Optimal building heights around Bogoti's first subway line
By
Esteban Castro Izquierdo
Bachelor in Architecture
Universidad de los Andes
Bogotd, Colombia (2002)
Submitted to the Department of Urban Studies and Planning
in partial fulfillment of the requirements for the degree of
Master in City Planning
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at the
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C 2014 Esteban Castro Izquierdo. All Rights Reserved
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b
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z f Thesis Supervisor
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JUN 19 2014
Master of City Planning Thesis
Massachusetts Institute of Technology
Optimal building heights around Bogoti's first subway line
Esteban Castro Izquierdo
Cra 4 No 70A - 82 / A615
Bogoti, Colombia
estebancastroizquierdo@gmail.com
May 2014
Abstract
The necessity of a more efficient and clean mode of transportation, combined with the possibility to
increase real estate supply in Bogotd, makes the first subway line a remarkable opportunity to redefine the
city's future. In order to do so, it is fundamental to understand the potential market demand in the area.
Historically, the market has not been taken into account when defining land use regulations, that is why
incorporating market factors would definitely contribute to a more appropriate land use policy. The
purpose of the present research is to develop a model to estimate optimal building heights that can help in
calculating optimal market densities in the first subway line catchment area. Estimating potential densities
would be useful not only to make better land use and regulation policy, but also to find real estate
opportunities in the subway corridor.
Acknowledgments
I would like to thank the following institutions and people who contribute with information, advice, and
their knowledge: Gustavo Marulanda and Ivan Dario Cubillos (Catastro Bogotd), Alejandro Forero
(Galeria Inmobiliaria), Anuar Prez, Camilo Zea, Adriana Gutidrrez, Ben Golder, and professors Chris
Zegras and Albert Saiz from MIT.
2
Table of Content
1.
INTRO DUCTIO N ..............................................................................................................
5
1.1 Introduction..................................................................................................................................5
2.
FRAM EW O RK ..........................................................................................................................
6
2.1 Bogotd's first subway line and its catchment area ...................................................................
6
2.2 Land values and redevelopment ............................................................................................
8
2.3 Land supply................................................................................................................................10
2.4 Transportation im pact on real estate prices............................................................................
12
25 M arginal prices and costs............................................................................................................14
3.
DATA ........................................................................................................................................
15
3.1 Subway station locations .....................................................................................................
16
3.2 Locational explanatory variables ..........................................................................................
16
3.3 New housing supply...................................................................................................................17
4.
3.3 Cadastral data in catchment area ...........................................................................................
18
3.5 Developm ent costs .....................................................................................................................
19
OPTIMAL HEIGHT DEVELOPMENT MODEL..........................................................
22
4.1 Data processing .........................................................................................................................
22
4.2 OLS Regression model ..............................................................................................................
23
4.5 M arginal revenues......................................................................................................................26
4.5 Optimal building heights ...........................................................................................................
5.
28
CO NCLUSIONS.......................................................................................................................32
5.1 M ain findings and further steps .............................................................................................
References ...........................................................................................................................................
32
33
3
Appendix .............................................................................................................................................
35
A .1 Spatial distribution of dwelling density ................................................................................
35
A .2 Spatial distribution of people density by UPZ......................................................................
35
A .3 Spatial distribution of employment density by UPZ.............................................................
36
A .4 Spatial distribution of reported traffic accidents by UPZ .....................................................
36
A .5 Spatial distribution of reported unsafe incidents by UPZ ...................................................
37
A .6 Spatial distribution of Transmilenio lanes and stops ............................................................
37
A .7 Spatial distribution of bus stops ...........................................................................................
38
A .8 Spatial distribution of dedicated bicycle lanes......................................................................
38
A .9 Spatial distribution of parks ..................................................................................................
39
A .10 Spatial distribution of public sport facilities ........................................................................
39
A .11 Spatial distribution of museums.........................................................................................
40
A .12 Spatial distribution of churches .........................................................................................
40
A .13 Spatial distribution of public libraries.................................................................................
41
A .14 Spatial distribution of public schools.................................................................................
41
A .15 Spatial distribution of non public schools..........................................................................
42
A .16 Spatial distribution of higher education centers.................................................................
42
A .17 Spatial distribution of health institutions ............................................................................
43
A .18 Spatial distribution of drugstores .......................................................................................
43
A .19 Spatial distribution of malls ................................................................................................
44
A.20 Spatial distribution of markets ................................................................................................
44
A .21 Spatial distribution of hotels ...............................................................................................
45
A .22 Spatial distribution of tourist attractions ............................................................................
45
A .23 Spatial distribution of police stations and military facilities ............................................
46
A .24 Spatial distribution of major industrial centers ...................................................................
46
A .25 Reported police incidents in 2010/01 - 2011/08 .................................................................
47
A .26 Python script to calculate euclidian distances ..................................................................
48
A .27 OLS mean observations by stratum ...................................................................................
49
4
1.
