Models Of Land Use And Urban Governance

Published Date: 02 Nov 2017

Institute for Management Research, Radboud University Nijmegen, 3 Thomas van Aquinostraat, 6525 GD Nijmegen, the Netherlands

DRAFT VERSION

ABSTRACT

Spatial planning research underscores the need for innovative urban governance arrangements to deal with the challenges of increasingly complex urban dynamics. Although there is substantial debate about the most suitable governance modes, there is little systematic research on how different governance modes will affect urban patterns. Agent-based models (ABMs) provide us with an excellent tool to design alternative governance arrangements, and simulate their implications on urban dynamics. In this paper, we develop an ABM to test for archetypical governance modes. The model is based on the (Alonso-Muth-Mills) monocentric city model, where agents search for a residential location based on a utility maximizing decision rule, subject to a budget constraint, but we introduce agent heterogeneity. To this setup we add the role of developers and planners. Developers follow a decision heuristic in which they are discouraged from developing parcels with long approval times. Planners develop a Plan aiming to promote compact city development and protect nature areas. The role of these actors in the model depends on the specific governance mode.

INTRODUCTION

In the governance literature, the central debate revolves around the extent to which new modes of governance have emerged recently. Most authors agree that, in the late 20th century, there was a shift from the prevalent hierarchical, state-based (Jessop 2000) and managerial (Harvey 1989) governance towards market-based (Jessop 2000) governance, and further towards more "horizontal, hybridized, and associational forms" (Hill and Lynn 2005) of governance, usually dubbed "networks" governance. While the literature acknowledges that pure hierarchical, markets or networks modes are not likely to be found in real life, thinking in terms of archetypes can be useful since they represent extreme versions that demarcate the boundaries within which real-life governance processes can be positioned (Martens 2006). Hence, the three modes are usually described in terms of contrasting attributes. Whereas hierarchical governance is said to rest on a logic of provision, market governance is said to rest on a logic of exchange and network governance on self-organization. In spatial planning, these differences imply different modes of production of space. The hierarchical mode suggests strong reliance on the Plan as the central element steering urban development. Pure market-based governance would in principle mean the absence of the plan, and the primacy of developer’s decisions about urban development. The implications of networks governance for spatial planning are more ambiguous.

The discussion about governance modes is often made on ideological terms, pitching one mode against the other based on the merits of their internal logic and assumptions, but hardly ever on what implications they have on urban development patterns. In fact, these implications are to a great extent unknown. For example, do the differences between urban governance modes manifest spatially? How do they manifest? And is one mode of governance inherently more capable of producing certain central urban planning goals, such as containment of sprawl or the protection of natural amenities? These questions have been largely overlooked. To be sure, urban patterns are the result of many factors beyond governance, interacting in complex and idiosyncratic ways that cannot be captured in a simple urban model. However, it is legitimate to expect that, beyond the idiosyncrasies of each urban setting, there is also a systemic component to the relationship between urban patterns and governance models.

To explore whether such systemic differences in urban development emerge as a product of different governance modes, we develop a model of urban dynamics where different governance arrangements are simulated. In the model, agents represent households, developers and planners. Households search for a residential location that maximizes their utility, subject to a budget constraint. Developers’ behaviour depends on both market factors (demand) and planning constraints, such as building permits’ approval times. Planners’ develop an Urban Plan that specifies which parcels are to be developed and prescribes the maximum building densities per plot. Three governance arrangements, intended to represent archetypical governance modes described in the literature (hierarchical, markets and networks), are simulated. These modes are conceptualized in terms of the changing roles played by each agent.

The paper is organized as follows. In Section 2, we briefly discuss the literature on urban models and on governance models. In Section 3, we describe our operationalization of governance. In Section 4, we present the model and in Section 5 we present and discuss the results.

MODELS OF LAND USE AND URBAN GOVERNANCE

Land Use Models

The history of land use modelling is deeply rooted in Von Thunen’s agricultural land rent theory (1826), which prescribed the optimum spatial distribution of agricultural crops around a central market area. Von Thunen’s theory was developed in an urban context by Alonso (1964). Refinements to Alonso’s model gave rise to the Alonso-Mills-Muth monocentric city model, which is the foundation for urban economics. The model assumes a monocentric city where households commute to the city centre to work. Each household spends its total income on housing, commuting costs and a bundle of all other commodities. Both transport costs and unit price of land depend on distance from the city centre. The household’s residential location choice entails a trade-off between the cost of housing and the cost of commuting.

