DISSERTATION PROPOSAL

Informality, Land Use Regulation and Housing Price: A Model Applied to Curitiba, Brazil

Draft

August 2007

Maria Teresa SouzaDepartment of Urban Studies and Planning

University of Maryland at College Park

ABSTRACT

This study examines the relationship between land use regulation, housing price, and informality, in the

metropolitan area of Curitiba, Brazil. It tests whether more restrictive urban regulation increases formal

housing price, and whether informal housing might be an imperfect substitute for formal housing, causing

the quantity demanded for informal housing to rise with an increase in formal housing price. Using a

simultaneous equation model, the study conducts a spatial regression analysis to understand the

magnitude of the effect of urban regulation on formal housing price and the effect of rising formal housing

price on the quantity of informal housing.

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INTRODUCTION

Most urban growth over the past 50 years has taken place in developing countries. Between 1950

and 1995 the number of cities in developed countries doubled while it increased six fold in developing

countries (Linden 1996). An exacerbation of this trend is anticipated in the future. A recent World Bank

study expects the urban population in industrialized countries to grow by 11%, from approximately 0.9

billion in 2000 to 1 billion in 2030. In contrast, developing countries’ cities are expected to double in the next

thirty years, from approximately 2 billion in 2000 to approximately 4 billion in 2030 (Angel, Sheppard and

Civco 2005).

A major feature distinguishing urban growth in developed countries from most developing countries

is the role played by the informal sector. While in industrialized countries housing is largely delivered in

compliance with property rights regimes and urban regulations (such as land use planning, building codes,

and subdivision standards), in most developing countries, low and moderate income households, which can

constitute 50% of the urban population, are housed by the informal sector (Dowall 1992).

The combination of these trends in developing countries – an accelerated urban growth associated

with high levels of informality – has brought renewed attention to the study of developing cities in general,

and of urban informality in particular (e.g. Roy and Alsayyad 2004, Drakakis-Smith 2000, Pamuk 2000, and

Barross and Linden 1990). The central problem many authors face is that most urban theories and

empirical models are rooted in the developed world and do not take into consideration the specificities of

urban development in developing countries, such as the role of the informal sector (Roy 2005).

The literature attempting to model the relationship between land use regulation and housing price

is a case in point. While several studies analyze the effects of land use regulation on housing price, most

studies focusing on developing countries do not try to understand the relationship between urban

regulation, housing price and informality. In many cases, these studies have concluded that land use

regulation increases housing price. However, it is not clear whether land use regulations and rising housing

price contribute to the expansion of the informal housing market. Another limitation of this literature is that,

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in most cases, it doesn’t take into consideration the spillover effect of the regulatory environment in one

municipality on the price and size of the informal sector of neighboring municipalities.

The purpose of this study is to address these issues. The study will develop a model to estimate

the effect of urban regulation on formal housing price and the effect of changes in housing price on the

quantity of informal housing, taking into account the spatial interaction between municipalities. The model

will be applied to Curitiba, a metropolitan area in the south of Brazil comprised of thirteen local

governments, with a total population of 2.6 million (2000).

Although Curitiba is internationally renowned for its success and innovation in the implementation

of urban planning policies, the city is facing major challenges with a rapid increase in population, brought

on by a significant investment from multinational corporations. Since the early 1990s, industries have been

moving out of Sao Paulo, and Curitiba has been a major destination because of its quality of life. The area

has attracted corporate entities such as Renault (US$1 billion), Audi/Volkswagen (US$800 million), and

BMW/Chrysler (US$500 million) (World Bank/Cities Alliance 2005a). The metropolitan area has

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experienced an annual population growth of 3.12% between 1991 and 2000. As not all households that

move to the city can afford a house in the formal market, there has been an expansion of informal land

developments.

Viewed in this context, Curitiba provides an excellent opportunity to understand whether land use

regulation is playing a major role in increasing housing price and pushing people to the informal housing

market and how urban regulation in one municipality affects housing price and informality in another.

This dissertation proposal is structured in five sections. It starts with brief background information

about urban regulation in Brazil and follows with a description of the research question. A review of the

literature on the subject is followed by the methodology and the expected timeline for the completion of the

dissertation.

BACKGROUND

Brazil is a Federative Republic comprised of 26 states and 5,564 municipalities (IBGE 2000).

According to the Federal Constitution of 1988, the Union and the States can establish legislation, general

norms and guidelines for urban development but municipalities have autonomy to carry out urban

development policies within their jurisdiction.

The Federal Constitution introduced several innovations including: the requirement for

municipalities with more than 20,000 inhabitants to approve and turn into law a master plan; the recognition

of the right to property (which is not the same as the guarantee to property right, something the Constitution

left out as noted by Lomar 2004); the requirement that real estate property fulfill a social function; and the

provision of practical instruments to ensure the fulfillment of the social function of real estate property.

Some examples of these instruments are the expropriation power to municipalities over vacant or

underutilized urban land and the acquisition of domain through “usucapião” (the concession of use of a plot

no larger than 250 square meters to a person who doesn’t own any other property and has lived in it for five

consecutive years).

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Despite these innovations, a study on the impact of the Constitution’s new provisions on urban

development in municipalities in the state of Sao Paulo found that 40% of surveyed municipalities with

more than 20,000 inhabitants didn’t have an approved master plan, 10 years after the approval of the

Constitution (Rolnik 1998). This study also showed that the practical instruments to ensure the fulfillment of

the social function of property had mixed results. Some were widely applied, such as the special legislation

for social interest housing, while others were scarcely adopted, such as the Special Zones of Social Interest

(ZEIS, for its Brazilian acronym). Additionally, the study raised some doubts as to whether the new

instruments were effective in solving conflicts in relation to the use and occupation of urban property.

In 2001, these provisions were enacted by the Federal law 10.257, also known as the City Statute.

This is the first law on urban policy in Brazil and it is acclaimed for acknowledging the existence of the

informal sector and for proposing instruments for the regularization and/or development of areas occupied

by low income households (Lomar 2004, Azevedo Netto 2003). The law listed 35 instruments municipalities

could use as part of their urban policies, regulating eleven of these instruments, such as the master plan,

progressive urban property taxes, and ZEIS. It is still early to assess the impact of the City Statute, but

several urban planners that are familiar with the reality of the informal sector in Brazil seem to agree that

this law is a major step in making the regulatory environment more flexible for the regularization of informal

land development and for the expansion of affordable housing.

In addition to this recent regulatory framework, there is one more law at the federal level that

affects urban development. Federal law 6.766 of 1979, as amended by Federal law 9.785 of 1999, is the

main legislation affecting land subdivision and development. Some of its provisions include: (a) the

requirement that municipalities designate an urban perimeter, outside which land subdivision is prohibited;

(b) the requirement that the land subdivision is located in an area with public infrastructure; and (c) the

requirement of a minimum plot area of 125 square meters and plot frontage of 5 meters. A major provision

of law 6.766/79 discarded by law 9.785/99 was the requirement that at least 35% of the land to be

subdivided be set aside for streets, parks and public facilities. The municipalities now define this

percentage.

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At the local level, the main instruments for the regulation of urban development are the master

plan, the zoning law, the municipal law for land subdivision, and the building code.

RESEARCH QUESTION

This study will try to understand the relationship between housing price, land use regulation and

informality by addressing three related questions: (1) Does a stricter regulatory environment increase

formal housing price? (2) Does an increase in the price of formal housing lead more people to find a

housing alternative in the informal sector? (3) Does the regulatory environment in one municipality affect

the housing price and the quantity of informal housing in a neighboring municipality located in the same

metropolitan area?

