Modeling Social Heterogeneity, Neighborhoods and Local Influences on Urban Real Estate Prices Nederlandse Geografische Studies / Netherlands Geographical Studies
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Modeling social heterogeneity, neighborhoods and local influences on urban real estate prices Nederlandse Geografische Studies / Netherlands Geographical Studies Redactie / Editorial Board Drs. J.G. Borchert (Editor in Chief ) Prof. Dr. J.M.M. van Amersfoort Dr. P.C.J. Druijven Prof. Dr. A.O. Kouwenhoven Prof. Dr. H. Scholten Plaatselijke Redacteuren / Local Editors Dr. R. van Melik, Faculteit Geowetenschappen Universiteit Utrecht Dr. D.H. Drenth, Faculteit der Managementwetenschappen Radboud Universiteit Nijmegen Dr. P.C.J. Druijven, Faculteit der Ruimtelijke Wetenschappen Rijksuniversiteit Groningen Drs. F.J.P.M. Kwaad, Fysich-Geografisch en Bodemkundig Laboratorium Universiteit van Amsterdam Dr. L. van der Laan, Economisch-Geografisch Instituut Erasmus Universiteit Rotterdam Dr. J.A. van der Schee, Centrum voor Educatieve Geografie Vrije Universiteit Amsterdam Dr. F. Thissen, Afdeling Geografie, Planologie en Internationale Ontwikkelingsstudies Universiteit van Amsterdam Redactie-Adviseurs / Editorial Advisory Board Prof. Dr. G.J. Ashworth, Prof. Dr. P.G.E.F. Augustinus, Prof. Dr. G.J. Borger, Prof. Dr. K. Bouwer, Prof. Dr. J. Buursink, Dr. J. Floor, Prof. Dr. G.A. Hoekveld, Dr. A.C. Imeson, Prof. Dr. J.M.G. Kleinpenning, Dr. W.J. Meester, Prof. Dr. F.J. Ormeling, Prof. Dr. H.F.L. Ottens, Dr. J. Sevink, Dr. W.F. Sleegers, T.Z. Smit, Drs. P.J.M. van Steen, Dr. J.J. Sterkenburg, Drs. H.A.W. van Vianen, Prof. Dr. J. van Weesep ISSN 978-90-6809-428-2 Netherlands Geographical Studies 385 Modeling social heterogeneity, neighborhoods and local influences on urban real estate prices Spatial dynamic analyses in the Belo Horizonte metropolitan area, Brazil Bernardo Alves Furtado Utrecht 2009 Koninklijk Nederlands Aardrijkskundig Genootschap Faculteit Geowetenschappen Universiteit Utrecht This dissertation was partly accomplished with financial support from Urban and Regional research centre Utrecht (URU), and CAPES (Brazilian government research institute) which funded an internship period at Research Institute of Knowledge Systems (RIKS). It is also part of an international partnership agreed upon by the Centre for Regional Development and Planning (CEDEPLAR) at Federal University of Minas Gerais (UFMG), and URU at Utrecht University. The author also benefited from a work leave in 2008 from Centro Universitário UNA. ISBN 978-90-6809-428-2 Graphic design: GeoMedia (Faculty of Geosciences, Utrecht University) (7517) Copyright © Bernardo Alves Furtado, c/o Faculty of Geosciences Niets uit deze uitgave mag worden vermenigvuldigd en/of openbaar gemaakt door middel van druk, fotokopie of op welke andere wijze dan ook zonder voorafgaande schriftelijke toestemming van de uitgevers. All rights reserved. No part of this publication may be reproduced in any form, by print or photo print, microfilm or any other means, without written permission by the publishers. Printed in the Netherlands by A-D Druk b.v. – Zeist Contents Figures 9 Tables 11 Acknowledgements 13 0 Introduction 15 0.1 Research questions 18 0.2 Research design and methodologies 18 Part I 1 Theoretical considerations regarding urban economics 27 1.3 Residence as a differentiated good 27 1.4 Alonso and his followers 28 1.4.1 Muth-Mills model 35 1.4.2 Advances in urban economics 38 1.5 Hedonic prices and urban amenities 41 1.6. Concluding remarks 42 2 Criticism of urban economics and alternative approaches 43 2.1 Introduction 43 2.2 Criticism of urban economics 43 2.3 Defining city 45 2.4 Complex and self-organizing systems: an alternative to urban analysis 47 2.4.1 Cellular automata 49 2.4.2 Agents, actors and cells 50 2.4.3 Transition rules 51 2.4.4 White and Engelen’s model 51 2.5 Other cellular automata models 53 2.6 Concluding remarks 54 3 An extension to White and Engelen’s model 55 3.1 Theoretical compatibility and the Brazilian case 55 3.2 The extended bottom-up model, including social heterogeneity and negative feedback 57 3.3 Detailing the model 60 3.3.1 The neighborhood effect (N) 60 3.3.2 Accessibility (A) 61 3.3.3 Constraints and competition between actors 62 3.3.4 Further adaptation: zoning and suitability 63 5 3.4 Implementing the model: the METRONAMICA System 63 3.5 Calibration procedures and the importance of parameters 64 3.6 Validation 64 Part II 4 Historical contextualization 67 4.1 Brazilian rapid urban population growth 67 4.2 Historical description at the state level 68 4.2.1 Establishing Belo Horizonte as Minas Gerais’ capital 68 4.2.2 Urban-economic evolution 69 4.2.3 1950s and 1960s: “urban land production” and speculation 71 4.2.4 Population growth of RMBH 72 4.3 Specific intra-urban evolution of RMBH 73 4.4 Administrative and