Emergent Phenomena in Housing Markets . Lidia Diappi Editor

Emergent Phenomena in Housing Markets

Gentrification, Housing Search, Polarization Editor Prof. Lidia Diappi Politecnico di Milano Department of Architecture and Planning Milano

ISBN 978-3-7908-2863-4 ISBN 978-3-7908-2864-1 (eBook) DOI 10.1007/978-3-7908-2864-1 Springer Heidelberg New York Dordrecht London

Library of Congress Control Number: 2012943937

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Physica-Verlag is a brand of Springer Springer is part of Springer ScienceþBusiness Media (www.springer.com) Contents

1 Introduction ...... 1 Lidia Diappi

Part 1 Modeling the Spatial Behavior of Agents

2 Employing Agents to Develop Integrated Urban Models: Numerical Results from Residential Mobility Experiments ...... 19 Oswald Devisch, Theo Arentze, Aloys Borgers, and Harry Timmermans 3 Modeling Housing Market Dynamics Using a Multi-agent Simulation of Participants’ Cognitive Behavior ...... 43 Maryam Esmaeili, Alberto Vancheri, and Paolo Giordano 4 Redevelopments and Gentrification: A MAS Model of the Urban Housing Market in ...... 85 Lidia Diappi and Paola Bolchi

Part 2 Empirical Investigations

5 Between Friends and Strangers: Schelling-Like Residential Dynamics in a Haredi Neighborhood in Jerusalem ...... 103 Shlomit Flint, Itzhak Benenson, Nurit Alfasi, and Yefim Bakman 6 Gentrification Without Exclusion? A SOM Neural Network Investigation on the Isola District in Milan ...... 127 Lidia Diappi, Paola Bolchi, and Luca Gaeta 7 Urban Policy and Gentrification. A Critical Analysis Using the Case of Paris ...... 151 Anne Clerval and Antoine Fleury 8 Conclusions ...... 171 Lidia Diappi

v . List of Figures

Fig. 2.1 General structure of a decision table (Figure from Verhelst (1980))...... 27 Fig. 2.2 Example of an activity diagram (Figure from Gooch (2000)) . . . 28 Fig. 2.3 Example of a decision tree; the square node represents a decision node and the circular node represents a nature node; U represents utility ...... 29 Fig. 2.4 Decision table of a student with preference-profile 2 ...... 31 Fig. 2.5 Activity diagram of unboundedly rational students in a stationary housing-market ...... 31 Fig. 2.6 Decision tree of an unboundedly rational student in a stationary housing-market; o represents a residence; U(v, c), U 0 (o0) and U represent the utilities the student expects to derive from living in, respectively, a residence belonging to a residence-class v and price-category c; the parental home o0; and the current residence ...... 32 Fig. 2.7 Activity diagram of unboundedly rational students in a non- stationary housing-market ...... 35 Fig. 2.8 Activity diagram of boundedly rational students in a non- stationary housing-market ...... 36 Fig. 2.9 Decision table of a student with preference-profile 2 ...... 37 Fig. 2.10 Decision tree of boundedly rational students in a non- stationary housing-market ...... 38 Fig. 2.11 Results on the level of single students; the life-course is indicated as a full line, the move-course as a dotted line. The life-course of an eventual partner is indicated in grey.The o-signs indicate moves within the same profile ...... 39 Fig. 3.1 stress-resistance hypothesis. The figure depicts the time- related behavioral decision-making process consisting of householder stress, resistance, and choice-decisions ...... 50 Fig. 3.2 Typical structure of a fuzzy logic system ...... 54 Fig. 3.3 The fundamental steps of the market model proposed ...... 59 Fig. 3.4 Conceptual model of the stress-resistance model ...... 60 Fig. 3.5 The conceptual model of the proposed product selection mechanism ...... 63

