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Transportation Research Procedia 00 (2017) 000–000
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Transportation Research Procedia 25 (2017) 5121–5143 www.elsevier.com/locate/procedia
World Conference on Transport Research - WCTR 2016 Shanghai. 10-15 July 2016 World Conference on Transport Research - WCTR 2016 Shanghai. 10-15 July 2016
Access as a determinant variable in the residential location choice of low-income households in Bogotálocation choice of Access as a determinant variable in the residential low-income households in Bogotá Juan Pablo Bocarejoa*, Luis A. Guzmana, Ingrid Portillaa, David Meléndeza, Ana M. a a , Carlos Rivera a Juan Pablo Bocarejoa*, Luis A.Gómez Guzman , Ingrid Portillaa, David Meléndeza, Ana M. a a Grupo de Sostenibilidad Urbana y Regional, Universidad de los Andes, Bogota, Colombia Gómez , Carlos Rivera a
a
Abstract
Grupo de Sostenibilidad Urbana y Regional, Universidad de los Andes, Bogota, Colombia
Abstract Providing adequate, conveniently located social housing (SH) is one of the main challenges that developing cities face nowadays. This research aims to provide evidence showing how low-income households value different attributes and their trade-offs when Providing conveniently social housing is onemay of the main challenges that developing cities face nowadays. locating inadequate, the city of Bogotá andlocated how different location(SH) options bring different benefits from households’ point of view. This research aims to provide showingbased how low-income attributes and their Accordingly, several locationevidence choice models on revealedhouseholds preferencevalue (RP)different and stated preference (SP)trade-offs surveys when were locating in Regarding the city of the Bogotá and howthe different options may bring was different benefits from households’ pointand of costs, view. estimated. RP models, utility location related to their settlement calculated considering travel time Accordingly, severalcost, location models based revealed andoptions. stated In preference (SP) werea quality and housing based choice on households’ currenton location andpreference alternative (RP) location the case of the surveys SP models, estimated. Regarding the RPlocation models,scenarios, the utilityincluding related to theirtime, settlement travel time and costs, survey presenting different travel house was size calculated and house considering rent, was applied. Analyses were quality andfor housing cost, based on with households’ location andlevels. alternative location options. the case of the shorter SP models, conducted two income groups, poor andcurrent very poor income For the RP models, theIn projects with travela survey presenting including house size and house rent, was applied. Analyses were time, located city different center orlocation close toscenarios, employment centers,travel showtime, higher utilities, while outskirts locations reveal the lowest. conducted forthetwo poor degree and very incometowards levels. For RP models, the projects the shorter travel Meanwhile, SPincome modelsgroups, presentwith a higher ofpoor sensitivity the the housing cost, especially for with the very poor model, time, located cityattractive center or close the to employment centers, show higherexpenditure, utilities, while locations reveal lowest. making the most projects ones with lower monthly housing evenoutskirts though the travel time stillthe weighs in Meanwhile, the SP models a higher of sensitivity the housing cost, especially for the very poor model, the poor-household models.present Nonetheless, in degree all the analyses, theretowards is a disjunction between location attractiveness and housing making the most projects the ones with monthly housing expenditure, evenproportion though theoftravel time still weighsthat in costs. These costsattractive arise as an important factor forlower low-income households because of the the monthly income the models. Nonetheless, in very all the analyses, there isthea disjunction between location attractiveness andforhousing theypoor-household represent. When comparing poor and poor households, former group has greater willingness to pay better costs. These arise as an important factor for low-income households because of the proportion the monthly locations andcosts living conditions, while the latter is highly sensible to costs. In particular, special of attention must income be paidthat to they represent.as When comparing and systems very poor the former group has greater willingness pay conditions for better accessibility, improvements in poor transport andhouseholds, better distribution of employment may translate into to better locations and future living SH conditions, when locating projects. while the latter is highly sensible to costs. In particular, special attention must be paid to accessibility, as improvements in transport systems and better distribution of employment may translate into better conditions when locating future SH projects.by Elsevier B.V. © 2017 The Authors. Published Peer-review under responsibility of WORLD CONFERENCE ON TRANSPORT RESEARCH SOCIETY. © 2017 2017 The © The Authors. Authors. Published Published by by Elsevier Elsevier B.V. B.V. Peer-review under responsibility of WORLD CONFERENCE ON TRANSPORT RESEARCH SOCIETY. Peer-review under responsibility of WORLD CONFERENCE ON TRANSPORT RESEARCH SOCIETY.
*
Corresponding author. Tel.: +57-1-3394949. E-mail address:
[email protected] * Corresponding author. Tel.: +57-1-3394949. E-mail address:
[email protected] 2214-241X © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of WORLD CONFERENCE ON TRANSPORT RESEARCH SOCIETY. 2214-241X © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of WORLD CONFERENCE ON TRANSPORT RESEARCH SOCIETY.