Three hypotheses will be tested to answer these questions. The hypothesis underlying the first

question is that holding other supply and demand factors constant, a more restrictive land use regulation in

a given municipality increases housing price in the formal housing market of that municipality. The study

will attempt to get as many measures that capture the stringency of the regulatory environment as possible,

through the application of a survey on urban regulation in each of the thirteen cities in the metropolitan area

of Curitiba. Some of the regulatory measures that will be analyzed include: minimum plot size, percentage

of saleable area (the area that can be sold after taking away areas required for public use such as streets,

public schools and parks), minimum street width, and the number of months taken to get a permit for a new

land subdivision.

The hypothesis underlying the second question is that informal housing is a substitute for formal

housing, so that an increase in formal housing price causes the quantity demanded of informal housing to

increase, as informal housing replaces the use of the formal housing market. The study will measure the

quantity of informal housing using local government cadasters of land squatting (favelas) and irregular land

subdivisions (loleamentos clandestinos).

The hypothesis behind the third question is that there is a spatial interaction between

municipalities, so that the adoption of stricter land use regulations and the rise in housing price in one

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municipality will lead low income households to look for a housing solution in the informal market not only in

that municipality, but also in neighboring municipalities. The spillover effect of land use regulations adopted

in one municipality on other municipalities will be assessed by incorporating spatially lagged variables of

urban regulation and housing price in to the regression analysis.

LITERATURE REVIEW

Land use regulation, housing price and informality has been studied by two distinct bodies of

literature. The first is the literature that has attempted to measure the cost and, to a lesser extent, the

benefits of urban regulation and its effect on housing price. The second is the literature that has tried to

define and understand the operation of the informal housing market. Each is discussed below.

1. The Effect of Land Use Regulation on Housing Price

There is an extensive literature on the effect of land use regulation on housing price in developed

countries. In the United States, Fischel (1990) is one of the most cited reviews of empirical work on the

subject, while Quigley and Rosenthal (2005) is the most recent one. A review of the literature focusing on

developing countries is more difficult to find. Malpezzi (1999) has a review that covers developed,

developing and countries with transition economies, while Buckley and Kalarickal (2005) include the subject

as part of a broader review of housing policies in developing countries.

The literature on the effect of land use regulation on housing price in developing countries can be

divided in two major groups: the ones that focus on a specific city or country, and the ones that focus on a

comparison of countries. This section presents a summary of these approaches and discusses some of the

ways in which land use regulation has been defined and measured. For a summary of the literature review

in this section, see Annex I.

1.1. City or country specific studies

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The studies that are focused on specific cities or countries generally try to investigate the cost and

in some cases the benefits of a particular regulation or set of regulations. Trying to establish a typology of

this literature is very difficult, since there aren’t that many studies and the ones that exist focus on a variety

of regulations and employ a variety of methods. Table 1 provides a summary of measures and methods

employed in this literature.

Table 1Summary of Regulatory Measure and Methods Employed by City or Country

Specific Studies on Land Use Regulation and Housing Price in Developing Countries

Regulatory Measure Method

Floor Area Ratio (FAR)

Percentage of saleable area

Special Area of Social Interest (AEIS)

Apartheid Land Use Restriction

Government permission to land conversion from rural to urban for residential use

Combination of governmental regulatory policies on financing, tax and land use

Cost-benefit analysis

Description of observed housing and/or land prices and land use patterns,

Case studies of housing and/or land developments

Literature review

Simulation

Theoretical

Regulatory measures can be very specific regulations, like the Floor Area Ratio (FAR), which is

determined by dividing the building’s total floor area by the area of the plot of land where the building is

located. Or it can be a specific measure that is determined by a set of regulations, such as the percentage

of saleable area, which is the area that can be sold after taking away areas required for public use such as

streets, public schools and parks; or the zone defined as Special Area of Social Interest (AEIS, for its

acronym in Portuguese), which is a zone with less restraining regulatory requirements for the construction

of social housing. In some cases, the studies don’t focus on one particular regulation or set of regulations,

but in the whole regulatory environment that applies to different aspects of housing development, such as

financing, taxation and land use regulation.

The methods of analysis employed in this literature also vary significantly. Bertaud and Malpezzi

(2001) use a cost-benefit model to estimate the net effect of several land use and related regulations in

Malaysia. Benefits are roughly estimated comparing current regulation to a baseline based on market

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comparison and “international practice”. Their results show that under current regulation for low income

developments, only 44% of the land is saleable and FAR is only 0.23. As a consequence, developers have

a profitability that is 15% below the baseline of a middle income development. With suggested reduction in

road width, elimination of back alleys and reduction of corner setback requirements saleable area rises to

55% and FAR rises to 0.41. These changes make profitability rise 17% in comparison to the baseline of a

middle income development.

Studies that use a description of observed housing and/or land prices and land use patterns

include Hannah, Kim and Mills (1993), Bertaud (1996) and Somekh (1999). Hannah, Kim and Mills (1993)

analyze housing price series and land use pattern and, in addition, conduct case studies of five Seoul

development projects to estimate the effect of South Korea limitation on land conversion from rural to

urban. Their results show that national housing price rose more than twice as fast as consumer price index

between 1974 and 1980 and land price is pointed as the main cause of it. The share of land for residential

use fell from 11.5% to 8.9% and residential land per resident decreased 20% between 1973 and 1988 due

to the under-allocation of residential land. Land with infrastructure for residential use was 1.7 to 6.5 times

more expensive than raw rural land and the authors interpreted this large difference as a disequilibrium

effect due to government undersupply of conversion permission.

Bertaud (1996) also use a descriptive method to analyze the effect of FAR on land prices in

Ahmedabad, India. It compares the density and land price profile in Ahmedabad and in cities where land

use density is less restrained. The author concludes that FAR restriction distorts land prices, and shows

that in most places where land markets operate well, there is a correlation between density and price

gradients, while in Ahmedabad there is a discrepancy.

Somekh (1999) lists the price that is being paid for parcels in areas zoned as AEIS in the

municipality of Diadema, Brazil, where more than 50% of vacant land was designated AEIS in 1993, when

a new master plan and zoning was approved. She concludes that AEIS designation led to a decrease in the

price of land.

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Green, Malpezzi and Vandell (1994) use a literature review to understand the effect of urban

regulation on the price of land and housing in Korea. On the demand side, the review shows that Korea has

rising aggregate demand for housing due to growth in urban population, household and income. On the

supply side, studies found housing to be price inelastic, explaining price rise with rise in demand. In

addition to geographical barriers, a main factor explaining inelastic supply of housing is urban regulation

and policies affecting land development (limitation on land conversion, green belts, tax on intensive land

use) and housing finance (credit constraints to housing finance).

Bertaud and Brueckner (2004) and Bertaud, Buckley and Owens (2003) use a theoretical approach

using the standard monocentric-city model to show how FAR restrictions affect land use; and a simulation

analysis to predict changes if FAR restriction was removed in Bangalore and Mumbai, India, respectively.

The simulation analysis shows that removing FAR restriction would increase population density near the

center of the city, reduce the edge of the city by 2 km in the case of Bangalore and 4 km in the case of

Mumbai, and, as a result, reduce commuting cost for residents living at the edge. The commuting-cost

saving is estimated to range from 3.3 to 5.0% of per capita income in the case of Bangalore, and 14% in

the case of Mumbai. The second study also shows that the cost imposed by FAR requirements is

regressive, imposing larger costs on low-income households.

Brueckner (1996) uses a theoretical model based on the urban model developed by Alonso (1964)

to assess the welfare net effect from removing apartheid land use restrictions in South Africa. Results

show that with the removal of apartheid land use restrictions, blacks compete for land close to the

employment center, displacing whites. Blacks have welfare gain because of decrease in commuting cost,

while whites suffer welfare loss because of longer commutes. Landowners benefit because of an increase

in total land rent due to greater competition. Because this gain is greater than whites’ losses, there is an

aggregate welfare gain from removing apartheid land use restrictions.