vii viii List of Figures

Fig. 3.6 Conceptual model of supply maker behavior ...... 64 Fig. 3.7 Negotiation protocol between two agents ...... 66 Fig. 3.8 Comparison of Swiss populations in 2008 in different districts of Lugano produced by IRE and simulation ...... 66 Fig. 3.9 Comparison of Italian population in 2008 in different districts of Lugano produced by IRE and simulation ...... 76 Fig. 3.10 Comparison of Portuguese population in 2008 in different districts of Lugano produced by IRE and simulation ...... 77 Fig. 3.11 Comparison of Turkish population in 2008 in different districts of Lugano produced by IRE and simulation ...... 78 Fig. 4.1 The rent gap theory. Rent variation in an urban area (Reworking of Smith (1979), p. 544) ...... 79 Fig. 4.2 The real estate market cycle (Muller€ 1995) ...... 87 Fig. 4.3 Regression line between estimated data (cycle 120) and real data (2003, OSMI) ...... 89 Fig. 4.4 (a, b) Comparison between real estate rents structure observed and estimated by the model (cycle 121) ...... 93 Fig. 4.5 A projection of the real estate rents in year 2013, cycle 241 ...... 94 Fig. 4.6 The cyclicality of average rent ...... 94 Fig. 4.7 The urban mean rent trend with and without the effect of redeveloped ...... 96 Fig. 4.8 The master plan and the eight urban renewal projects: (1), (2) Rubattino 1 e 2, (3) Santa Giulia, (4) Bisceglie Lorenteggio, (5) Portello, (6)PompeoLeoni,(7)PiazzaleLodi, (8) Bicocca...... 96 Fig. 4.9 (a) Simulation results after 100 cycles without interventions. (b) Simulation results after 100 cycles including redevelopment projects ...... 97 Fig. 4.10 Distribution of housing units in rent classes with and without the effect of the planned developments after100 cycles ...... 97 Fig. 5.1 Jerusalem, the expansion of the city’s borders; (a) population groups (b) Sanhedria and old city ...... 108

Fig. 5.2 Sanhedria buildings and the coverage of Voronoi polygons constructed based of buildings’ centroids. Voronoi-neighbors of the selected building (Black) are shown in Gray ...... 109 Fig. 5.3 Population dynamics in Sanhedria in absolute numbers ...... 112 Fig. 5.4 Spatial distribution of Lithuanians, Hassidim, Sephardim, Foreign-Lithuanians, National-Religious and Secular in apartment buildings, Sanhedria 1983–2008 ...... 114 Fig. 5.5 The dynamics of Haredim residential segregation in Sanhedria, according to Moran I index, during the period of 1982–2008 ...... 115 Fig. 5.6 The probabilities to leave the apartment on the fractions of sects in the building ...... 116 List of Figures ix

Fig. 5.7 The probabilities to replace friend on the fractions of sects in the building ...... 118 Fig. 5.8 The probabilities to leave the apartment as dependent on the fractions of other sects in the building’s neighborhood ...... 118 Fig. 5.9 Significant dependencies of the probabilities to replace the family of the same sect on the fractions of each of the other sects in the building’s neighborhood ...... 119 Fig. 5.10 Significant dependencies of the probabilities to replace the families of the other sects as dependent on the fractions of families of the same sect in the building’s neighborhood ...... 120 Fig. 5.11 The dynamics of Hassidim – sephardim pattern ...... 122 Fig. 6.1 Map showing the extent of the gentrification process in Milan between 1991 and 2001 defined on the base of the multi criteria evaluation method ...... 134 Fig. 6.2 Boundaries of the study area and its main streets ...... 135 Fig. 6.3 The area beyond the Comasina gate in 1888 and 1936. In black the Isola district ...... 135 Fig. 6.4 The Garibaldi Repubblica redevelopment ...... 136 Fig. 6.5 Household composition of immigrants in the Isola district by period of arrival compared to Milan ...... 138 Fig. 6.6 Comparison by age between the Isola district and Milan (census data 2001) ...... 139 Fig. 6.7 Housing units of respondents by period of settlement and period of construction (data from interviews) in comparison with the period of construction of housing in Milan (census data 2001) ...... 139 Fig. 6.8 Housing units of respondents by period of settlement and house surface area (data from interviews) in comparison with the average house surface area in Milan (census data 2001) ...... 140 Fig. 6.9 Location of the former dwellings of respondents in the Isola neighbourhood ...... 140 Fig. 6.10 Workplaces of respondents in the Isola district ...... 141 Fig. 6.11 Opinions on the seriousness of problems and on the facilities in the district (weighted average) ...... 142 Fig. 6.12 Factors that positively or negatively affect the district ...... 143 Fig. 6.13 Functioning of the SOM RN: the network is deformed by the learning algorithm to bring the nodes close to the groups of observations ...... 144 Fig. 6.14 The nine groups prototypical profiles (Codebook) ...... 145 Fig. 6.15 The prototypical profile (Codebook) for the group C11 ...... 146 Fig. 7.1 Paris: study area ...... 155 Fig. 7.2 IRIS typology in Paris according to main residence characteristics and social category of households in 1982 ...... 156 x List of Figures

