analysis is that the house age is not controlled because there was no such variable for 2001.

Figure 3. House prices controlled for housing structure (a) 2001, (b) 2010, and (c) 2018. FigureFigure 3 3. .House House prices prices con controlledtrolled for for housing housing structure structure ( a(a) )2001, 2001, ( b(b) )2010, 2010, and and ( c(c) )2018 2018 Figure4 shows changes in controlled average house prices by communes. This ﬁgure FigureFigure 4 4 shows shows changes changes in in controlled controlled average average house house prices prices by by communes. communes. This This figure figure thus shows a particular positive polarization of the values, located near the CBD, and thusthus shows shows a a particular particular positiv positivee polarization polarization of of the the values, values, located located near near the the CBD, CBD, and and a a a negative one, situated in the city’s southern area. The communes in the south with negativenegative one, one, situated situated in in the the city city’s’s southern southern area. area. The The communes communes in in the the south south with with multi- multi- multiple squatter settlements have the lowest increase in house values compared to the pleple squatter squatter settlements settlements have have the the lowest lowest increase increase in in house house values values compared compared to to the the rest rest of of rest of the city. thethe city. city.

Figure 4. ChangesFigureFigure in 4 4.controlled .Changes Changes in in house controlled controlled prices house (housea) 2001–2010, prices prices ( a(a) ()2001b 2001) 2010–2018,––2010,2010, ( b(b) ) and2010 2010 (–c–2018)2018 2001–2018., ,and and ( c(c) )2001 2001––20182018

ConverselyConversely,Conversely, ,the the coefficientcoefficient coefficient valuesvalues values associatedassociated associated with with with the the the squatter squatter squatter settlements’ settlements settlements accessibility’ ’accessi- accessi- bilityvariedbility varied varied positively pos positivelyitively and negatively. and and negatively. negatively. Figure Figure Figure5 shows 5 5 shows theshows spatial the the spatial variationspatial variation variation of the localof of the the model’s local local modelcoefficients’s coefficients and their andt-values. their t-values. Coefficients Coefficients are considered are considered to be statistically to be statistically insignificant insig- if nificantthe t-value if the falls t-value between falls 2between and −2. 2 and −2. The coefficients of accessibility to squatter settlements exhibited considerably different spatial heterogeneities across periods. In the first period, in 2001 (Figure5a), the negative value is concentrated in the city’s southern area with small spots in the north. However, estimated values for 2010 and 2018 (Figure5a,b) show a gradual transition and ISPRS Int. J. Geo-Inf. 2021, 10, 359 14 of 18

ISPRS Int. J. Geo-Inf. 2021, 10, 359 The coefficients of accessibility to squatter settlements exhibited considerably dif-14fer- of 18 ent spatial heterogeneities across periods. In the first period, in 2001 (Figure 5a), the negative value is concentrated in the city’s southern area with small spots in the north. How- ever, estimated values for 2010 and 2018 (Figure 5a,b) show a gradual transition and clus- teringclustering of the of c theity’s city’s northern northern area’s area’s negative negative effect. effect. This change This change shows shows a transition a transition of the of negativethe negative effect effect of squatter of squatter settlements settlements moving moving from from an area an areawith witha high a highagglomeration agglomeration of squatterof squatter settlements settlements toward toward the thenorth north,, where where there there are are few few squatter squatter settlements. settlements. There There werewere also also locations locations having having positive positive coefficients coefﬁcients,, which which indicates indicates an an increase increase in inthe the house house priceprice with with the the proximity proximity to to squatter squatter settlements settlements..

Figure 5. GWRGWR r result,esult, coefficient coefﬁcient estimates estimates of of accessibility accessibility to tosquatter squatter settlements settlements and and t-valuest-values of (a of) 2001, (a) 2001, (b) 2010, (b) 2010, and (c and) (2018.c) 2018.

TheThe interpretation interpretation of of the the squatter squatter settlements settlements’’ effect effect is isstrongly strongly related related to tothe the spatial spatial extentextent of of neighborhood neighborhood effec effectsts on on housing prices. These These results might indicate three differ-dif- ferentent housing housing regimens regimens in in the the city,city, explainingexplaining the CBA CBA’s’s spatial spatial fragmentation, fragmentation, especially especially betweenbetween the the northern, northern, southern, southern, and and central central areas. areas.

66.. Discussion Discussion and and C Conclusionsonclusions WeWe confirmed confirmed that the proximityproximity toto aasquatter squatter settlement settlement lowered lowered the the house house price, price but, butlocally locally in thein the areas areas surrounding surrounding the the squatter squatter settlements settlements clustering clustering in in the the south south regionregion in in2001 2001 (Figure (Figure5a). 5a) However,. However, the the geographical geographical pattern pattern changed changed substantially substantially over over the the period. period.The negativeThe negative effect effect significantly significantly diminished diminished in 2018 in in 2018 the southernin the south region,ern region and it, increased and it increasedin the northern in the north regionern (Figure region5c). (Figure 5c). TheseThese observations observations refute refute o ourur initial initial hypothesis hypothesis that that the the squatter squatter settlement settlement growth growth resultedresulted in in greater greater house house price price disparities disparities be betweentween the the city city’s’s north north and and the the south. south. Had Had the the squattersquatter settlement growth growth been been the the cause, cause, the the areas areas affected affected by squatter by squatter settlements settlement woulds wouldhave remained have remain theed same. the Thesame. negative The negative effect ofeffect squatter of squatter settlements settlement appearings appearing in the highin thehouse high price house areas price in areas the north in the implies north implies that squatter that squatter settlements settlement have insteads have instead narrowed nar- the rowedhouse the price house disparity price betweendisparity thebetween north the and north the south. and the south. WWee did did confirm confirm with with the the controlled controlled house house price price that that house house prices prices were were higher higher in inthe the northnorth than than in in the the south south (Figure (Figure 3)3),, and and the the disparity disparity ha hass grow grownn over over the the study study period period (Figure(Figure 4).). Yet, Yet, the the areas areas affected affected by by squatter squatter settlement settlementss have have changed changed in a way in a t wayhat nar- that rownarrowss the house the house price price disparities. disparities. We attempt We attempt to interpret to interpret this seemingly this seemingly contradicting contradicting ob- servationobservation through through logical logicallyly understanding understanding what what we have we have seen seen so far. so far. The negative effect of squatter settlements on house prices in the southern area may have disappeared because of the high demand for low-cost housing induced by widened income disparity (Figure2a,b). The asset boom rapidly increased property prices in the northern area, but the proximity to the squatter settlement agglomeration in the south kept house prices there from rising. The southern region became almost the only housing alternative for the low-income households within the city in the face of increased housing ISPRS Int. J. Geo-Inf. 2021, 10, 359 15 of 18