Despite the differences in regulatory measures and methods of analysis employed, the studies

reviewed have common results. They all conclude that regulations that restrain urban development

increase land and housing price and impose costs that exceed their benefits. On the other hand,

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regulations that permit greater degrees of density for urban development, such as AEIS, reduce the price of

land.

One of the limitations of these studies is that they don’t provide a statistical estimation of the effect

of urban regulation on the price of housing or land. The theoretical and simulation analysis remain

hypothetical. That is not to say that their results are not robust. Brueckner (1996) introduces various

modifications to the basic model and still gets the same results, while Bertaud and Brueckner (2004) and

Bertaud, Buckley and Owens (2003) use conservative assumptions when applying the theoretical model to

the simulation analysis of Bangalore and Mumbai. But these analyses are limited by the fact that they have

not been empirically tested.

The limitation of cost-benefit analysis is that it takes price and costs as given and ignores general

equilibrium effects. As argued by the authors, this analysis is useful to illustrate how actual and very

specific regulations affect costs (and hence land and housing price) on specific development projects

(Bertaud and Malpezzi 2001: 395). However, to understand how land use regulation affects housing price,

it is necessary to control for several demand and supply variables that are ignored in this type of analysis.

Studies that use a description of observed housing and/or land prices and land use patterns are

even less specific about the effect of urban regulation on housing price and their results are subject to

interpretation. Hannah, Kim and Mills (1993) present data that “suggest” that a substantial part of the

increase in housing price is due to the government’s tendency to underallocate land to urban residential

use and the authors acknowledge that the conclusion that large differences between rural and urban land

prices are a result of disequilibrium is a matter of judgment. The same can be said about Bertaud’s (1993)

conclusion that FAR restriction distorts land prices based on the observed discrepancy between density

and price gradients in Ahmedabad. And in the case of Somekh (1999), there is very little evidence in her

study that proves that land prices have been declining and even less evidence that this could be a result of

AEIS zoning.

1.2. Comparative studies

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The literature on the effect of land use regulation on housing price in developing countries that

focus on a comparison of countries tries to understand how the stringency of the regulatory environment of

different countries affect their housing markets. In contrast with city or country specific studies, comparative

studies use statistical analysis to estimate the effect of different regulatory environments on the housing

market. Also, the comparative studies focus less on a particular regulation or set of regulations. Instead,

they assess the regulatory environment as a whole to draw conclusions about its effect on the housing

market.

Mayo and Sheppard (1996) use ordinary least squares and autoregressive least squares to find

price elasticity of supply for housing in Korea, Malaysia, and Thailand, to test a previously developed

theoretical model (Mayo and Sheppard 1992) which predicts that regulatory constraint will reduce price

elasticity of housing supply, which in turn may contribute to excessively wide swings in housing prices. The

authors also use a recursive model to estimate the change in price over time. Based on a literature review,

they qualify South Korea as the most restrictive regulatory environment; Thailand, as the least; and

Malaysia as intermediate. Their results show that Malaysia and Korea had low elasticities of supply, while

Thailand had high elasticity. The recursive model showed that although Korea and Thailand were relatively

stable over time, Malaysia had high elasticity in the years immediately after the adoption of more restrictive

planning system, but over time supply became less elastic.

Malpezzi and Mayo (1997) use a cost-benefit model using present value analysis as did Bertaud

and Malpezzi (2001), to asses the effect of regulation in Malaysia, and a model similar to Mayo and

Sheppard (1996) to compare the elasticity of supply of Malaysia, Korea, Thailand and the United States.

The first model estimates the cost of percentage of saleable area, approval time, building code

requirements, and regulation encouraging sales to special ethnic groups. The cost-benefit analysis

indicates that regulations add about $4,000 (Malaysian) to the developer’s cost. The cross-country

comparison indicates that Malaysia and Korea have inelastic housing supply curves and Thailand has an

elastic curve, similar to the United States.

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Angel’s (2000) book tests whether an enabling housing policy environment1 has a positive effect on

the performance of the housing sector, using qualitative and quantitative analyses of housing markets and

policies around the globe. To measure the regulatory environment for urban development, the author

develops a Regulatory Regime Index, a composite measure of three variables: permits delay, minimum lot

size, and minimum floor area per dwelling. The regulatory index is integrated with indexes that measure

other elements of the housing policy environment (property rights regime index, housing finance regime

index, housing subsidy index, and residential infrastructure index) and an ordinary least squares regression

analysis estimates the effect of this broad index (the enabling index) on a housing price index, a rent price

index, and a weighted housing price index. His results show that a more enabling housing policy

environment significantly lowers the housing price index in the 45 countries studied. The same applies to

the housing rent index and the weighted housing price index in the 38 countries that have little or no rent

control.

The comparative studies arrive at similar results observed in the city and country specific studies: a

more restrictive regulatory environment reduces price elasticity of housing supply (which may contribute to

excessively wide swings in housing prices) and increases housing costs. On the other hand, a more

“enabling” regulatory environment, in conjunction with other “enabling” housing policies, lowers housing

price and rent.

One of the main shortcomings of these studies is the level of aggregation of their data and of their

measures of urban regulation. In Mayo and Sheppard (1996) the measure of regulatory restrictiveness

compares a variety of policies such as Korea’s centralized planning system of limiting land conversion from

rural to urban and limiting growth through greenbelts; Thailand’s centralized planning system of guiding

rather than controlling development; and Malaysia’s newly decentralized planning system. In addition, their

model is subject to the same criticism that was addressed to similar models developed earlier, such as the

possibility of aggregation bias because of the use of national data; the small size of the sample, which

1 Angel defines an enabling housing policy environment as one that meets each of five criteria: adjudication of property rights in land and housing, development and regulation of housing finance institutions, administration of housing subsidies, provision and maintenance of residential infrastructure, and regulation of land and housing development. The author quantifies any given housing policy regime along one of its five components, and arrives at a composite measure of the degree of enabling of the housing policy regime as a whole - the Enabling Index.

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varies between 14 and 16 annual observations; and the model specification. With respect to the last point,

Olsen (1987) criticized reduced-form equations where the long-run supply price is the dependent variable

and output quantity and input prices are included on the right hand side. He argued that the function should

have input prices and the parameters of the production function or output quantity, but not both.

Malpezzi and Mayo’s (1997) cost-benefit model based on Bertaud and Malpezzi (2001) is subject

to the shortcoming of ignoring general equilibrium effects while the comparison of supply elasticity based

on Mayo and Sheppard (1996) has the shortcomings discussed in the previous paragraph.

Angel’s (2000) book is such an ambitious study that it is not surprising that it has shortcomings,

especially with respect to the limitation on data, which in many cases limits the accuracy of the indices.

One limitation is that each country is assessed based on the information of a single metropolitan area.

Another problem is the lack of data even on these single metropolitan areas. For the construction of the

regulatory regime index, there were several variables for which data was not available, such as the actual

percentage of land unavailable to growth. As a result, the index has a very limited set of indicators. For the

construction of housing price and rent indexes, the author had to apply a crude method for controlling for

housing quality. Angel normalized housing and rent price using a construction quality index for each

metropolitan area, which is defined as an equally weighted sum of three indicators: permanent structures,

quality attributes (the presence of seven attributes such as piped water and electricity in the median-priced

house) and annual median household income. Finally, the regression analysis aggregates not only an

already aggregated measure of the regulatory environment for urban development, but all other aspects

that affect the production and consumption of housing, making it very hard to assess the extent to which

regulation itself is affecting the price of housing in each of these metropolitan areas.