2352-1465 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of WORLD CONFERENCE ON TRANSPORT RESEARCH SOCIETY. 10.1016/j.trpro.2018.02.042
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Keywords: inequalities; location models; household preferences; social housing; Bogotá
1. Introduction Urban planning in Latin American cities has often been overcome by the rapid growth of informal low-income settlements. This development has been characterized by sprawling (overflowing municipalities boundaries), fragmented and unplanned growth, particularly with a total disconnect between transport and land-use planning. This growth model is based on urban developments that favor low housing costs and high transportation costs well described in Mexico by the 3D land-use model: Distant, Disperse and Disconnected (Sarmiento et al. 2014). This model contrast with the 3D “good” development model: Density, Diversity, and (well) Design of urban environmental proposed by Cervero and Kockelman (1997), which later evolved into a 5D model adding transport as a key element (Distance to transit and Destination access). This urban model seeks more sustainable cities by increasing travel by public transport and reducing VKT per capita. In Bogotá city, the urban development is more like Mexico 3D model that proposed by Cervero and Kockelman. An important part of the low-income settlements has developed informally in different parts of the city, leading to unplanned urbanization with several deficits in urban conditions, including a lack of public services and mainly not being well located. Although over time those neighborhoods have received public investment aimed at improving the initial situation, it has been difficult to change the negative impact that an inconvenient location may have had on access to city opportunities. This structural housing deficit, along with an ever-increasing demand for new housing, is putting great pressure on planning authorities to find adequate solutions. Since the 1990s, Colombian cities have been committed to improving urban planning processes to avoid informal and uncontrolled growth. Land-use plans that are adequately integrated with public services’ master plans and mobility plans have begun to be formulated, and providing adequate housing for the low-income population appears always to have been one of the main challenges. This is a concern not only for local authorities but also for the national government, which has launched several social housing (SH) programs over time, the last one being an ambitious initiative to provide free housing to low-income citizens. Questions arise regarding the location of those projects, as local authorities have to correspond to this national government program by providing available land to develop while, at the same time, also adequately integrating them with the city plans. Especially in Bogotá, there is a huge discussion concerning where to provide this land as the former administration (2012-2015) is promoting the redevelopment and densification of the city center. Furthermore, to provide adequate social housing solutions, it is also important to understand the target population’s (low-income households’) preferences. The objective of this research is to gain an understanding of how low-income households value location attributes such as housing costs, access and transport facilities, vital private space and public spaces, to find evidence of the characteristics that these households take into account when locating and consequently to provide information to support the SH planning process. 2. Theoretical framework A large amount of scientific literature has focused on explaining residential location choices because understanding the reasoning behind household’s decisions and preferences contributes to urban, housing and transport planning policy making and evaluation. 2.1. Residential location choice modelling Several empirical studies have investigated the impact of households’ location on transport behavior; however, the trade-off between the transport and the housing costs (location decisions) has attracted much less attention. Household residential location choices are a function of a wide range of spatial attributes, valued in different ways
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according to household characteristics. Most of the literature on this topic has explained households’ decision from an economic point of view, based on the alternative that maximizes their utility. Usually, households’ utility functions include variables related to racial and socioeconomic factors, the built environment or neighborhood layout, activities’ density, housing affordability and transport system attributes (Kaplan, Frenkel & Bendit 2013, Shiftan, Hershkovitch-Sarusi & Prashker 2008, Montgomery, Curtis 2006, Weisbrod, Ben-Akiva & Lerman 1980). Regarding the built environment or neighborhood layout and population density, these are taken into account when deciding on the residential location. Some studies have found that household and neighborhood characteristics explain residential location choice (Guo, Bhat 2007, Sermons, Koppelman 2001, Ben-Akiva, Bowman 1998); in particular, access to open space and natural features and neighborhood design preferences affect location decisions (Morrow-Jones, Irwin & Roe 2004, Vogt, Marans 2004). Hunt (2010) a priori included in his research the trade-off elements of urban form and transport, such as mobility, air quality, traffic noise, treatment of neighborhood streets, development densities and funding sources such as taxes. He found that amongst them, housing type, traffic noise and municipal taxes or rent have the greatest impacts on residential attractiveness for the typical household. Academics have hypothesized that the transport system supply influences residential location choice. Weisbrod, Ben-Akiva and Lerman (1980) suggested that households make significant trade-offs between transport and other public services. However, the role of public services when choosing where to live is small compared with socioeconomic and demographic factors. On the contrary, housing costs appear to be an important factor, since a small change in housing costs can have an effect on residential location decisions equivalent to the effect of a larger proportional