legislation issues 82 4.4.1 Municipalities boundaries 82 4.4.2 A brief history of Belo Horizonte’s land-use legislation 84 4.4.3 Description of the present administrative legal situation in RMBH and its trends 84 4.5 Concluding remarks 85 5 The neighborhood 87 5.1 The literature and the definition of neighborhood 87 5.2 The spatial unit chosen 89 5.3 Data: describing urban space 90 5.3.1 Neighbourhood quality index 90 5.3.2 Level of activities index 91 5.3.3 Level of industrial activities index and Predominance of innovative sector 91 5.3.4 Other data 91 5.4 Methodology 92 5.5 Results 92 5.5.1 Neighborhood quality index 93 5.5.2 Level of activities and Level of industrial activities indices 93 5.5.3 Predominance of innovative sectors 101 5.6 Concluding remarks 101 6 Analyzing the neighborhood hedonic prices function using spatial and 103 quantile regression modeling 6.1 Introduction 103 6.2 Methodology 105 6.2.1 Spatial analysis and spatial econometric models 105 6.2.2 Weight matrices 106 6.2.3 Quantile regression analysis 107 6.2.4 Spatial-quantile regression analysis 107 6.3 Dataset 108 6.3.1 Dataset used in the model 111 6 6.4 Model, diagnoses, tests, weight matrix alternatives, and results 111 6.4.1 Weight matrices 113 6.5 Interpretation of results and use of matrices 115 6.5.1 OLS and spatial 115 6.5.2 Quantile Regression Results 118 6.5.3 Spatial-quantile (IVQR) 120 6.6 Concluding remarks 122 7 Description of reference data for calibration and validation 125 7.1 Methodology: multivariate analysis – clustering 125 7.2 Data 127 7.3 Results and discussion 128 7.3.1 Results of the clustering procedures 128 7.3.2 The reference year: 1991 128 7.3.3 Validation reference for 2000 137 7.3.4 Description of evolution from 1991 to 2000 139 7.3.5 Quantitative occupation of space by different actors 141 7.4 Spatial configuration of prices in 2007 141 7.5 Concluding remarks 143 8 Application: cellular automata model for the Greater Area of Belo Horizonte (1897-1991) 145 8.1. Objective of the application 145 8.2. Definition of actors 145 8.2.1 Exogenous historical features implemented 147 8.3 Application data 147 8.3.1 Workspace: study area 147 8.3.2 Exogenous demand 149 8.3.3 Accessibility 149 8.4 Possible scenarios: data 150 8.5 Model application time scheme and validation 150 8.6 Choice of parameters 152 8.6.1 Neighborhood (N) 152 8.6.2 Accessibility (A) 153 8.6.3 Price (P) 157 8.6.4 Comparison with the reference map to confirm parameter choice 157 8.6.5 Relative price configuration and comparison with reference map 164 8.7 Validation part I: actors 164 8.8 Sensitivity analysis 166 8.8.1 Simulation results without prices 166 8.8.2 Simulation results with double influence of prices 167 8.8.3 Simulation results without accessibility parameters 169 8.8.4 Simulation results with similar N parameters for all residential income levels 169 8.8.5 Simulation without accessibility parameters, with similar N parameters, 169 and without price parameters 7 8.9 Scenario analysis 171 8.10 Concluding remarks 172 9 Cross-validation: part II, prices 175 9.1 Models 175 9.2 Results, analyses and interpretation 176 10 Concluding remarks 181 10.1 Critical concluding remarks 181 10.2 Research questions revisited 182 10.3 Limitations 183 10.4 Suggestions for further research 184 References 185 11 Appendix 197 11.1 Initial occupation of Belo Horizonte 197 11.2 RMBH municipalities at creation in 1974 197 11.3 Principal components 197 11.4 Other data 200 11.5 Metrics on quantifying maps 200 11.6 Methodological note on the data availability for 1991 201 11.7 CA model data 201 Resumo em português (Portuguese extended summary) 215 Samenvatting in het Nederlands (Dutch summary) 223 Curriculum Vitae 227 Endnotes 229 8 Figures 0.1 Research design 19 1.1 Diagrammatic scheme of von Thünen’s model for agriculture land 31 3.1 Illustration of processes 58 3.2 Illustration of influence table effects 61 3.3 Illustration of influence of a high-income cell for high-income transition potential on its neighborhoods 63 4.1 Location of Minas Gerais and RMBH within Brazil 70 4.2 Urban occupation in Belo Horizonte 1900 75 4.3 Urban occupation in Belo Horizonte 1910 76 4.4 Urban occupation in Belo Horizonte 1920 77 4.5 Urban occupation in Belo Horizonte 1930 78 4.6 Urban occupation in Belo Horizonte 1940 79 4.7 Urban occupation in Belo Horizonte 1964 80 4.8 Official urban areas RMBH 2000 81 4.9 Evolution of RMBH municipalities boundaries from 1893 to 1996 83 5.1 Synthesis of the indicators used to describe the urban fabric 90 5.2 Neighborhood quality index for Belo Horizonte 94 5.3 Activities index for Belo Horizonte 95 5.4 Industrial activities index for Belo Horizonte 98 5.5 Predominance of innovative sectors 99 6.1 Commercialized real estate units in Belo Horizonte, 2007, by type 110 6.2 Visualization of matrix of contiguity 113 6.3 Visualization of matrix of distance.