Photo: Fabio Venni Urban Conventions and Residential Location Choice Exploring a Heterodox Perspective of Urban Economics with a Spatially-Explicit Simulation Model Flávia F. Feitosa 1 Antônio Miguel V. Monteiro 1,2 1 Earth System Science Center – CCST 1,2 Earth Observation Coordination – COBT National Institute for Space Research - INPE CAMUSS - International Symposium on Cellular Automata Modeling for Urban and Spatial Systems November 8-10, 2012 — Oporto, Portugal

The Production of Urban Space in Brazil São Paulo, SP, Brazil blog.opovo.com.br

São José dos Campos, SP, Brazil imoveisesaojosedoscampos.com.br

Jardim Aquarius - São José dos Campos, SP (1/7/2006)

(9/3/2008)

(4/17/2010)

(9/2/2011)

The Production of Space in Brazilian Cities High Demand for Urban Dwellings SocioDemographic Changes

Urban Population Increased Urban Population: From 36% to 84% of total population (1950 to 2010)

Family Size Decreased

The Production of Space in Brazilian Cities High Demand for Urban Dwellings SocioDemographic Changes Market Dynamics Urban Speculation Urban dwellings/locations seen as an investment

Empty Dwellings vs. Housing Deficit (2010) 6.07 million dwellings are empty (urban speculation) 5.8 million dwellings are needed (housing deficit) Photo: Henrique O. Loeffler (Flickr)

Urban Segregation Imposes obstacles that contribute to perpetuate poverty Photo: Tuca Vieira

Residential Location Choices Residential Preferences of Families (Stated or Revealed) BUT… How genuine are these preferences? Are the families (consumers) really “sovereign”?

Consumer Sovereignty vs. Producer Sovereighty

John Kenneth Galbraith ( ) Argues that far from consumers deciding what should be produced, it is the producers who are deciding what should be produced, on the basis of what makes the most profits for them Consumer Sovereignty vs. Producer Sovereighty

The Kaleidoscopic City By Pedro Abramo Chosen as an alternative framework to model residential location choices 1998 (French), 2007 (Portuguese), 2011 (Spanish)  Builds on the heterodox economic literature to develop an interpretation of how residential choices are made  Criticizes the idea that the spontaneous action of market forces promotes higher levels of consumer satisfaction and efficiency of resource use Focus on the role of entrepreneurs actions in influencing the decisions of families

Families (Consumers)  Perceive urban space as a mosaic of neighborhood externalities  Preference for places where lower-income families are not present  Location Choice = Investment Choice (human capital, speculation…) But...  Families’ decisions are simultaneous and decentralized  Neighborhood externalities are constantly changing Opportunistic Decision = Crucial Decision Radical Urban Uncertainty

Entrepreneurs (Producers)  Contribute to Urban Uncertainty through the Practice of Innovation  Build dwellings that are are more innovative/attractive to avoid competition with old housing stocks and redirect the demand to new locations  Promote a fictitious depreciation of old housing stocks: not a physical depreciation, but a depreciation in the social status of residents living in the location Innovation = Crucial Decision Radical Urban Uncertainty

How do market participants make their decisions? Techniques suggested by Keynes (1936) Imitation Under this context of uncertainty… Mimetic behavior converging to an Urban Convention Collective conviction regarding the type of family that is going to live in a particular location

Agents need to identify who is better informed and should be imitated Keynesian speculator : Task is to predict the psychology of the market By adopting a mimetic behavior… In the residential market Schumpeterian Entrepreneur = Keynesian Speculator “Better-informed agents”, since they are able to promote innovations that depreciate existing areas. Urban Convention Element of spatial coordination that results from a mimetic speculative process where families elect the entrepreneur’s actions as source of information

This tension between the order promoted by urban conventions and the disorder introduced by crucial decisions (innovations) reveals the context of radical urban uncertainty and kaleidoscopic spatial order that characterizes the market coordination of the urban space CONVENTIONS Order CRUCIAL DECISIONS Disorder Quite different from the stable and efficient process advocated by the neoclassical approach... Order vs. Disorder

SPECULATOR Anticipates Changes (Rumors) NEW CONVENTION Herd Behavior ORDER Blocks Speculation Belief is confirmed RUPTURE: INNOVATION Crucial Decision END CONVENTION Fictitious Depreciation of Stocks DISORDER Uncertainty Speculation Returns Order vs. Disorder

The Kaleidoscopic-City Model Seeks to investigate how crucial decisions made by entrepreneurs (innovation) contribute to change the urban spatial order and the lifecycle of different regions in a city rorschmap.com

The Kaleidoscopic-City Model Two Types of Agents Families (Consumers): hierarchized by their income Entrepreneurs (Producers): innovative or imitative Environment Regions (set of cells): can be, temporarily, be recognized by the urban convention as the region where the richest families are going to live, “urban-convention region” Cells: can be urbanized or not. Once urbanized, they can accommodate one or more dwellings, depending on the maximum density allowed in the region. Dwellings located in a cell have a certain degree of innovation and can be occupied by family agents.