change in travel time. In particular, a vast literature body has focused on analyzing the effect of the workplace on residential choices (Guo, Bhat 2007, Anas, Duann 1985, Weisbrod, Ben-Akiva & Lerman 1980). The most commonly used models that explain residential location choice, at the individual or household level, are discrete choice models (Kaplan, Frenkel & Bendit 2013) that state that the “probability of individuals choosing a given option is a function of their socio-economic characteristics and relative attractiveness of the option”. The fundamental theoretical basis of this kind of model is the random utility theory (Ortuzar, Willumsen 2011), which assumes that individuals from a homogeneous population Q behave rationally as homo economicus subject to some constraints (i.e. budgetary, social and physical, among others) and, as in the economic consumer theory, it is assumed that the decision-maker is endowed with perfect discrimination capability (Ben-Akiva, Lerman 1985). Individuals can choose from a finite set of alternatives A={A1,A2,…,AN,}, and there is a set X of vectors describing individuals as well as the measurable attributes of the alternatives. Thus, an individual q is described by an attribute vector x∈X and will face a choice set A(q)∈A. Each of the Aj∈ A has an associated utility Ujq for the individual q ∈Q. Since the analyst or modeler has incomplete information about all the elements involved in the decisionmaking process, uncertainty must be taken into account. In this regard, besides having a measurable deterministic (systematic) component Vjq, a random component εjq is introduced into the utility function, reflecting nonmeasurable attributes like particular tastes, idiosyncrasies and observation or measurement errors. Depending on the probability distribution of the random utility term, different kinds of models can be derived. In the particular case of residential location, the logit family models are widely used, so we focus our attention on them (Schmidheiny, Brülhart 2011, Kockelman, Nurul Habib 2008, Kumar, Krishna Rao 2004, McFadden 1977), particularly on the multinomial logit model (MNL). According to mathematical derivation, the aforementioned probability becomes: 𝑃𝑃𝑗𝑗𝑗𝑗 =
𝑒𝑒𝑒𝑒𝑒𝑒(𝑈𝑈𝑗𝑗𝑗𝑗 ) 𝑒𝑒𝑒𝑒𝑒𝑒(∑𝑘𝑘 𝜃𝜃𝑗𝑗𝑗𝑗 𝑥𝑥𝑗𝑗𝑗𝑗𝑗𝑗 ) ⁄ = ⁄ ∑𝑖𝑖 𝑒𝑒𝑒𝑒𝑒𝑒(𝑈𝑈𝑖𝑖𝑖𝑖 ) ∑𝑖𝑖 𝑒𝑒𝑒𝑒𝑒𝑒(∑𝑘𝑘 𝜃𝜃𝑗𝑗𝑗𝑗 𝑥𝑥𝑖𝑖𝑖𝑖𝑖𝑖 )
(1)
Regarding the explanatory variables, they can be of two different types: case-specific or alternative-specific. The former refers to attributes that appear in the utility function of every alternative, the coefficients of which can be assumed to be identical; in this case, these are household-specific variables such as income or household size. On the contrary, the latter kinds of variables do not appear in the utility function of all of the alternatives, because their coefficients vary across alternatives. These are the housing and transport costs and the urban facilities in each zone.
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For further detail and the mathematical derivation of the models above, refer to Ortuzar and Willumsen (2011), BenAkiva and Lerman (1985) or McFadden (1977). As explained later in the methodology, the transport cost (time or monetary) is not case-specific, because each household’s commuting destination remains fixed and therefore each location alternative implies different transport costs. This MNL is the base for the revealed preference (RP) model; for the stated preference (SP) model, a binary response scheme is used between two location alternatives and in this case the MNL becomes a binomial logit (BL). 2.2. Accessibility and social exclusion Besides being an important element of residential location, transport can be seen a fundamental tool for overcoming social exclusion since it modifies accessibility. From a classical point of view, accessibility refers to the potential for opportunities for interaction (Hansen 1959), which can be understood as the ease of reaching activity areas using a given transport system (Dalvi, Martin 1976). In public transport, this potential is influenced by landuse patterns as well as by the characteristics of the transport alternatives, elements that can constrain the availability of opportunities (Ben-Akiva, Lerman 1985). The combination of these factors translates into additional barriers and costs for travelling, which limit the ease of reaching opportunities even more (Bocarejo, Oviedo 2012). Thus, accessibility can be used as an indicator of spatial inequalities linked to social inequalities. Accessibility analysis focuses on features linked to the effects of the transport infrastructure and services on the connectivity of people and activities (Paez, Ribeiro & Antunes 2010, Gutierrez 2001) or land-use and the location of socioeconomic opportunities, paying particular attention to the number of activities that can be reached within a given range of travel (Ben-Akiva et al. 2006, Halden 2002, Gutierrez 2001, van Wee, Hagoort & Annema 2001) or, recently, to a combination of the transport supply and the spatial distribution of activities (Bocarejo et al. 2014, Bocarejo, Oviedo 2012, Straatemeier 2008, Wu, Hine 2003, Levine, Garb 2002). Another factor that can be considered in the case of low-income households is affordability. For low-income households, the relative cost that transportation may represent when compared with their level of income is too high generally. Transport-related costs have been included in location theories since the early development of location models (Alonso 1964, Wingo 1961), which conceptually explained an individual location decision as one that may be influenced mainly by production and transport costs; that decision is restrained both by the individual’s and by other actors’ willingness to pay for any location. This explains typical urban arrangements in which low-income households, as they have lower economic capacity, are located in the farthest zones of the city, while higher-income households and richer activities are located in nearer zones, in the case of Bogotá. This situation creates social exclusion whereby the low-income population prefers housing affordability (low land values) at the expense of accessibility to city opportunities. Regarding social exclusion, an individual can be considered as socially excluded when he or she resides geographically in a society but cannot be involved in its normal activities (Witter 2009). Particularly, Kenyon, Lyons and Rafferty (2002) defined mobility-related social exclusion as a process whereby individuals are prevented from participating in a community’s economic, political and social life due to reduced accessibility to opportunities caused by inadequate travel means. In the case of people experiencing conditions of exclusion, the travel choice is removed as a result of an urban environment built around the notion of high mobility to access goods and services and to participate in society. These circumstances are often reinforced by poverty and a low quality of public transport services in areas with low motorization rates. However, not all people who experience mobility-related exclusion live in poor neighborhoods and not all people experiencing income poverty are excluded. 