As the review of this body of literature has shown, both city and country specific studies, and

comparative studies, have results pointing in the same general direction: restrictive land use regulation

increases housing price and imposes net costs. However, this literature is subject to major shortcomings

that could affect the results obtained by these studies. First, several studies suffer from the possibility of

aggregation bias for housing price and urban regulation measures. Second, research tends to use

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methodologies that fail to control for demand and supply factors that affect the price of housing. Finally,

most studies don’t provide a statistical estimation of the effect of urban regulation on the price of housing or

land.

2. The Informal Housing Market

In the literature reviewed, there are three major approaches to the study of informality:

internal/theoretical, structural and legalistic. This section presents a summary of these approaches and the

ways in which they have explained the causes of informality and its relationship with urban regulation and

housing prices.

2.1. The Internal/Theoretical Approach

The internal/theoretical approach refers to the literature that either focuses on the internal structure

and operation of the informal sector, or focuses on a theoretical approach to urban informality. For

example, De Souza (2002) uses five case studies of squatter settlements in Recife, Brazil to understand

the relationship between perception of security and housing consolidation. Pamuk (2000) interviews an

informal credit institution that provides credit for land and infrastructure in Trinidad and Tobago to

understand how informal institutional arrangements are utilized by squatting communities to solve their land

problems. Roy (2005) focuses on defining a new conceptual framework for informality that challenges the

traditional way of portraying it as a sector in contrast with the formal sector. This approach to the study of

informal housing markets is important for improving the conceptualization of informality and understanding

the way in which it operates. However, because the causes of informality are a secondary concern of this

literature, it provides limited insight in responding to the question of how informality relates to land use

regulation and housing price.

2.2. The Structural Approach

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The structural approach refers to authors such as Gilbert 1990, Maricato 1996 and Smolka 2003,

who focus on the relationship between informal and formal housing markets and on its structural causes.

Gilbert 1990 focuses on the costs and benefits of informal self-help housing in Latin America.

Building on previous case studies and complementing it with secondary data analysis, the author argues

that self-help has been the traditional housing delivery system in Latin America and planning standards that

were imported from Europe since the 1930s made it illegal or irregular. The author identifies a tendency for

the regularization of these settlements in Latin America and compares the cost and benefits of complete

formalization from a welfare standpoint. Gilbert challenges the view that informality can be explained by

capitalist international division of labor. Instead, the author argues that local factors such as the dominant

economic, political and social forces in a particular society will determine the extent and pattern of irregular

land supply.

Maricato 1996 focuses on the relationship between informality, inequality and violence in Brazilian

cities. Using a Marxist theoretical framework and based on secondary data analysis, the author reviews the

historical conditions of industrialization and wealth concentration and the role of the Estate in supporting

capitalist accumulation and exclusion. In this context, informality is viewed as a result of the process of

“exclusionary modernization” that has characterized capitalism in Brazil and the main factors contributing to

the informal production of housing are: (i) low wages, (ii) the concentration of land rent by the private

property rights regime, (iii) the cost of the urban regulatory regime, and (iv) the use of public investment

favoring industrial accumulation and infrastructure.

Smolka 2003 focuses on the interdependence of formal and informal urban land markets in Latin

America. Using secondary data analysis, the author argues that the high price for serviced land in formal

markets is pushing prices up in unserviced informal land markets. Smolka argues that in addition to the

high price of serviced land, other factors that explain the extent and persistence of informal land markets on

the supply side are: the lack of sufficient social housing programs, inadequate public investment in

infrastructure, high profitability of informal developers, and “elitist” urban regulations. On the demand side,

Smolka identifies as factors that lead to housing informality: low income, lack of finance, and a strategy of

capital accumulation developed by the poor to protect themselves against high inflation.

The interpretation of urban regulation varies across this literature: at times it is seen as an imported

or elitist concept that clashes with local social practices (Gilbert 1990 and Smolka 2003), while in other

instances it is seen as an instrument of class domination (Maricato 1996). The emphasis on the causes of

informality also varies between Gilbert and Maricato: the former emphasizes local conditions while the latter

emphasizes the contradictions of capital accumulation. However, all authors seem to support a conceptual

framework in which informality is an outcome of the adoption of urban regulations that leads, in turn, to the

increase in housing prices.

2.3. The Legalistic Approach

The third category of literature on informal housing markets focuses on the role of the legal and

planning apparatus in the development of informal housing markets and is represented by De Soto 2000.

De Soto’s book attempts to explain why capitalism has worked in the West and has failed in all

developing and former communist countries. Using secondary data analysis, the author argues that most of

the poor in developing countries already possess the assets they need to make a success of capitalism.

The problem, he continues, is that these assets don’t have title and therefore cannot be readily turned into

capital, be traded outside localities, be used as collateral for a loan, and be used as a share against

investment. The solution, according to De Soto, is to change the official legal system in order to

accommodate the requirements of the extralegal sector. It is important to note that this magic solution to

developing country problems in general, and to informality in particular, has been severely criticized in the

academic world, and many studies of illegal settlements that have been regularized show that a property

title doesn’t necessarily improve poor people’s access to credit and the formation of a secondary mortgage

market.

For the legalistic approach, planning, and therefore urban regulation, is at the center of the problem

of informality. According to this view, informality is solely the result of a property legal system that was

imported from the West without considering the local needs of developing countries. Contrary to the view

that informality is structural in peripheral capitalist societies and can’t be completely removed, the legalistic

approach believes that informality can disappear when the legal apparatus is reformed.

Although in most of the studies reviewed here, land use regulation is found to be one of the factors

that could explain the presence of an informal housing market, there are very few studies that have

attempted to identify and quantify more specifically how land use regulations are contributing to raise

housing price and push low income families to the informal housing market. Most studies in this area have

been broad in scope and have not attempted to understand how different regulatory environments in

different municipalities will affect the price of housing and the size of the informal housing market. The

answer to this question will provide important information for policy decisions and the revision of land use

regulation at the municipal level to prevent the growth of informal housing.

METHODOLOGY

1. Empirical Model

The study assumes that there are two broad and distinct housing submarkets: (i) a formal one,

comprised of housing units in compliance with property rights regimes and urban regulations (such as land

use planning and subdivision standards); and (ii) an informal one, comprised of housing originated as a

result of land squatting (favelas) or irregular land subdivisions developed and commercialized without

complying with one or more urban regulation requirements (loteamentos clandestinos). It also assumes that

these two types of housing are imperfect substitutes and low income households move towards the

informal housing market in response to changes in housing price in the formal market.

These assumptions are not unique and have been widely used in other housing studies. Struck

and Turner (1986) proposed a simultaneous equation model to study the quantity of formal and informal

financing for housing in the Philippines and Korea. There is a large body of literature that has proposed a

filtering model of the housing market, where it is assumed that there are two housing submarkets, one for

19

medium-quality housing and one for low-quality housing, and consumers move between markets in

response to changes in price.

These assumptions lead to the construction of two equations. For the first, the study follows Green

(1999) specifying a reduced form equation, where formal housing price ( ) is a function of demand and

supply factors such as population pressure, household income, and demographic characteristics, on the

demand side; and land, labor, materials, and formal subdivision/building taxes on the supply side2. As

justified by Green (1999), in a localized area such as the metropolitan area of Curitiba, it is fair to assert

that it has something like a single labor and materials market, so we don’t actually need to control for these

variables.