The central concept of Alonso’s urban land market theory is the bid-rent function, defined as the "maximum rent that can be paid for a unit of land at some distance from the city centre if the household is to maintain a given level of utility". Alonso assumes a competitive land market where households bid for available space and land owners offer land to the highest bidder. One important modification of Alonso’s model introduced by Muth was the relaxation of the closed city assumption. Unlike Alonso, Muth assumed that the city extended from its centre as far as necessary for the demand to equal the supply of housing services. Mills (1972) analyzed also the location of employment, relaxing the assumption of location of all employment at the CBD. In Mills’ model, commodity production competes with transportation for land.

The "new urban economics" took a deeper interest in the location choices of firms over those of households, and especially in the role of agglomeration economies as driving forces for city growth and for the endogenous creation of new centres (Ogawa and Fujita 1980; Fujita and Ogawa 1982). But while firm location may be a main force shaping regional patterns, residential location is also important, especially in what concerns problems of sprawl and the preservation of open-space amenities. The canonical monocentric city remains the usual starting point for modelling residential patterns. Customary refinements of the basic model include the introduction of income heterogeneity, taste heterogeneity, and of externalities and constraints in the use of land (congestion, zoning, segregation, fiscal jurisdictions) (Capello and Nijkamp 2004) as determinants of location choice.

A few authors have developed variations on the Alonso-Muth-Mills and the Fujita-Ogawa models in cellular automata (CA) and ABM environments. CA and ABMs are particularly useful to model neighbourhood effects and heterogeneity of space and agent tastes without having to deal with intractable mathematics. Filatova et al. (2009; 2011) present agent-based models of land markets, first reproducing the structure of the Alonso/Von Thünen model, and then adding to it, to investigate how urban morphology and land rents change are affected by the relative market power of buyers and sellers (Filatova, Parker et al. 2009), or by taxes on land use in a coastal zone (Filatova, Voinov et al. 2011). Caruso et al. (2007; 2009) present a model of residential growth that emphasises the path-dependency and uses a cellular automata (CA) approach to introduce endogenous neighbourhood effects. Households are assumed to trade-off neighbourhood density with housing space consumption and commuting costs, giving rise to the emergence of discontinuous spatial patterns.

Effects of Accessibility on residential patterns

Accessibility is the main driver of location choice in Alonso-Muth-Mills type models. There is no end of accessibility measures, and each measure will reflect some nuance in the concept of accessibility. The simplest measures compute accessibility as inversely proportional to the distance to a destination of interest. In the transport literature, however, the use of negative exponential as a distance weighting functions is widespread. In the original model of Alonso (1964), accessibility enters the utility function directly, not just in the budget constraint. As Filatova et al. (2011) point out, in this way, the disutility of commuting time is represented separately from travel costs.

When no neighbourhood externalities are considered, and space is isotropic, each location can be differentiated from another only by its distance to the CBD. Households will then choose to be as close as possible to the CBD, so as to minimize commuting costs. This leads to a compact concentric growth around the CBD (Caruso, Peeters et al. 2009).

Effects of Household Income on residential patterns

Increasing household income or decreasing the cost of commuting leads to increased urban expansion. This is the well known suburbanisation effect (Caruso, Peeters et al. 2009) that partially explains the growth of the world’s cities with the evolution of transport technology, and decreasing commuting costs all throughout the 20th century.

Effects of Open-space amenities on residential patterns

There is considerable empirical evidence that households value the existence of green areas in the proximity of their houses. This is especially true if green areas are contiguous to the housing location, allowing for a sense of living in open-space, with broad views and close to nature. Under the presence of these open-space amenities, the city develops a discontinuous manner at the fringe. For example, Caruso et al. (2009) show that when greenness is more valued, the discontinuous fringe (mixed area) is wider, the speed of expansion of the commuting field increases, and the compact core of the city emerges later (the expansion of the commuting field slows down later). When households consider openness over a larger neighbourhood, the commuting field expansion is more rapid, rural interstices are larger, and the local arrangement patterns are more diverse (Caruso, Peeters et al. 2009).

Models of developers’ behaviour

Most models of urban dynamics do not take into account the role of developers in shaping spatial patterns. More often than not, land owners (usually farmers) are assumed to either use the land to derive a (agricultural) rent from it or to sell it directly to another land user, such as a household. This formulation ignores the central role developers play in shaping urban development.