3. Bogotá’s background Bogotá is the most important economic and industrial center in Colombia and is the largest and most populous city in the country. It is a dense and compact city with a rapidly growing population. It had 7.9 million inhabitants in 2015, spread over an area of 365 km2. The population migration in the late twentieth century in Bogota has
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encouraged a fast and uncontrolled growth of the city’s boundaries. Illegal settlements in the periphery were the most common way of developing low-income housing. In order to know the socioeconomic characteristics of the population of Bogotá, a mobility survey was conducted n 2011 (SDM 2011), in which each household was asked, among other things, their average income per month. The responses were classified in different categories. These categories include eight predefined ranges (1 USD=1,900 COP approx., in COP 2011): Range 1: $0 - $282 Range 2: $282 - $632 Range 3: $632 - $1,052 Range 4: $1,052 - $1,475 Range 5: $1,475 - $2,105 Range 6: $2,105 - $2,895 Range 7: $2,895 - $4,210 Range 8: > $4,210 The high-income group population, which accounts 6% of the population (higher than range 6), is primarily located in the northeast part of the city, while medium-income class (28% of the population, ranges 3, 4 and 5) is spread out in the southern and western areas, and finally, the poorest segment of the population (ranges 1 and 2) lives in the outskirts of the city. This has created an extremely unequal urban configuration with the most vulnerable groups located in the farthest and less accessible places where population densities attain the maximum value. Lowincome population is often excluded from Bogotá’s dynamic lifestyles due to poor access to opportunities. Regarding urban built environment as a whole, the city is characterized as a low-rise, reduced-green-space urban area. In average, the number of floors is around 2.05 and for predominantly residential constructions it goes up to 2.3. Despite this low-rise structure, the average population density remains high due to the average indoor space per capita (dwelling space) of 25 m2/inhab. This means that the population density in low-income zones is around 23,100 inhab/km2, 12,700 inhab/km2 in medium-income zones and 7,500 inhab/km2 in the wealthiest zones (see Fig. 1). Moreover, the green area only represents 7% of the city and provides around 4 m 2/inhab. This value is significantly below the World Health Organization’s standard of 10-15 m2/inhab. The low-income group presents the highest density, composed mainly by low Built-up Area per Capita (BUAC), neighborhoods predominately residential and low-rise development which corresponds to the type of illegal settlements on the outskirts of the city. As for the medium-income group, it presents a comparable density but it is achieved by more occupied area, less residential neighborhoods and with larger (BUAC). Finally, the high-income group presents low-density pattern and also with the highest BUAC.
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Fig. 1. Population density and average household income
3.1. Urban mobility As a household’s income increases, its mobility grows. The trip rates for income ranges 1 and 2, which refer in this study to the very poor (VP) and poor (P) household categories, are 6.08 and 7.08 trips per day, while for a household belonging to income range 8, the trip rate is 20% higher. Additionally, there are important differences in the use of transport modes. Households from the first two income ranges mainly use public transport and nonmotorized modes, while in the case of medium and high-income households the use of private motorized modes increases. However, when considering work trips, the aforementioned differences increases as expected, this shows that, the gap between poorest and wealthy households is bigger (see Fig. 2). This indicates that low-income households are very sensible to transport conditions and therefore their mobility is reduced mainly to commuting trips, caused conjunctly by time and cost issues, as these households have the largest commuting times and transport costs and have a significant expenditure relative to their income.
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Fig. 2. Average number of daily trips per household and income range
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In any case it is remarkable to see that the difference between the low-income households (ranges 1 and 2) and wealthiest households (ranges 6, 7 and 8) is around a trip per day when sometimes the number of household members may be more than 3 people among the same groups. An average of 7.2 trips per day per household is observed in the study area. The change in the quantity and transport mode used for work trips and all-purpose trips can be attributed to time spent depending on travel purpose. The employment location it is also important. A remarkable fact is that in several zones the travel time spent (public transport) in non-work activities is similar to the travel time spend in work activities. This shows that in these zones, there is not a good range of non-work related activities and the supply of public transport system can be improved. Another curious data on non-motorized is that regardless of the income range, the richest and the poorest families of the city are the ones that travel more distance and time.