Each variable of urban regulation (such as minimum plot size, percentage of saleable area, and

minimum street width) is incorporated in the equation with a spatial lag variable ( ) that

will measure the weighted sum of the effect of the regulatory variable on the “j” geographic neighbor unit on

the price of the “i" geographic neighbor unit. This specification permits testing how the regulation of

municipality “i" affects the price of housing in municipality “i" and also how the weighted sum of neighboring

municipality “j” affects the price of housing in municipality “i".

For the second equation, a similar equation is specified except that in this case quantity of informal

housing ( ) is used as the dependent variable, instead of the price. Taxes and regulatory variables are

not incorporated in this equation because the informal housing market doesn’t comply with urban regulation

and developers don’t pay formal taxes. Since formal and informal housing are treated as imperfect

substitutes, an instrumental variable for the price of formal housing obtained from the first equation and its

correspondent spatial lag variable ( ) is added to the equation. This variable will measure

the weighted sum of the effect of the formal price variable on the “j” geographic neighbor unit on the

2 The reduced form equation is obtained from two structural equations for the supply and demand for formal housing. Because in equilibrium the quantity demanded and supplied must equal each other, Green (1999) equals both equations to obtain the reduced form.

20

quantity of informal housing of the “i" geographic neighbor unit. This specification permits testing how the

formal housing price of municipality “i" affects the quantity of informal housing in municipality “i" and also

how the weighted sum of neighboring municipality “j” affects the quantity of informal housing in municipality

“i".

2. Definition and Measures: Housing Price, Urban Regulation and Informality

2.1. Housing Price

Most quantitative studies on the impact of urban regulation on housing price in developed countries

in general, and in the United States in particular, use regression analysis of hedonic price models to control

for the several attributes of a house. Usually the dependent variable is the sale price of an individual house

or the average value of units in a census tract or municipality. The independent variables are the attributes

of the housing unit such as square feet of interior space, number of bathroom, and square feet of lot.

Fischel (1990) noted that although the most important and significant coefficient across all housing

regressions is that for the measure of floor space of the house, there is an ongoing and seemingly

inconclusive debate about the proper specification of hedonic price models. Fischel (1990) also suggests

that too many independent variables don’t yield a better model. He cites Richard Buttler (1982) who reports

that a house value estimates using four independent variables (number of rooms, age of house, and two

variables that measure quality of housing) are statistically indistinguishable from estimates of the same

sample using eleven independent variables.

In developing countries, the lack of information has led many authors to use alternative measures

of housing price or some crude adaptations of hedonic price models. For example, Hannah, Kim, and Mills

(1993) used national housing price index for Korea and Mayo and Sheppard (1996) used the shelter price

component of the consumer price index published by Thailand’s Ministry of Commerce. Angel (1990) used

what he calls a primitive Construction Quality Index to obtain a number of price indices for several countries

21

around the globe that are normalized by quality. The Construction Quality Index is an equally weighted sum

of three indicators: permanent structures, quality attributes, and the annual median household income3. The

estimated price for each country in the sample was obtained by fitting a regression line to the quadratic

equation , where was the natural logarithm of the reported price for

country i, the value of the Construction Quality Index for country i, and the square of this value.

For this dissertation, I will attempt to control for quality with a hedonic price model. The exact way

in which this will be done will depend heavily on the data I am able to gather on the ground. Based on

preliminary phone interviews with public officials of municipalities included in my study, I expect to be able

to get the average price of housing for each of the 135 geographical zones using the housing values

assessed for the property taxes purposes. I will regress this value on available data on housing attributes.

From my preliminary interviews, I expect to get at least data on the floor space of the house. If there aren’t

any other attributes, such as age of house and quality attributes, I will complement this information with

household survey information about the quality of housing structures, the presence of water, sewage,

paved roads and electricity in each geographical zone.

2.2. Urban Regulation

The literature on the subject has used different measures to capture the stringency of the

regulatory environment for urban development, as was discussed before. Urban regulation measures

include government restrictions on land conversion from rural to urban (Hannah, Kim, and Mills 1993); a

combination of governmental regulatory policies on financing, tax and land use (Green, Malpezzi and

Vandell 1994); floor area ratio (Bertaud 1996, Bertaud and Malpezzi 2001, Bertaud, Buckley, and Owens

2003, Bertaud and Brueckner 2004); percentage of saleable area (Malpezzi and Mayo 1997, Bertaud and

Malpezzi 2001); apartheid land use restriction (Brueckner 1996); level of regulatory restrictiveness (Mayo 3 This and other indicators were part of the Global Survey of Housing Indicators conducted in 1990 as a joint venture of the World Bank and the United Nations. On this survey, the permanent structures indicator was defined as the percentage of housing units that were likely to last 20 years or more given normal maintenance and repair, and taking into account environmental hazards. The quality attributes indicator was a discrete measure of the number of positive answers to questions about the presence of seven basic amenities in the median-priced house (piped water to unit, water-borne sewage, electricity, central heating, air conditioning, elevators, and paved roads adjacent to the house. As these two indicators were deemed to be insufficient to distinguish the quality of construction associated with higher levels of income, annual median household income was added as a third factor to the index, assuming that construction quality varied in direct proportion to income.

22

and Sheppard 1996); length for obtaining a permit for a land subdivision (Malpezzi and Mayo 1997, Angel

2000); regulatory requirements in Special Area of Social Interest (Somekh 1999); regulatory regime index,

a composite measure of three indicators: permit delays, minimum lot size, and minimum floor area (Angel

2000).

Urban regulation measures can be categorical variables, such as the number of months for

subdivision approval, or dummy ones, such as whether development fees are imposed in a jurisdiction

(Mayer and Sommerville 2000). Urban regulation can be assessed separately by adding several individual

variables to the equation, for example using one variable for minimum required lot width, one variable for

minimum required front setback, one variable for minimum required street width, and so on, as was done

by Green (1999). Alternatively, urban regulation can be assessed by an index that combines several

aspects of urban regulation in a single variable such as the City Regulatory Index, which is an unweighted

sum of values of seven variables collected by a Wharton survey on urban regulation4, or the State

Regulatory Index, which is an unweighted sum of eight dummy variables on the presence or absence of

several planning instruments (Malpezzi 1996)5. Finally, urban regulation can take the form of a very

objective and specific norm or standard, such as the measure of floor to area ratio (FAR) required by local

ordinances, or it can take the form of a more subjective and broad assessment of the regulatory

environment, such as the restrictiveness level of districts rated either as flexible or restrictive according to

local planners and real estate agents in a study carried out by Monk and Whitehead (1999).

The measures of urban regulation can focus on different aspects of the regulatory environment for

urban development. They can specifically assess the impact on price of building codes, environmental

4 The City Regulatory Index is the unweighted sum of values of seven variables collected by a 1990 Wharton survey: APPTIME: change in approval time (zoning and subdivision) for single family projects between 1983 and 1988 (1 – shortened considerably, 2 – shortened somewhat, 3 – no change, 4 – increased somewhat, 5 – increased considerably); PERMLT50: estimated time between application for rezoning and issuance of permit for a residential subdivision less than 50 units (1 – less than 3 months, 2 – between 3 and 6 months, 3 – 7 to 12 months, 4 – 13 to 24 months, 5 – more than 24 months); PERMGT50: same as PERMLT50 but for single-family subdivision greater than 50 units; DLANDUS1: acreage of land zoned for single family housing as compared to demand (1 – far more than demanded, 2 – more than demanded, 3 – about right, 4 – less than demanded, 5 – far less than demanded; DLANDUS2: similar to DLANDUS1 but for multifamily housing; ADQINFRA: Wharton scale for adequate infrastructure – roads and sewers (1 – much more than needed, 2 – slightly more than needed, 3 – about right, 4 – slightly less than needed, 5 – much less than needed). The index ranges from 7 to 35.5 The State Regulatory Index is the unweighted sum of eight dummy variables on the presence or absence of: state comprehensive land use planning; state coastal zone management plans; state wetlands management regulations; state floodplain management; state designation of some locations as “critical” for land use regulation; state enabling regulation for “new towns”; state requirement for environmental impact assessments; and state regulations preempting local regulations for “developments of greater than local impact”. The index ranges from zero to eight.