Process Schedule Set up initial state of the system Set up initial state of the system Create new families and expand urbanized areas Entrepreneurs’ actions: Build new dwellings and evaluate urban convention Entrepreneurs’ actions: Build new dwellings and evaluate urban convention Families’ actions: Move to a different location and evaluate urban convention Families’ actions: Move to a different location and evaluate urban convention Compute and report output measures SIMULATION CYCLE t n+1 = t n + 1

Process Schedule Set up initial state of the system Set up initial state of the system Create new families and expand urbanized areas Entrepreneurs’ actions: Build new dwellings and evaluate urban convention Entrepreneurs’ actions: Build new dwellings and evaluate urban convention Families’ actions: Move to a different location and evaluate urban convention Families’ actions: Move to a different location and evaluate urban convention Compute and report output measures SIMULATION CYCLE t n+1 = t n + 1

Initial State of the System  12 Regions  Urbanized area in the center of the region  20 Families with different income level, occupying 20 dwellings with equal degree of innovation  Profile of Entrepreneurs (innovative or imitative) set according to a user-defined probability Urbanized area (t=0) Higher Family Income Level Predefined regions Family agent (consumer) Lower

Process Schedule Set up initial state of the system Set up initial state of the system Create new families and expand urbanized areas Entrepreneurs’ actions: Build new dwellings and evaluate urban convention Entrepreneurs’ actions: Build new dwellings and evaluate urban convention Families’ actions: Move to a different location and evaluate urban convention Families’ actions: Move to a different location and evaluate urban convention Compute and report output measures SIMULATION CYCLE t n+1 = t n + 1

Process Schedule Set up initial state of the system Set up initial state of the system Create new families and expand urbanized areas Entrepreneurs’ actions: Build new dwellings and evaluate urban convention Entrepreneurs’ actions: Build new dwellings and evaluate urban convention Families’ actions: Move to a different location and evaluate urban convention Families’ actions: Move to a different location and evaluate urban convention Compute and report output measures SIMULATION CYCLE t n+1 = t n + 1

Convenient? Is demand supplied? Stop Choose plot and build new dwellings Select region to build Innovator? Select Entrepreneur Establish new convention Evaluate current convention Entrepreneurs‘ Actions

Process Schedule Set up initial state of the system Set up initial state of the system Create new families and expand urbanized areas Entrepreneurs’ actions: Build new dwellings and evaluate urban convention Entrepreneurs’ actions: Build new dwellings and evaluate urban convention Families’ actions: Move to a different location and evaluate urban convention Families’ actions: Move to a different location and evaluate urban convention Compute and report output measures SIMULATION CYCLE t n+1 = t n + 1

Any adequate location? Move to selected location Evaluate alternative locations Evaluate current location: Neighborhood externality and dwelling innovation Evaluate current location: Neighborhood externality and dwelling innovation New in the city? Select Family Stay in current location It needs/wants to move? Families‘ Actions

Process Schedule Set up initial state of the system Set up initial state of the system Create new families and expand urbanized areas Entrepreneurs’ actions: Build new dwellings and evaluate urban convention Entrepreneurs’ actions: Build new dwellings and evaluate urban convention Families’ actions: Move to a different location and evaluate urban convention Families’ actions: Move to a different location and evaluate urban convention Compute and report output measures SIMULATION CYCLE t n+1 = t n + 1 a.Density of dwellings in each region b.Average income of residents in each region (proxy of land value) c.Spatial isolation of income groups

Families‘ Response to Entrepreneurs‘ Actions Lower Income Higher Income Without Innovation Spatial Isolation of Wealthier Families Lower Isolation Higher Isolation

Lower Income Higher Income Families‘ Response to Entrepreneurs‘ Actions With Innovation Spatial Isolation of Wealthier Families

Urban Regions Density of Dwellings Dwelling’s density Without Innovation With Innovation Time Dwelling’s density

Urban Regions Mean Income of Families Mean Income Without Innovation With Innovation Time Mean Income

(a) DENSITY OF DWELLINGS (b) FAMILIES’ MEAN INCOME time (c) CONVENTION REGION R1 R2R3 R4 R5R4 R3R2 Region Green (R1) Region Brown (R2) Region Pink (R3) Region Red (R4) Region Orange (R5) Other Regions transition phase 1 phase 2 phase 3 fictitious depreciation increase in prices Regions‘ Life Cycles

Source: ACONVAP – Associação das Construtoras do Vale do Paraíba Empirical Data: São José dos Campos DWELLING UNITS DWELLING UNITS UNDER CONSTRUCTION

Jardim Aquárius - Urban Convention- Empirical Data: São José dos Campos DWELLING UNITS DWELLING UNITS UNDER CONSTRUCTION Source: ACONVAP – Associação das Construtoras do Vale do Paraíba

Final Remarks  The Kaleidoscopic-city model explores a heterodox perspective of urban economics: focus on the impacts of entrepreneurs’ decisions  In pursuit for higher profits, entrepreneurs manipulate the sovereignty of consumers through the practice of innovation  Entrepreneurs: from the neutral position of price-takers to an active role as price-makers

Final Remarks  The framework/model addresses only the formal market  The model considers a single “urban convention”, but different conventions could be simultaneously represented  The role of the State, as regulator agent, should be included Limitations and Perspectives

Final Remarks  This alternative way to envision the residential market has implications for the future urban order and, consequently for the development of urban policies.  Instead of adopting the neoclassical exchange paradigm, the model is built on the Keynesian speculative-financial paradigm.  Studies and policies developed under this perspective should focus less on economic predictions and more on the historical process of urban development and the possibility of having economic agents making crucial decisions that redefine the course of history

Thank You! Flávia Feitosa Antônio Miguel Monteiro

Photo: Fabio Venni Urban Conventions and Residential Location Choice Exploring a Heterodox Perspective of Urban Economics with a Spatially-Explicit Simulation Model Flávia F. Feitosa Antônio Miguel V. Monteiro Earth System Science Center – CCST Earth Observation Coordination – COBT National Institute for Space Research - INPE CAMUSS - International Symposium on Cellular Automata Modeling for Urban and Spatial Systems November 8-10, 2012 — Oporto, Portugal