Fig. 3. Average travel time per travel purpose and income range
In assessing the average travel time is observed that the higher the household income is, less travel time experience the traveler. The difference between the average travel times can be more than a half hour per trip between ranges 1 and 6. 3.2. Housing costs A comparison of the percentage of income spent on public utilities (gas, energy, water and sewage) and house rent reveals important disparities. An average household from income range 8 spent around 19% of its monthly income; in contrast, in the case of lowest income ranges (1 and 2) this amount grows to 57% and 43%. Disparities become even worse if the monthly transport cost is included and according to the calculations, very poor households (range 1) spend 10% approximately and an average poor household (range 2) around 7%.
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Fig. 4. Housing costs by income range
This is a critical condition, firstly if compared with high-income ranges, for which the proportion is less than 20%, and secondly because these costs represent around half of households’ income; when adding transport-related costs, the proportion may total more than 65%. 3.3. Low-income housing policy Bogotá has implemented several strategies to develop SH projects for vulnerable social groups. In 1999, the local government launched Metrovivienda, a land-banking program aiming to foster urban land supply to ease SH with access to urban services and facilities. By 2000, the land use plan of the city (POT for Plan de Ordenamiento Territorial in Spanish) set most of the vacant land in peripheral areas and demanded that, when developing it, a percentage of the plot area must be allocated to SH units. Conversely, the new POT, which has not yet been adopted, envisions a more inclusive city by building SH in the expanded city center and adopting a compact city model that forces developers to include some SH shares in every project to be developed in re-densification. In 2010, the national government set as a goal the construction of a more equitable and solidarity-based society, in terms of access to public amenities and goods and income distribution, particularly through a strategy that seeks to reduce the social and regional inequalities through efficient use of land, providing more public space and fostering organized and planned urban development patterns in Colombian cities. Complementary housing goals are designed to avoid the formation of slums and to improve the conditions of families in substandard housing through the intermediation of the financial sector and the consolidation of an efficient and competitive construction sector. However, the development of these goals is not combined with transport policies that will improve the accessibility to housing and quality of life. Besides, the Metrovivienda program and dispositions stated in the POT that the Colombian national government has recently launched a free housing program, an ambitious plan aiming to provide 100,000 houses in a 2-year period, giving priority to displaced families, among others. Under the aforementioned programs and related policies, nowadays in the city a total of 37 SH projects exist for poor and very poor families, that is, families earning less than 280 USD and less than 630 USD monthly, respectively. The Government plays a central role in these projects, since it subsidizes up to 41%, in the case of very poor households, and 36%, for poor households, of the sales price of an SH unit. Since it was not possible to gather information on the 37 projects mentioned, a total of 18 projects were analyzed (see Fig. 5). On average, these SH
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units measure 45 sqm approximately, cost 18,200 USD and are located primarily on the periphery of the city. Due to the similarities and closeness of projects, they can be differentiated into 6 different groups (see Fig. 5, left side). The first four groups (1-4) are located on the outskirts of the city’s south end, while groups 5 and 6 are located in central areas close to the main activity centers. Nonetheless, a vast majority of the projects – 16 out of 18 – are located in peripheral areas.
Fig. 5. SH project location and main commuting destinations of low-income households
Low-income households’ commuting destinations zones is mapped in Fig. 5 (right). It is clear that trips are concentrated in the east area of the city, where the most of the commercial, official and service activities are located. The top three commuting destinations were selected according to different travel patterns and therefore location suitability. The destinations are: Fontibón (industrial cluster), Las Nieves (a traditional downtown area endowed with several commercial areas as well as private and official institutions), and Chicó-Lago (an area where the tertiary sector usually settles, accounting for around 10% of all the work trips). 4. Proposed methodology This research seeks to determine, by means of discrete choice models, low-income households’ residential location preferences and trade-offs between explanatory variables. Given some differences in mobility patterns and built environments, it was decided to divide low-income households into two segments, to be known as very poor (income range 1) and poor households (income range 2). The very poor (VP) group is composed of households with
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a monthly income below USD 280, while a household is considered poor (P) if its income is higher than this limit but less than USD 630. This research approach consists of the estimation of preference models based on current hypothetical households’ choices, followed by the characterization and classification of a set of SH projects based on the resulting preference models. Finally, analyzing those preferences, some recommendations are made quantifying the changes required in each variable to make projects more attractive. Table 1 explains the methodology and the specific activities undertaken in this research. Table 1. Proposed methodology Analysis
Households’ residential location preferences and trade-offs
Discrete choice modelling based on revealed preference data (RP model)
Discrete choice modelling based on stated preference data (SP model)
Social housing projects
Recommendations
Social housing data collection and calculation Trade-off analysis and improvement assessment
Activities Identification of low-income households based on a 2011 mobility survey Estimation of households’ transport variables based on stated trips: commuting times and costs Definition of distinguishable and independent low-income location alternative zones (clusters) and calculation of their average urban and transport variables Attaching the current location urban variable to households based on clusters: housing costs, size and urban variables Calculation of what-if location alternative variables based on clusterlevel values Estimation of MNL models based on the current location and available alternatives Trade-off analysis between explanatory variables Stated preference survey design and application o Define the variables and levels o Select random and relevant scenarios Estimation of BNL models based on the housing and location selected and discard scenarios Trade-off analysis between explanatory variables Collection of SH data (housing costs, size, local urban amenities) Calculation of future mobility variables for each SH project based on main commuting destinations Estimation of projects’ attractiveness based on the RP and SP models Identification of improvements for SH project cases Definition of the required improvement level and calculation of each explanatory variable change based on trade-offs