23

regulations, zoning and land use, impact fees (something that is particularly relevant to the United States

market), and administrative processes. Alternatively, they can assess the overall or the cumulative impact

of urban regulation on price. Most studies focus either on land use and impact fees or on the overall impact

of urban regulation (Schill 2005).

The measures of urban regulation used in this dissertation try to capture two classes of regulations:

land use regulations that impose costs on the final housing solution by limiting the density of occupation of

land and administrative processes that impose cost by delaying land subdivision projects. Some of the

regulatory measures that will be analyzed include: minimum plot size, percentage of saleable area (the

area that can be sold after taking away areas required for public use such as streets, public schools and

parks), minimum street width, and the number of months taken to get a permit for a new land subdivision.

Instead of using an index, I will add these variables separately in the equation to try to understand whether

individual regulations or classes of regulation have effects that are economically more significant and can

be targeted in the revision of urban policies.

2.3. Informality

Most of the studies on informal housing acknowledge the difficulty in defining and measuring this

market. In general terms, informal housing can be defined as a housing solution that doesn’t follow the

norms and laws of a particular place. But since laws and norms vary, what might be legal or regular in a

locality, might be illegal or irregular in another. Therefore, informality doesn’t have specific characteristics

that can define it and help compare it across cities or countries.

Some of the literature establishes a very broad sub-classification of informality, distinguishing

between illegality, a property that lacks legal title of the land and has generally been originated by land

squatting; and irregularity, a property that might have a legal title but doesn’t conform to urban regulation

and has generally been originated by a commercial land developer that sells the property without complying

with all subdivision regulations (De Souza 2002, Gilbert 1990). In several instances, though, informality,

illegality and irregularity are used interchangeably (Maricato 1996, Pamuk 2000, Smolka 2003).

24

Measuring housing informality is also a challenge for two main reasons. First, the measure

depends on the adopted definition of informality. For instance, some studies focus only on squatters (De

Souza 2002, Gilbert 1990, Pamuk 2000), while others include all forms of housing informality, including

housing without legal title and housing that doesn’t comply with land use, subdivision and building norms

and codes (Maricato 1996).

The second challenge for measuring housing informality is the difficulty of capturing information on

the informal sector by formal sources (census, household surveys, property registries, property tax records,

subdivision and building permit records, real estate transactions, etc.). Houses without proper title are not

included in real estate property registries and property tax records. Houses in subdivisions that have been

sold without a subdivision permit might be counted in the census and household surveys as “regular

houses”, but they are not included in official local government maps and records. Untangling all possible

ways in which a property might be informal is a very difficult task.

In the literature, there are two main sources for operationalizing informality: (i) the census and

household surveys, and (i) local government cadasters of squatter settlements, irregular land

developments, irregular buildings and irregular renting units. In Brazil, the census and household surveys

measure of informality are residences built with temporary materials (shacks), located in a settlement that

presents a disorganized pattern of occupation and lacks essential public services. Squatter settlement will

most likely be captured by these measures, but irregular land developments and irregular buildings will

most likely be left out. Because of this limitation, several authors complement this information with local

governments’ cadasters of illegal and irregular housing (Gilbert 1990, Maricato 1996).

The difference between the census/household surveys and local governments’ measure of

informality can be considerably large. For instance, Maricato 1996 calculated that 70% of the residences of

the city of Sao Paulo have some degree of informality according to local cadasters, while the census

estimates that the city of Sao Paulo had 7.61% of its population living in substandard settlements6 in 1996.

6 The Brazilian Institute of Geography and Statistics defines “subnormal settlement” as a group of 50 residences or more (shacks, houses, etc.) that occupy a land that is or was owned by third parties, generally presenting a disorganized pattern of occupation, densely occupied and lacking essential public services.

25

For this dissertation, I will use census and household surveys complemented by local cadasters of

informal housing. That is the only way in which I will be able to capture not only areas that were subjected

to squatting (such as favelas), but also areas that were commercialized without getting land subdivision

permits and/or without following all regulatory requirements for residential development (such as irregular

land subdivisions). It is important to note that this measure will leave out properties that are located in a

formal land subdivision, but have building structures that have some form of irregularity, such as an

addition or a major renovation done without a building permit that infringes one or several requirements of

the building code.

3. Data set

The data set for this study will come from four sources: (i) the 2000 census and household survey

data collected by the Brazilian Institute of Geography and Statistics (IBGE, for its Brazilian acronym); (ii) the

data from the World Bank’s assessment of the urban land market of Curitiba, which was collected in 2002

and has estimations for 2000 (World Bank/Cities Alliance 2005b); (iii) the data from municipal cadasters

and land use ordinances for the year of 2000; and (iv) data from the survey on urban regulation and

informality (see Annex II).

From the census and household survey, my study will obtain data on income, changes in

population size, number of houses, number of houses in subnormal settlement, housing attributes (housing

structures, number of rooms, presence of water, sewage, paved roads and electricity), and the

demographic variables. From the World Bank’s land assessment, the study will obtain data on land price for

2000. From the municipal cadasters, the study will obtain data on formal housing assessed value, its main

attributes, taxes and the quantity of houses in land squatting (favelas) and irregular land subdivisions

(loteamentos clandestinos); from land use ordinances, the study will obtain data on minimum plot size,

percentage of saleable area, and minimum street width. From the survey on urban regulation and

informality, the study will obtain data on minimum plot size, percentage of saleable area, minimum street

width, and the number of months required for a land subdivision permit. As justified by Green (1999), data

26

on wages and materials is unnecessary because these costs are likely to be very similar across the

metropolitan area of Curitiba.

The World Bank data is available for 135 geographic zones with similar land use patterns, housing

typology, socioeconomic conditions, and population density and were defined to be compatible with the

limits of the census tract. The census data is available at census tract level, the municipal data on formal

housing assessed price, its main attributes, taxes and the quantity of houses in land squatting and irregular

land subdivisions is available at census tract level and the data on land use regulation is available at the

zoning area and at the municipal level. The appropriate census, zone and municipal information will be

applied to each of the geographic zones, to obtain a total of 135 observations.

Table 2. List of Variables and DataVariable Definition Year SourceFormal Housing Price (PF) Estimated value of Median Assessed Price of Registered Single Family

Houses in Reais (R$) regressed on several housing attributes.2000 Municipal

cadastersPopulation Changes in population size 1991-2000 IBGEIncome Median household income in Reais (R$) 2000 IBGEAge Percentage of population <25, >25<35, >35<45, >45<55 years old 2000 IBGEHousehold Type Percentage of married couples 2000 IBGERace Percentage of households headed by blacks, pardos 2000 IBGETaxes Land subdivision and building taxes in Reais (R$) 2000 Municipal

cadastersLand Median price of land in Reais per square meter (R$/m2) Estimated

2000World Bank 2005b

Regulation Minimum plot size, percentage of saleable area (the area that can be sold after taking away areas required for public use such as streets, public schools and parks), minimum street width, number of months taken to obtain a land subdivision permit.