prices. The concentration of low-income households in the south could have lowered the house price there. At the same time, poverty forced households to trade off the higher perceived safety to access to the labor market, and therefore, the negative effect of squatter settlements disappeared in the south. In contrast, the negative effect has appeared in the northern area since 2010 (Figure5b), far away from the bulk of squatter settlements. Possibly, the negative coefficients appearing in the north might be associated with increased perception of insecurity pertaining to the squatter settlements in the south due to widened disparity between the squatter settlement dwellers and the citizens in the north. Alternatively, we suspect that the perceived nuisance of high-income citizens in the north may not be particularly arising from squatter settlements alone but also due to the whole low-income south region. The explanation of the positive coefficients for the accessibility to squatter settlements can be explained from the speculation of specific squatter settlements to be redeveloped. In 2001, the positive estimates appeared around the squatter settlements near the CBD and one of the city’s central transport hub, Retiro Railway Terminus (Figure5a). A location near the CBD and the transport hub is a likely target for gentrification. In 2008, the positive estimates value shifts to another location (Figure5b), where urban policies promoted a technological district in the city from 2008 [71]. By 2018, the technological district’s prices waned, and the positive estimates shifted back to the locations to the north of the CBD (Figure5c). Urban renewal policies during this period in CBA focused on promoting a polycentric model by converting run-down neighborhoods and slums into subcenters. Accordingly, residential areas adjacent to the squatter settlements that have potential to be redeveloped into subcenters are attractive to private real estate investors, neutralizing the squatter settlements’ nuisance effect. We conclude that the squatter settlement growth is not the cause of spatial fragmentation in the housing market and the widening house price disparities. Widened income disparity segregated low-income households to the low-housing-cost area, which reinforced the existing house price disparities. Therefore, it is the further segregation of low-income households to the south that is causing the widened house price disparities between the south and the high-income north. In the case of CBA, the squatter settlement growth is the symptom of widening income disparity, which might be applicable to other cities experiencing squatter settlement growth and widening income disparity. Squatter settlements had some role in the process, however, as they have determined where the higher concentration of low-income households have occurred. In this context, squatter settlements are at the core of low-housing areas, implying the spatial fragmentation between regions in the city. This fragmentation is a recurrent and frequent phenomenon in developing countries’ cities, resulting in an urban structure and conditions of exclusionary housing access for certain social groups. The consequence is the gradual loss of the classical city’s heterogeneity that has enabled interaction between different social groups. An alternative to this fragmentation and the price depreciation in areas near squatter settlements is promoting an approach based on mixed-income housing developments. Such a program can achieve social integration, reduce uneven housing and infrastructure investments, provide affordable housing, and reduce environmental inequalities, including the exposure to communicative disease such as COVID-19. The major limitation to this research is that a GWR analysis using proximities to geographical features cannot distinguish the effects of spatially coincident features, in our case, between the effects of squatter settlements and the low-income areas that surround them. It is therefore inconclusive as to whether the effect that has appeared since 2010 as the negative effect of squatter settlements on the affluent north, at some distance away from the bulk of the squatter settlements in the south, is the effect of squatter settlements alone. It is also possible that all the low-income areas that surround the squatter settlements in the south are lowering the house prices in the north. Approaches incorporating complementary research, such as the contingent valuation method, may be useful. ISPRS Int. J. Geo-Inf. 2021, 10, 359 16 of 18

Author Contributions: Conceptualization, Alberto Federico Ogas Mendez and Yuzuru Isoda; Data curation, Alberto Federico Ogas Mendez; Formal analysis, Alberto Federico Ogas Mendez; Fund- ing Acquisition, Yuzuru Isoda; methodology, Alberto Federico Ogas Mendez and Yuzuru Isoda, project administration, Alberto Federico Ogas Mendez; Software, Alberto Federico Ogas Mendez; Supervision, Yuzuru Isoda; validation, Alberto Federico Ogas Mendez and Yuzuru Isoda; Visual- ization, Alberto Federico Ogas Mendez; Writing—original draft, Alberto Federico Ogas Mendez; Writing—review and editing, Yuzuru Isoda. Both authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. The costs of the publication are covered by the Graduate School of Science of Tohoku University. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data presented in this study are available on request from the corresponding author. Acknowledgments: The author wishes to thank Alblooshi Abdulrahman and Broitman Dani for their constructive comments on earlier versions of this manuscript. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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