The household preference analysis was undertaken to determine the possible trade-offs between housing costs, transport costs and public space, among others, and to find out how low-income households value these variables. As logit models appeared to be suitable, a total of three MNL models, one for each of the analysis groups and one more for the whole sample, were estimated based on revealed preference data and three more based on data from a stated preference survey especially designed and applied for this study. To capture preferences, we made use of two different approaches: the revealed preference (RP) method, which analyses individuals’ preferences based on their current decision, and the stated preference (SP) method, which tries to reflect what individuals would choose given some hypothetical scenarios. In the particular case of low-income household location choice in Bogotá, we identified two main issues in the RP analysis. The first one is that, even though households are expected to be homo economicus, their current housing location choice may have been guided by conditions or factors that cannot be easily included in an analytical model. Residential location choice is a result of decisions made formerly based on valuations of several attributes of the available alternatives, like land value, local roots, a sense of community and even social segregation, elements that change throughout an individual’s life cycle. Therefore, the current residential location may reflect previous tastes instead of present preferences. This can be particularly true in Bogotá, where urban development in the last decades has been guided mainly by the development of unplanned informal settlements, in which social mobility is scarce and household do not tend to move frequently to find better housing positions.
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To work out part of the aforementioned issues, the SP experiment was proposed. The experiment sought to evaluate how a household would choose a location based on two hypothetical alternatives. It is worth noticing that the SP analysis also shows some difficulties regarding its application and results. Firstly, as the analysis is based on hypothetical scenarios, the choices may not be guided by logical deduction but more by taste and by preconceptions of the variables. Secondly, the model is also sensitive to the design of the experiment; both the levels of the variables asked and the way in which the survey is applied may bring bias that can affect the results of the model systematically. 4.1. Discrete choice modelling based on stated preference data To perform the RP analysis, it was decided to estimate three MNL models including characteristics of the built environment, housing conditions and transport costs in their utility function household attributes. A database including travel times, distances, costs and modes of low-income households’ commutes, based on the 2011 mobility survey, and spatial information on the built environment and housing prices, from cadastral information, was produced. To run an MNL model, it was necessary to model other location alternatives for each household; therefore, the city was divided into zones that grouped all the current low-income locations. An iterative process was implemented to group neighborhoods into similar groups with significantly different location variables between them. A spatial cluster analysis was performed to group zones with distinguishable characteristics between them. This analysis was run with 3 variables: travel distance, housing expenditure and contiguity of zones. As a result, a set of 12 different residential location options, named from now on “clusters”, was defined to create a closed number of location alternatives to run the model that would fulfil the logit assumption of independence of irrelevant alternatives (IIA). The spatial cluster analysis responded to this assumption by assuring that each cluster was significantly different from its contiguous clusters based on two explanatory variables that group the main characteristic of households’ location decision: average housing cost and travel distance. Clusters are identified with a number from 1 to 12, 12 being the city central zone and 1 the peripheral one (see Fig. 6).
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Fig. 6. Clusters
The variables’ spatial distribution displays some specific patterns. It is worth noticing that this corresponds to low-income households’ values and not the average values of the zone, which may have other dynamics due to other income-level groups. The average commuting distances are greater in the outer zones at the south end of the city, mainly because the employment center is located in the blank zones where there are no low-income settlements. The housing cost follows the opposite dynamic, with greater values in the central zones; this represents first evidence of a trade-off between transport and housing costs. Nonetheless, this relationship is not always presented; in cluster 12, it is significant in the zones with lower commuting distances but not in the category of the highest housing costs. Once the clusters had been defined, a database of 2,274 households (766 very poor and 1,508 poor) was built with the characteristics of the zones where they are located currently along with 11x2,400 registers representing the location alternatives and their variables for each household if they were located in each of the other 11 clusters. The main variables, their reference values and their sources are shown in Table 2. The location variables are grouped into three different categories depending on their variability between alternatives:
Transport costs (Alternative-Specific/A-S): As these depend on households’ current commuting destinations, it was assumed that even if their residential location changes, their jobs do not. Regarding the transport modes used, it was supposed that individuals do not change their preferences if they change their residential location, except for long-distance non-motorized trips (more than 30 minutes), for which it was assumed that a trip exceeding this threshold shifts to public transport. The costs and times were calculated with a distance-based linear function relating time and monetary cost between origin (cluster)–destination (transport zone, TAZ) pairs in terms of network distances for three principal modes:
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private transport, public transport and non-motorized. This linear function was estimated based on approximately 5,000 low-income commutes registered by the 2011 mobility survey. The time and cost were calculated for each commute and then aggregated at the household level. Location-based variables (Alternative-Specific/A-S): Housing conditions, such as house floor area, floor area value and neighborhood variables (number of urban facilities, green areas, public spaces), were computed through GIS using the current built environment conditions at the cluster or TAZ level depending on the availability of data. Household-based variables (Case-Specific/C-S): Socioeconomic variables, such as household size and the number of workers, vehicles and motorcycles, were taken from each household register of the mobility survey.