Fig. 7.3 IRIS typology in Paris according to main residence characteristics and social category of households in 1999 ...... 156 Fig. 7.4 Towards a redefinition of leisure centralities by the public authorities ...... 164 List of Tables

Table 2.1 Average results on the level of the whole population (in the experiment without joint decision-making, only the moving behaviour of the student making decisions is recorded) ...... 32 Table 3.1 Household entity and it’s low-level state variables ...... 57 Table 3.2 Apartment entity and it’s low-level state variables apartment .... 58 Table 3.3 Supplier entity and it’s low-level state variables apartment supplier ...... 58 Table 3.4 Parcels entity and it’s low-level state variables apartment supplier parcels ...... 58 Table 3.5 The feature have been used for satisfaction evaluation ...... 72 Table 3.6 The variables considered by a householder living in hectare 35 at the 2nd round of simulation with a 5,000 CHF/month income and 2 household members ...... 73 Table 5.1 Importance of apartment’s price, neighbors’ identity and institutional proximity in apartment choice, by Haredi sect ...... 112 Table 5.2 Sects’ segregation in the buildings in 2008; note that total population percentage of four Haredi sects in Sanhedria is 95.9, the rest are National Religious and Secular families ...... 115 Table 5.3 Averaged over 1983–2008 probability to replace a family of an own sect ...... 116 Table 5.4 Averaged by 1983–2008 probability to replace the family of the other sect in an apartment ...... 118 Table 5.5 Averaged annual probability to leave an apartment ...... 118 Table 5.6 The revealed dependencies between the probabilities to leave, to replace the friend and to replace the stranger and the frequencies of the sects ...... 120 Table 6.1 Multicriteria assessment of gentrifying districts: indicators and ranking ...... 132

xi xii List of Tables

Table 7.1 Evolution of housing lacking basic conveniences compared to “official” social housing in Paris since 1982 (Source INSEE RGP 1982, 1990 and 1999; Mairie de Paris, 2006) ...... 159 Table 7.2 The “green quarters” in their socio-demographic context (source INSEE RGP 1999, Mairie de Paris/Direction de la voirie; based on Fleury 2007, pp. 305–306) ...... 162 List of Authors

Nurit Alfasi Department of Geography and Environmental Development, Ben Gurion University of the Negev, Beer Sheva, Israel Theo Arentze Urban Planning Group, Department of Architecture, Building and Planning, Eindhoven University of Technology, Eindhoven, The Netherlands Yefim Bakman Department of Geography and Human Environment, Tel-Aviv University, Tel.Aviv, Ramat Aviv, Israel Itzhak Benenson Department of Geography and Human Environment, Tel-Aviv University, Tel.Aviv, Ramat Aviv, Israel Paola Bolchi Department of Architecture and Planning, Politecnico di Milano, Milano, Italy Aloys Borgers Urban Planning Group, Department of Architecture, Building and Planning, Eindhoven University of Technology, Eindhoven, The Netherlands Anne Clerval UMR 8504 Ge´ographie-cite´s, Paris, France; Universite´ Paris Est, Marne la Valle´e, France Oswald Devisch Urban Planning Group, Department of Architecture, Building and Planning, Eindhoven University of Technology, Eindhoven, The Netherlands Lidia Diappi Department of Architecture and Planning, Politecnico di Milano, Milano, Italy Maryam Esmaeili Accademia di Architettura, Universita` della Svizzera Italiana, Mendrisio, Switzerland; University of Lugano, Lugano, Switzerland Antoine Fleury UMR 8504 Ge´ographie-cite´s, Paris, France Shlomit Flint Department of Geography and Human Environment, Tel-Aviv University, Tel.Aviv, Ramat Aviv, Israel Luca Gaeta Department of Architecture and Planning, Politecnico di Milano, Milano, Italy Paolo Giordano Accademia di Architettura, Universita` della Svizzera Italiana, Mendrisio, Switzerland; University of Lugano, Lugano, Switzerland

xiii xiv List of Authors

Harry Timmermans Urban Planning Group, Department of Architecture, Building and Planning, Eindhoven University of Technology, Eindhoven, The Netherlands Alberto Vancheri Accademia di Architettura, Universita` della Svizzera Italiana, Mendrisio, Switzerland; University of Lugano, Lugano, Switzerland