Two models of developers’ behaviour provided the crucial insights for our model. Schaeffer and Hopkins (1987) model developers’ decision making processes under uncertainty. The authors’ focus is on the decision to obtain more information, at each stage of the development process, in order to reduce uncertainty. The uncertainty resides in the fact that, in the beginning of the process, the developer does not fully know the characteristics of the land and of the project, but also in the fact that he has to obtain a set of rights (property rights, building permits) form the planning agency. The uncertainty as to whether he can obtain the necessary approvals is represented by a random variable, because the planner and the planning (regulatory) system are not explicitly modelled.

Czamanski et al. (2011; 2012) introduce the concept of Characteristic time to explain urban development patterns. Characteristic time represents the "duration of the process of land development, from the purchase of land rights by developers until the realization of income from it" (Czamanski and Roth 2011). Their main insight is that it is the duration of the development process, and not prices of land and real-estate products, that constitutes the critical variable in developers’ decision-making. In their model, developers face a simultaneous decision of where to build and at what intensity (height). The duration of the development process can vary throughout space and time and project due to the variation in approval times, reflecting changes in planning philosophy and the reaction of planning authorities to changes in a variety of conditions in the particular town. This model can explain the leapfrogging of vertical development observed in cities like Tel-Aviv. Similarly, Schaeffer and Hopkins (1987) suggest that differences in project approval time are critical to define the final form of urban development. They point out: "If the project stays within the current constraints set by zoning regulations and building codes, the approval should be obtained easily. The [developer] may, however, want to apply for a zoning change, for a variation of other existing regulations, or for other changes in the rights and obligations attached to the land (...) It may be necessary to compromise with the regulatory agencies. In the process of negotiation that may precede approval, the final shape of the project will be determined".

These insights suggest that the planning system imposes constraints on developers’ decisions that can be operationalized through a time variable reflecting approval times for different developments. Long permit approval times affect developers’ decisions because they imply a delay to realize profits, during which the money invested in the project is "stalled" and thus unproductive. Furthermore, the final form of the development (the intensity or building height) is also affected by planning prescriptions, in that projects that are more similar to what the planner prescribes should be granted approval more easily (or swiftly) whilst projects that deviate from the plan should take longer.

Governance and institutions

In his theory of Local Expenditures, Tiebout (1956) devises a model where citizens move between local jurisdictions that offer different bundles of public goods. Citizens then sort themselves according to their (heterogeneous) preferences, and are said to vote with their feet. Tiebout argued that this kind of competition between the local jurisdictions could lead to efficient outcomes.

Kollman et al. (1997) show that the performance of different political institutions in sorting citizens differs. The authors test three different institutions – democratic referenda, direct competition and proportional representation, concluding that some result in higher levels of aggregate utility.

MODELLING GOVERNANCE

In this paper, we want to explore the implications of different governance modes on urban development patterns. By urban development patterns we mean the transformation, in space and time, of non-urban land into urban (residential) land and the intensity of this development, in terms of the built density at each location.

Most models of urban dynamics assume that, for each particular plot of land, one single agent (usually a land-owner, a farmer, a household or a firm) has the power to decide on its land use, whether by choosing to sell it or not, or by choosing to locate there. In our model, the land use of each location is the outcome of a multi-actor decision process, rather than the product of a decision by one individual actor. Urban governance refers to the particular constellation of institutional actors and rules that shape this decision process, and, ultimately, urban development patterns. The governance level is therefore distinct from the level of individual decision-making, which comprises the individual goals, motivations and preferences of single actors. This distinction is important since we want to simulate different governance modes without introducing too many arbitrary assumptions about how individual decision-making behaviour changes in relation to changes in governance.

We choose a rather symmetrical conceptualization of the three modes of governance to allow us to minimize arbitrary assumptions and variation between the three modes. Thus, each governance mode will be characterized by a different mode of production of urban development that differs only in that it ascribes different roles to the same actors, while keeping the individual goals and motivations roughly the same.

In this conceptualization, market governance is characterized by the absence of constraints on the operation of the market. The developer is the agent that drives urban growth and for the most part determines its form, since he is the agent that decides on which locations to build and at what density. However, the developer must cater to the preferences of the households, and thus the households moderate or regulate his actions. In this laissez-faire mode, the planner is passive, imposing virtually no constraints to the actions of developers.

Figure . Conceptual framework

In the hierarchical governance mode, the central concept is that the provision and distribution of welfare is organized centrally by a planner. In what concerns urban development, this does not mean that the planner will provide the built environment directly, but rather that he will provide for spatial planning - the set of rules and guidelines to which urban development should obey (and some amount of public space and public buildings, which are not covered by our model). Thus, the developer is still the agent that initiates urban development, but his actions are regulated by the planner. The planner regulates developers actions by imposing constraints on the maximum density allowed in each plot.