2000 Municipal land use ordinances and Urban Regulation Survey

Quantity Informal Housing (QI)

Number of subnormal houses as defined by IBGE and number of plots in irregular land subdivisions according to municipal cadasters

2000 IBGE, Municipal cadasters

4. Analysis

Using the simultaneous equation model described previously, the study will conduct a spatial

regression analysis to understand the magnitude of the effect of urban regulation on formal housing price

and the effect of rising formal housing price on the quantity of informal housing. The spillover effect of land

use regulations adopted in one municipality on other municipalities will be assessed by incorporating

spatially lagged variables of urban regulation and housing price to the regression model.

TIMELINE

27

I expect to be able to finalize and defend the dissertation proposal in August 2007. The months of

September, October and November of 2007 will be dedicated to data collection in Curitiba, Brazil. Running

the model and analysis is expected to be finalized by the end of the Spring semester of 2008 and I

anticipate defending the dissertation in the 2008 Summer semester.

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Annex I - Summary of the Literature on Land Use Regulation and Housing Price in Developing Countries

Author(s) Year Geography Covered

Regulatory Measure Housing Price Measure

Other Variables

Method Results

Hannah, Kim and Mills

1993 South Korea (country, city and project level)

Government permission to land conversion from rural to urban for residential use.

National housing price index, national land price index and construction material price index

Analysis of housing price and related series; analysis of time series of land use pattern; and case studies of five Seoul development projects

National housing price rose more than twice as fast as consumer price index between 1974 and 1980 and land price is pointed as the main cause of it. The share of land for residential use fell from 11.5% to 8.9% and residential land per resident decrease 20% between 1973 and 1988 due to under- allocation residential land, according to authors. Land with infrastructure for residential use was 1.7 to 6.5 times more expensive than raw rural land and large difference is result of disequilibrium due to government undersupply of conversion permission, according to authors.

Green, Malpezzi and Vandell

1994 South Korea A combination of governmental regulatory policies on financing, tax and land use.

Several price measures used in the literature reviewed

Literature review On the demand side, Korea shows rising aggregate demand for housing due to growth in urban population, household and income. Similarly to US, demand for housing seems to be responsive to income and price, with elasticities below 1. On the supply side, studies found housing to be price inelastic, explaining price rise with rise in demand. In addition to geographical barriers, a main factor explaining inelastic supply of housing is urban regulation and policies affecting land development (limitation on land conversion, green belts, tax on intensive land use) and housing finance (credit constraints to housing finance)

Bertaud 1996 Ahmedabad, India

Floor Area Ratio (FAR) Land price gradients

Density gradients

Comparison of density and land price profile in Ahmedabad and in cities where land use density is less restraint.

Author concludes that FAR restriction distorts land prices, showing that in most markets where land market operates well, there is correlation between density and price gradients while in Ahmedabad there is discrepancy.

Brueckner 1996 South African cities

Apartheid Land Use Restriction Theoretical model based on urban model developed by Alonso (1964)

Removal of apartheid land use restriction, blacks compete for land close to employment center, displacing whites. Blacks have welfare gain because of decrease in commuting cost, while whites suffer welfare loss because of longer commutes. Landowners benefit because of increase in total land rent due to greater competition, this gain is greater than whites’ losses, so there is aggregate welfare gain from removing apartheid land use restriction.

Mayo and Sheppard

1996 Malaysia, Thailand and South Korea (country-level)

Level of regulatory restrictiveness, comparing three countries: South Korea being the most restrictive; Thailand, the least; and Malaysia the intermediate.

National housing price indexes for each country

Income, factor prices for housing production, and the price of other goods

OLS and autoregressive least squares to find price elasticity of supply for housing. Also uses a recursive model to estimate the change in price over time.

Results show that Malaysia and Korea had low elasticities of supply, while Thailand had high elasticity. The recursive model showed that although Korea and Thailand were relatively stable over time, Malaysia had high elasticity in the years immediately after the adoption of more restrictive planning system, but over time supply became less elastic.

Malpezzi and Mayo

1997 Malaysia, Thailand, and South Korea (country-level)

Estimates cost of: (i) percentage of saleable area (area that is sold after subtracting requirements for road size, setback, and community facilities); (ii) approval time; (iii) building code requirements and regulation encouraging sales to special ethnic groups. Malaysia is compared with Thailand (less restrictive) and South Korea (similar restrictiveness)

National housing price indexes

Similar to Mayo and Sheppard (1996)

Cost-benefit model using present value analysis as in Bertaud and Malpezzi (2001) and model similar to Mayo and Sheppard (1996) for the three country comparison

The cost-benefit analysis indicates that interventions add about $4,000 (Malaysian) to developer’s cost. The cross-country comparison indicates that Malaysia and Korea have inelastic housing supply curves and Thailand has an elastic curve, similar to the United States.

Somekh 1999 Diadema, SP, Brazil

Special Area of Social Interest (AEIS) – zoning areas designated to the construction of social housing with less restraining regulatory requirements.

Not shown Not clear Author concludes that AEIS led to a decrease in the price of land located in areas designated as such, because of increase supply of land allocated to this use, but author doesn’t provide any evidence of it.

Angel 2000 Global cross-country analysis of 38 or 45 countries depending on the model used

Regulatory Regime Index, a composite measure of three indicators: permit delays, minimum lot size, and minimum floor area. The last two indicators were normalized by dividing them by the median house size.The regulatory index and four other indexes (property rights regime index, housing finance regime index, housing subsidy index, and residential infrastructure index) were equally weighted into a composite index, the Enabling Index.

Housing price index, housing rent index (both normalized by a construction quality index), and weighted housing price index (a combination of housing price and rent indexes)

Log of household income, city population growth rate, housing credit portfolio, construction cost-to-income ratio, land cost-to-income ratio

OLS A more enabling housing policy environment significantly lowers housing price index, housing rent index in countries with little or no rent control and weighted housing price index in countries with little or no rent control.

33

Bertaud and Malpezzi

2001 Malaysia (project-level)

Authors identify what they believe are the two most important indicators of urban regulation: floor area ratio (FAR – building’s total floor area divided by area of land plot) and percentage of saleable area (the area that can be sold after taking away areas required for public use such as streets, public schools and parks).

Cost-benefit model of land use and related regulations, where benefits are roughly estimated comparing current regulation and practice to a baseline based on market comparison and “international practice”.

Under current regulation for low income developments, only 44% of the land is saleable and FAR is only 0.23. As a consequence, developers have a profitability that is 15% below the baseline of a middle income development.With suggested reduction in road width, elimination of black alleys and reduction of corner setback requirements saleable area rises to 55% and FAR rises to 0.41. These changes make profitability rise 17% in comparison to the baseline of a middle income development.

Bertaud, Buckley and Owens

2003 Mumbai, India

Floor area ratio (FAR) Use the same theoretical and simulation model used in Bertaud and Brueckner (2004)

Simulation analysis shows that FAR increases the edge of the city by 4 km and, as a result, increase commuting cost by14% of per capita income. This cost is regressive: the cost for the first income decile is 10% in Bangalore and 22% in Mumbai

Bertaud and Brueckner

2004 Bangalore, India

Floor area ratio (FAR) Theoretical approach using standard monocentric-city model to show how FAR restrictions affect land use; and simulation analysis to predict changes if FAR restriction was removed.

Theoretical part shows that FAR limits population density in the center and causes the city to sprawl, resulting in an increase in commuting cost. Simulation analysis shows that removing FAR restriction would increase population density near the center of the city, reduce the edge of the city by 2 km and, as a result, reduce commuting cost for residents living at the edge. The commuting-cost saving is estimated to range from 3.3 to 5.0% of per capita income and between 3.0 and 4.5 % of household consumption.