Table 2. Variables’ description
Variable
Description (units)
HH commuting costs
Monthly commuting cost per household (USD)/commuting cost per household one way (USD)
Source
Housing cost
Monthly rent per household (USD)
Bogota cadastre survey
HH size Urban facilities’ density Commuting distance
Spatial information Zonal green area density (m2 from Urban of green area/km2 of urban Planning Office of gross area) Bogotá Number of members per household (individuals/household)
Bogota mobility survey 2011
Zonal urban facilities’ density Spatial information (units/km2). Schools, from Urban hospitals, sports fields, Planning Office of cultural and official buildings Bogotá Commuting distance per household one way (km)
$33.8
$22.9
$40.1
$0.84
$0.6
$1.00
$37.3
$22.9
$42.5
$0.93
$0.6
$1.06
VP-HH
63.4
55.1
93.6
2
39.4
10
P-HH
64.7
50.1
109.1
1
33.6
12
VP-HH
$100
$4.8
$111
10
$95
1
P-HH
$133
$9.8
$149
10
$118
2
VP-HH
2,899
34,475
12,822
11
1,162
7
P-HH
1,693
1,974
3,507
1
1,040
11
VP-HH
4.1
1.6
4.4
4
3.6
10
P-HH
4.0
1.5
4.4
12
3.6
9
VP-HH
3.9
3.2
5.9
1
1.6
8
P-HH
4.2
3.8
8.6
1
0.9
12
VP-HH
11.9
10.7
17.7
1
7.5
10
P-HH
12.5
9.6
21.9
1
7.0
12
P-HH
Bogota mobility survey 2011
Bogota mobility survey 2011
Lowest
Std. Dev.
Bogota mobility survey 2011
Commuting time per household (min) one way
Highest
Mean
VP-HH
HH commuting costs
Green area
Household category
Value
CL 5
Value $25.9
CL 4
$0.65 11
$30.3 $0.76
4
4.2. Discrete choice modelling based on stated preference data The stated preference model approach was applied to evaluate the interaction between variables that could not be found in the RP models because of a lack of available data. There was a need to evaluate how households internalize house size when deciding where to locate; therefore, this variable was defined as the first one in the SP experiment. The remaining variables selected were travel time and monthly housing expenditure, which were two of the most important variables that resulted in the RP models and that group the main dynamics incurred in a location decision.
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Just three variables were considered, as each additional variable would increase exponentially the number of scenarios and make it more difficult for the person to evaluate between two different alternatives. The survey was applied to the three main commuting destinations of low-income households (see Fig. 5). The sample size was calculated in each zone for a 95% confidence level and a confidence interval of ±5 min of commuting travel time. The sample was calculated based on the variable travel time to work obtained from low-income workers’ commuting trips to these zones based on the 2011 mobility survey. The SP survey was applied to 450 workers, a total of 150 surveys per zone, once the pollster had identified that the individual was the main working member of the household, that the household was paying rent and that the household’s total monthly income was lower than USD 600, to assure that it belonged to the target population. The SP survey included background questions (household size, current house tenancy condition and monthly rent) and commuting information (trip origin, transport mode and travel time), followed by six different hypothetical location choice scenarios combining house size, commuting time and monthly housing rent alternatives. For the first variable, two levels were included, and for the other two, three were included (see Table 3); thus, 18 different options can be created and combined in 54 different and logical selection pairs. These alternatives were then pooled to create 5 different 6-question surveys. Questions were asked regarding the scenarios in the following form: Would you rather live in a 45 sqm house with an average 30 min commuting time and a monthly housing expenditure of 125 USD or live in a 65 sqm house with 90 min commuting time and also paying 125 USD monthly? Each of the variables remained fixed in one scenario and the remaining three scenarios all had changing variables. Scenarios were selected if they were logical as a scenario, since asking the respondent to choose between a smaller, farther and more expensive house and a bigger, closer and cheaper house would not create valid data to estimate a choice model. The levels of each variable were selected based on the current housing and commuting conditions of the target population. Table 3. SP variables and levels House size (sqm)
Commuting time (min)
Monthly housing cost (USD)
45
30
75
65
60
125
-
90
175
As the survey was just administered to one commuter per household (usually the main worker), it was necessary to transform the commuting time to make it comparable to the RP variables. The time was then multiplied by 1.3 (very poor) and 1.3 (poor) commutes per household to set the variable as the daily commuting time per household. This assumes that the commuting time of every other commuter in the household is the same as that of the survey respondent. 4.3. Trade-off analysis and project assessment This analysis allows us to determine, depending on household preferences and a given level of utility, how households combine the consumption of several goods to obtain the same utility, that is, their willingness to stop consuming a particular good to gain more of another good. Trade-off analysis is founded on three main concepts besides the utility function U(x,y): the indifference curve (U1), the marginal utility MUx and the marginal rate of substitution MSSx,y. The first refers to a set of consumption bundles that produces indifference (different consumption levels of a set of goods produce the same level of utility). The second concept, marginal utility, relates to the gain in utility given a unitary change in the consumption of a particular good or service x: 𝑈𝑈𝑈𝑈𝑥𝑥 =
𝜕𝜕𝜕𝜕(𝑥𝑥, 𝑦𝑦)⁄ 𝜕𝜕𝜕𝜕
(2)
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Finally, the third concept is defined as the negative slope of an indifference curve at a certain point.