The networks mode is characterized by a more self-organizing and grassroots process, where households themselves pick the locations and densities at which they would like to reside, and prompt developers to build at their desired density and location. This process forms neighbourhoods / networks of self-selected communities of households with like preferences. In this governance mode, households are at the forefront of urban development. Developers play a regulatory role, since they are prompted by the households to develop certain locations, but will only do so at a profit. The planner is passive.

Planner

Developers

Households

Hierarchical governance

regulator

initiators

regulators

Market governance

passive

initiators

regulators

Network governance

passive

regulators

initiators

Table Role of different agents in different governance modes.

In the above formulation, the planner’s role is either a passive or a regulatory one. Regulation is understood as the setting of rights, and it changes the range of permissible alternatives or the incentives to act in a particular way (Schaeffer and Hopkins 1987). However, if we take a long term perspective, these rights and prohibitions are not static or insurmountable. Rather, they are not as much prohibitions as they are hindrances. Developing at higher densities than allowed might be possible depending on the ability of developers to endure long approval periods. Especially powerful developers might be willing to sit through long approval processes, if this proves profitable. At its extreme, if developers are given a lot of power, this operationalization of hierarchical governance evolves to market governance.

THE MODEL

Environment

The model is set on an m x m discrete square grid. The Central Business District (CBD) consists of a point located at the centre of the grid, at coordinates (0, 0). Each grid cell is taken as the areal unit and represents a plot of land. Each plot can house a varying number of households depending on the density at which it is built. The initial setup for the model is represented below. The area is divided in 3 zones, which we will refer to as centre, nature and suburbs. The centre consists of a 10 x 10 cells area and contains the CBD at the origin. Nature is located to the east of the centre, and represents an area with highly valuable natural resources that the planner wants to protect. The suburbs are the area west of the centre.

urbanform10_hierarhies0

Figure Initial setup for the model

Agents

Agents in the model include households, developers and one planner.

Households’ residential choice

Each household is represented by a single agent. Households from the "rest-of-the-world" migrate into the city at a given exogenous rate, and search for a place to live. Households’ choice of a home location depends on the available rental opportunities at the time of their arrival to the city and on their preferences regarding house location and built density. More specifically, in their decision, the household evaluates the level of accessibility (to the CBD) of the location, the presence of nature in the surroundings, and the housing typology (single-family vs building housing several households per plot), besides cost factors such as commuting costs and rent. Agent Utility follows a Cobb-Douglas formulation:

Hence, utility depends on the consumption of a non-spatial composite good Z (chosen as the numéraire), a residential plot (chosen as the surface unity), and externalities that are of three types: Accessibility, A, representing preferences for proximity to services and contacts, schools, public transport etc, Environmental, E, representing preferences for open and natural surroundings, and crowding disamenities D, representing a preference for single-family housing, and lower plot densities in general. Households commute to the CBD to work and to shop and earn a fixed income, Y, a part of which is allocated to commuting costs, another part to the house rent and the remainder to consumption of the good z.

Accessibility of a location to the CBD is computed using a negative exponential formula, which is considered by many authors the most suitable approach to modelling accessibility (Bodenmann and Axhausen 2010), and in any case the most often used and most closely tied to travel behaviour theory (Handy and Niemeier 1997). The accessibility measure has the following form:

For which ij is the cell for which accessibility is being computed; c is a constant; θ is a distance decay parameter and distanceCBDij is the topographical distance of the cell to the CBD. For chosen parameters, Equation 1 renders values that normalize to a 100 to 0 scale:

Figure Accessibility with distance from CBD for c = 100 and θ = 0.50, θ = 0.15 and θ = 0.05.

The presence of nature in the surroundings of a location gives rise to a positive environmental externality valued by households. E is a proximity effect that decreases quickly with distance, reaching no further than the location’s immediate neighbourhood. Eij is therefore a local dispersion force (Caruso, Peeters et al. 2007), unlike Accessibility, which is a "global centripetal" force. The development of the neighbouring cells will result in loss of green area, therefore diminishing the value of the externality. As in Caruso et al. (Caruso, Peeters et al. 2007), we assume E to be a negative exponential of a variable that denounces the lack of green in the area. We take this variable to be the number of "non-nature" cells in the 8-cell neighbourhood, Bij.