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Annex II – Urban Regulation and Informality Survey

Dissertação de DoutoradoInformalidade, Regulação de Uso do Solo e Preço de Habitações: um Modelo Aplicado à Curitiba, BrazilDepartamento de Estudos Urbanos e PlanejamentoUniversidade de Maryland em College ParkDoctoral DissertationInformality, Land Use Regulation and Housing Price: a Model Applied to Curitiba, BrazilDepartment of Urban Studies and PlanningUniversity of Maryland at College Park

Maria Teresa SouzaCandidata ao Dutorado (Doctoral Candidate)10560 Assembly Drive Fairfax, VA 22030 USA +1 (703) 273-5702 mariateresa_souza@yahoo.com

Para (To): Chefe do Departamento de Planejamento Urbano (Head of Urban Planning Department)Ref.: Pesquisa sobre Regulação do Uso do Solo e Informalidade Urbana (Survey on Land Use Regulation and

Urban Informality)

Eu estou aplicando esta pequisa sobre regulação do uso do solo e informalidade urbana em treze municípios da região metropolitana de Curtiba7. Os resultados vão proporcionar uma base de dados para minha tese de doutorado, a qual buscará estimar a magnitude do efeito da regulação urbana no preço das habitações formais e o efeito de um aumento no preço das habitações formais na quantidade de habitações informais.I am applying this survey on land use regulation and urban informality on thirteen municipalities in the metropolitan area of Curitiba. The results will provide database for my doctoral dissertation which will estimate the magnitude of the effect of urban regulation on formal housing price and the effect of rising formal housing price on the quantity of informal housing.

Obrigada por sua colaboração. Por favor entre em contato comigo se você estiver interessado nos resultados dessa pesquisa. Eles deverão estar disponíveis em fevereiro de 2008.Thank you for your assistance. Please contact me if you are interested in the results of this survey. They should be available in February, 2008.

INFORMAÇÃO GERAL (GENERAL INFORMATION)

1. Nome do município (Name of municipality):______________________________________________________2. Nome do entrevistado (Name of respondent):____________________________________________________3. Cargo do entrevistado (Title of respondent):_____________________________________________________

CARACTERÍSTICAS DO MUNICÍPIO (CHARACTERISTICS OF THE MUNICIPALITY)

4. Area do município (Size of municipality): ______________km2

5. População do município (Population of Municipality):_______________________________________________6. Qual o número de imóveis que constam no cadastro da prefeitura? (How many real estate properties exist in the

municipality cadastre?)Residenciais (Residential): ________________________Não residenciais (Non-residential): _____________________

7. Houve modificação no perímetro urbano desde a aprovacao da Constituicao Federal de 1988? (Was there a modification in the urban perimeter since the approval of the Federal Constitution of 1988?)

Sim (Yes) Não (No)Em que ano? (In which year?) ____________

LEGISLAÇÃO URBANA (URBAN LEGISLATION)

8. Para cada legislação, assinale somente uma resposta e informe o ano (For each legislation, mark only one answer and provide the year)

7 Os municípios incluídos nesta pesquisa são: (The municipalities that are included in this survey are): Almirante Tamandare, Araucaria, Campina Grande do Sul, Campo Largo, Campo Magro, Colombo, Curitiba, Fazenda Rio Grande, Madirituba, Pinhais, Piraquara, Quatro Barras, e (and) Sao Jose do Pinhais.

Formulado (Written)

Aprovado(Approved)

Regulamentado

(Amended)

Sendo aplicado(Being

applied)

Em revisão(Being

revised)Não tem

(Don’t have)

Ano de aprovação

(Year of approval)

Plano Diretor (Master Plan) ______Lei de uso e ocupação do solo (Zoning)

______

Código de Obras (Building Code) ______Código de Postura (Posture Code) ______Lei de loteamento/parcelamento (Land subdivision law)

______

REQUISITOS URBANÍSTICOS PARA LOTEAMENTO RESIDENCIAL (URBAN REQUIREMENTS FOR RESIDENTIAL LAND SUBDIVISION)

9. Qual é a área mínima de lote residencial permitida no município? (What is the minimum plot size allowed in the municipality?) ___________ m2

Qual é a legislação que se aplica neste caso? (What is the legislation that applies in this case?)Quando foi aprovada? (When was it approved?)

10. Qual é o tamanho mínimo de frente de lote residencial permitido no município? (What is the minimum plot front size allowed in the municipality?) ___________m

Qual é a legislação que se aplica neste caso? (What is the legislation that applies in this case?)Quando foi aprovada? (When was it approved?)

11. Qual é a porcentagem que um loteamento residencial deve reservar para uso público (para circulação, equipamentos comunitários e urbanos, e espaços livres de uso público)? (What is the percentage that a land subdivision has to reserve for public usage (for circulation, urban and community equipment, and open areas for public use?)

Qual é a legislação que se aplica neste caso? (What is the legislation that applies in this case?)Quando foi aprovada? (When was it approved?)

12. Qual é a largura mínima de rua exigida em um loteamento residencial? (what is the minimum street width?)Qual é a legislação que se aplica neste caso? (What is the legislation that applies in this case?)Quando foi aprovada? (When was it approved?)

PROCESSO DE APROVAÇÃO DE LOTEAMENTO/PARCELAMENTO RESIDENCIAL (RESIDENTIAL LAND SUBDIVISION PROCESS OF APPROVAL)

13. Tipicamente, qual o tempo que leva o pedido de aprovação de um loteamento/parcelamento residencial? (Typically, how much time takes the process of approval of a residential land subdivision?)

Menos de 50 lotes (Less than 50 plots) Mais de 50 lotes (More than 50 plots)Menos de 3 meses (Less than 3 months) 3 a 6 meses (3 to 6 months) 7 a 12 meses (7 to 12 months) 13 a 24 meses (13 to 24 months) Mais de 24 meses (More than 24 months)

INFORMALIDADE URBANA (URBAN INFORMALITY)

14. O município tem um cadastro das habitações informais? (Has the municipality a cadaster of informal houses?)

Ano(s) do levantamento (year(s) of data collection) Cadastro de favelas (cadastre of slums) Cadastro de loteamentos clandestinos (cadastre of irregular land subdivision) Cadastro de ocupação ou invasão de terra urbana (cadastre of urban land squatting or invasion)

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15. Quais são os números estimados pela prefeitura de unidades construídas e de famílias nos seguintes tipos de assentamentos humanos:

Favelas (slums):__________________Loteamentos clandestinos (irregular land subdivision): _______________________Ocupação ou invasão de terra urbana (urban land squatting or invation): ____________________________

16. Há levantamento da renda da população residente nos assentamentos? Se houver, a percentagem da população residente nesses assentamentos nas seguintes fairxas de renda:

Até 3 salários mínimos ______________%Mais de 3 salários mínimos e até 6 salarios mínimos ______________%Mais de 6 salários mínimos e até 12 salários mínimos ______________%Mais de 12 salários mínimos ______________%

17. Há levantamento da ocupação da população residente nos assentamentos? Se houver, quais sao as ocupações mais frequentes e qual a porcentagem da população residente nos assentamentos em cada uma dessas ocupações:

Ocupação Porcentagem1. ______________%2. ______________%3. ______________%4. ______________%5. ______________%6. ______________%7. ______________%8. ______________%9. ______________%10. ______________%

CONTROLE E PUNIÇÃO DA INFORMALIDADE URBANA (CONTROL AND PUNISHMENT OF URBAN INFORMALITY)

18. Como o município combate a informalidade urbana? (How does the municipality fights urban informality?) Através da fiscalização ostensiva e aplicação de multas (Through ostensive fiscalization and aplication of fines) Através da participação da população (Through the participation of the population) Através de campanhas educativas (Through informative campaings) Não há uma estratégia de combate à informalidade (There is no strategy to fight informality) Outras. Quais? (Others. Which?)_______________________________________________________________

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