𝑀𝑀𝑀𝑀𝑀𝑀𝑥𝑥,𝑦𝑦 =
𝑑𝑑𝑑𝑑 − 𝑑𝑑𝑑𝑑
=
𝑈𝑈𝑈𝑈𝑥𝑥 𝑈𝑈𝑈𝑈𝑦𝑦
=
𝜕𝜕𝜕𝜕(𝑥𝑥,𝑦𝑦) 𝜕𝜕𝜕𝜕 ⁄ 𝜕𝜕𝜕𝜕(𝑥𝑥,𝑦𝑦) 𝜕𝜕𝑥𝑥
(3)
Thus, through MSRx,y, it is possible to capture trade-offs between explanatory variables. These trade-offs are intended to be calculated in the form of the willingness to pay housing rent for an additional square meter of floor area or the minutes by which an individual is willing to increase his/her regular commute travel time to reduce the housing rent by 10 USD monthly. As two different models were estimated, the trade-offs may differ between RP and SP; moreover, it was expected that the trade-offs would vary between poor and very poor households. Theoretically, households with a higher income should have greater willingness to pay for savings in time or better living conditions. The trade-offs were then used to quantify specific SH project improvements by calculating how much a variable has to be increased or decreased to take low-ranked projects to the level of high-ranked ones. 5. Model results and analysis The estimated RP logit models are shown in Table 4. It can be seen that housing cost and commuting attributes, like cost and time per household, are relevant variables for decision-makers belonging to the very poor and poor household categories as well as for the whole sample. The very poor households’ coefficient for transport and housing cost is greater than that of the poor ones, which means that the members of the former group gain more disutility from monetary costs as they live in a more limited income condition; therefore, their location decisions are more sensitive to costs. Conversely, the travel time coefficient is higher for the poor group, which means that, as this group has a better economic condition, they value the time spent on commuting more highly. Relatively, this shows that very poor households locate in places with high commuting times where they can find more affordable housing choices, while poor households consider more carefully the time that they would spend. The MNL models reveal that built environment attributes do not seem to be important for both of the categories of low-income households; the green area resulted as statistically significant at the 1% level and positively related to utility just for the very poor model. When comparing the Z-score coefficients, it stands out that in the poor and all models, travel time is the most sensitive variable, followed by housing cost, transport cost and green area. This means that a change in the standard deviation of travel time results in a greater effect on utility and therefore on its probability of being chosen over any of the other variables. This provides the first sight of the importance of travel time and access to households’ location decisions. Even though the analysis is centered on the low-income population, monetary variables appear at the second level of importance.
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Table 4. RP logit models
(1)
Variables
(2) Very poor HH Coef. Z-score
(3) Coef.
Poor HH
(4)
(5)
Z-score
Coef.
All HH
(6) Z-score
HH commuting costs
-0.66***
-0.378***
-0.28**
-0.160**
-0.42***
-0.241***
HH commuting time
(-3.20)
(-3.20)
(-2.18)
(-2.18)
(-3.78)
(-3.78)
-0.0120***
-0.660***
-0.017***
-0.855***
-0.0152***
-0.80***
(-6.35) -0.074*** (-6.35) 3.56e-06*** (2.36)
(-6.35) -0.355*** (-6.35) 0.122*** (2.36)
(-13.54) -.036*** (-12.57)
(-13.54) -.361*** (-12.57)
(-14.33) -.0038*** (-13.69)
(-14.33) -.687*** (-13.69)
755
755
1,479
1,479
2,234
2,234
Housing cost Green area Cases
Robust z-statistics in parentheses, *** p