In what concerns building typologies, households are assumed to prefer single family housing to multi-family, that is to say, they prefer the lowest possible number of families to be housed in their plot. Increases in the number of households per plot represent a disutility from the point of view of the household. We call building density Hij to the number of households in cell ij, which can vary from 1 to 10.

The value of the exponents η and κ determines the shape of the externalities.

Figure . Environmental externality vs number of non-nature cells in the neighbourhood for various values of the exponent n (the same shapes apply to Density externality vs plot density)

Households are heterogeneous in what concerns their preference profile for the location’s attributes. Some households attribute more importance to Accessibility while others find that the Presence of nature or Building typology weighs more in their decision. The households’ preference profile is the vector of weights [α,β,γ]. Alpha, Beta and Gamma reflect the strength of households’ preferences concerning, respectively, Accessibility, Environmental and Building type (Density). The preferences of the households for each factor are assumed to be perfectly independent of each other and of household income.

When choosing a house, households choose out of the available rental opportunities, the one that maximizes their utility U and is affordable given their budget, net of transport costs. The exponents θ, η and κ are parameters of the simulations; Alpha, Beta and Gamma are random parameters not controlled for in the simulations.

Developers

A number of developers operate in the city. Their goal is to build houses and rent them at the highest profit, and their program is to find the best locations, assess the potential revenue that can be obtained from building in a determined location at a certain density, assess planning constraints, build, set the rent, and rent the houses to the highest bidders.

Finding the best locations

Developers have access to limited information about the housing preferences of their potential tenants. In our model, this is translated in the fact that developers only have access to the average preferences of the households that already inhabit the city, and know nothing about the profiles of the future population. Also, developers do not evaluate all possible locations for building, but rather probe a limited number of randomly chosen locations, from which they select the 3 most promising for further evaluation. Developers evaluate 9 different alternatives, corresponding to building at 3 different plot densities for each of the 3 selected locations. They compare the revenue that can be obtained in each alternative (developing costs are assumed not to differ significantly).

Assessing planning constraints

Developers will develop the location and at the density that affords them the highest revenue, given their assessment of demand, unless there are severe planning constraints, in which case they will consider the second highest revenue alternative (and the third, fourth, etc until a satisfactory one is found). Severe planning constraints mean, in this case, large delays in obtaining a building permit.

To represent planning constraints, we adapt the concept of Characteristic time introduced by Czamanski and Roth (Czamanski and Roth 2011; Czamanski and Broitman 2012) and discussed in section 2.1. In our model, however, we focus on the time to approval – assuming the other components of characteristic time do not vary between the alternative developments considered. Longer times to approve a permit are a result of the fact that the density at which the developer would like to develop is different from the density prescribed by the urban plan for the location in question. Developers have different degrees of resilience to delays in permit approval. Some developers can sustain investments for longer and thus are thus not discouraged by long approval times. This is reflected in the developer’s attribute Patience. Patience reflects the number of ticks that a developer is willing to wait for a permit without being discouraged from developing. Patience is a random variable generated through a Poisson-distribution with mean equal to Developer_power. The Poisson distribution is a discrete probability distribution that expresses the probability of a given number of independent events occurring in a fixed interval.

developer power = 5

developer power = 20

developer power = 40

Figure Developer’s Patience (vertical axis) for 10 developers, generated through a Poisson distribution with mean 5, 20 and 40.

Building

When both demand factors and planning constraints are considered, the developer makes his decision of whether or not to build in a location. Once he has decided, he has to wait the due approval time for the development to be built. After this time, he can rent the houses in the development.

Setting the rent

Usually, the price of a housing good unit is set in relation to a reservation bid rent, set to be equal to the price of undeveloped land, which is assumed to be at least no less than the average price of agricultural land. As the argument goes, the plots that are not converted to urban use only provide the agricultural rent to the landowners, or at least, average agricultural land price serves as opportunity costs for conversion into urban land (Filatova, Voinov et al. 2011). But as these authors point out, in the real world, agricultural productivity, cost of production and prices for agricultural goods, affecting price for agricultural land are heterogeneous (Filatova, Voinov et al. 2011), and thus do not provide a solid base for price formation. In the present model, the developer alone sets the price of the house goods, on the basis of his knowledge of the market, and households are takers of that price. As was mentioned before, developers are bounded in their access to information, so that they only know the average taste profile of the present population. Thus, their market estimates are based on the willingness and ability to pay of the average household, which is a hypothetical agent with income and preference profile equal to the mean of all household agents in the population. According to (Filatova, Parker et al. 2009), willingness to pay can be calculated according to:

Where Y is the income net of commuting costs and b is a constant representing the price of the good z. This function tends to Y as U → ∞, meaning that individual WTP increases with utility but does not exceed the budget. The value of parameter b can be thought of as a proxy of the affordability of all other goods to reflect their relative influence on the WTP for housing. According to (Filatova, Parker et al. 2009) this function exhibits the main qualitative properties of the neoclassical demand function.

Figure . Willingness (constrained by ability) to pay of the average household (income = 1000) for a house depending on accessibility of the location (depending on distance to the CBD), constant nature ratio and density.

Planner

The planner’s job is to develop the Urban Plan for the area. The Urban Plan should implement the planner’s policy for urban development, which is that urban development should remain compact (minimize sprawl by concentrating urbanization) and to minimize urban pressure on the green areas (steer urban development away from nature and minimize densities close to nature). For simplicity, the Plan’s only prescriptions concern the maximum densities allowed for each plot. At each plan update, the planner decides which undeveloped plots should change planning status (from undeveloped to planned) to allow urban development, and which maximum density should be allowed for the new plots. Table describes the criteria used by the planner in more detail. When an area has been developed to an index greater than a threshold, the Plan classifies it as urban consolidated; then, if there is need for the city to extend and open new plots for development, the planner searches for the plots that are contiguous to consolidated areas and far from natural areas.

Built density higher than 8

Built density higher than 6

Nature in surroundings lower than 3

Contiguous to consolidated distance to nature planned

Plot attributes

Planning status

built density > 6 OR

consolidated

built density > 8 AND next to nature

contiguous to consolidated AND far from nature

to be developed

contiguous to consolidated AND close to nature

to be developed

undeveloped

Table

How do the plan prescriptions impact on the developers’ decisions? When developers have chosen which plot to develop and at what density, they compare it with the maximum density allowed for the plot by the plan. If the density at which they want to develop is higher than the planned density, than developers know they face a long approval time (see Table Approval times according to built densities.). If the developer’s Patience is higher than the approval time, the developer will still decide to develop the said plot; otherwise, he will abandon this project in favour of his next most profitable project.

Classification

Approval time

density ≤ planned density

density > planned density

consolidated

low approval time (1 tick)

medium approval time (15 ticks)

to be developed

low approval time (1 tick)

medium approval time (15 ticks)

to be developed (next to nature)

low approval time (1 tick)

high approval time (25 ticks)

undeveloped

-

high approval time (25 ticks)

Table Approval times according to built densities.

Governance

Hierarchies

Markets

Networks

1 Planner plans

1 Developer develops

3 Households settle

1.1 Updates information

nature_ratio

planning_status

1.1 assess market

3.1 Create households

1.2 Estimates urban pressure

1.2 select locations

Utility average household

3.2 Evaluate utility

1.3 Decides on nº of zones to develop

1.3 choose density as a function of characteristic time

3.3 Select location and move in

1.4 Selects zones

Contiguous to consolidated & nature lower than threshold

3 Households settle

1.5 Set densities as a function of nature

3.1 Create households

1.6 Set characteristic time as a function of density

3.2 Evaluate utility

2 Developer develops

3.3 Select location and move in

2.1 assess market

2.2 select locations

2.3 choose density as a function of characteristic time

3 Households settle

3.1 Create households

3.2 Evaluate utility

3.3 Select location and move in

Dynamics

The model is run for x time steps. At each time step, all developers are called upon to decide on whether to build and in which locations. Next, at each time step, x new households enter the model. When households enter the model, they evaluate all available (empty) houses for their utility, which depends on accessibility, presence of nature in the surroundings and plot density) constrained to their budget and choose to locate at the house that maximizes their utility. A household that fails to find a house leaves the model. Planners re-evaluate the Plan at certain time intervals.

Results

urbanform11_markets25

urbanform11_markets50

urbanform11_markets100

market governance t = 25

market governance t = 50

market governance t = 100

urbanform11_hierarhies25

urbanform11_hierarhies50

urbanform11_hierarhies100

hierarchical governance t = 25

hierarchical governance t = 50

hierarchical governance t = 100

developer power = 15

urbanform11_hierarhies25

urbanform11_hierarhies75

urbanform11_hierarhies100

hierarchical governance t = 25

hierarchical governance t = 50

hierarchical governance t = 100

developer power = 35

CONCLUSIONS