INTRODUCTION
1.1 Introduction
With nearly seven million people, and a mono-centric functioning model, Bogoti is experiencing an
economic and physical growth process, caused mainly by large capital inflows, population migration,
macroeconomic stability, decrease in violence, and until the beginning of this year, a stable political
environment.
While the demand for real estate is increasing at a fast pace, supply of land is severely constrained not
only by the cities' geography and political boundaries, but also by historical land use patterns, the lack of
available green fields, and current zoning and regulations dictated by its master plan.
Over the past 16 years, Bogotd has undertaken several projects to enhance mobility. The city has built
nearly 112 km of the BRT system Transmilenio, and has been trying to shift from an "informal" bus
transit system to an organized transport system. It also has built 300 km of a dedicated bike lane network,
as well as making unsuccessful effort to control private car usage. Still, citizens are experiencing
unbearable congestion and intolerable levels of pollution. While some responsibility lies with the lack of
infrastructure maintenance, and its capacity, some transportation experts and urban planners argue that a
subway system is necessary to address transportation challenges in the city.
Whether or not to build the first subway line -FSL- has been a very contentious debate. Opponents argue
that the Transmilenio system should be the priority, and could be finished with the same resources the
subway would cost. Another argument is that the city should not take the risk of investing in this high
technology infrastructure which the country has no experience implementing. Conversely, promoters
claim the subway will be more efficient, will have a lower impact on the urban fabric, and that property
values are expected to increase more than in the case of the BRT.
After many years of debate among the government agencies, academic institutions, politicians, and
specialists, the city has recently embarked on an ambitious project to build Bogoti's FSL. The necessity of
a more efficient and clean mode of transportation, combined with the possibility to increase real estate
supply by redeveloping the subway corridor, makes this project a remarkable opportunity to redefine the
city's future. The subway line will mean a complete urban renovation process, which implies
improvement in transportation efficiency, and also positive impacts in social, economic, and politic
domains.
5
This urban renovation process could only be accomplished by aligning land use policies to the FSL
catchment area. In order to do so, it is fundamental to understand the potential market demand in the area.
Historically, the market has not been taken into account when defining land use regulations. Incorporating
market factors would definitely contribute to a more appropriate land use policy.
The purpose of the present research is to include market factors for estimating optimal building heights
that can help in calculating optimal market densities in the FSL catchment area. Estimating potential
densities would be useful not only to make better land use and regulation policy, but also to find real
estate opportunities in the subway corridor.
Bogoti's master development plan, Plan de Ordenamiento Territorial -POT-, has not yet established a
direct relationship between the the FSL corridor and land use policies and regulations. Even worse, there
is no clear linkage between public transportation and real estate development. Consequently,
redevelopment calculation scenarios are helpful to guide to policy and decision making to determine the
kind of city that should be fostered. These scenarios can serve as a tool to estimate future property and
betterment levy tax variations, future infrastructure needs, public space requirements, zoning and
regulation strategies, social and income redistribution policies, or even to look for alternative financial
strategies for the construction of the subway.
This study is structured as follows: The second section explains the study framework, and the third
section describes the data used in the analysis. The fourth section presents the methodology and results of
the model, while the last section discusses conclusions and further steps.
2.
FRAMEWORK
2.1 Bogoti's first subway line and its catchment area
Even though more than a dozen initiatives to build a subway in BogotA have been made since 1942, it was
not until 1997, when Antanas Mockus was appointed as the city mayor, that the FSL was included in the
conceptual design of the integrated system of mass transit. Unfortunately, this initiative was frozen when
Enrique Pefialosa was in office as mayor in 1998. Nevertheless, during his mayoralty, Transmilenio's first
phase was designed and implemented.
6
In 2007, during his election campaign, Samuel Moreno promised that if he was elected, he would start the
FSL construction. During Moreno's mayoralty, the city council defined the first subway line as a priority
project. For that purpose, an inter-institutional commission was created. The commission was led by the
city transportation department (Secretarfa Distrital de Movilidad) and had the support of other
government institutions, the Inter-American Development Bank, and World Bank. The commission
managed to elaborated the terms of reference for the "conceptual design of the subway mass transit
network design and operational, legal, and financing modeling"'. Those terms of reference have served as
the structure for the subsequent steps to develop the subway project.
The terms of reference had five stages: The first stage was a comprehensive study of current and future
transportation needs of Bogoti, the POT, and forecast projections about cultural, economic, social,
demographic, environmental, and mobility and local characteristics. This stage also included a proposal of
various alternatives for the subway line location. The second stage was the assessment of the alternatives
presented and the priorities for implementing the selected one. The third stage consisted of the
operational design for the FSL. The fourth stage involved the detailed designing, and the fifth stage was
the financial and legal structure.
All stages were open to public bidding. While the first three were developed by a consortium led by Sener
Ingenieria and Transporte Metropolitano de Barcelona, the forth stage will be delivered in September
2014 by another Spanish consortium led by Euroestudios, Idom and Cano Jim6nez.
An important milestone which reinforced the need the city for in building the subway, was in 2011, when
Steer Davis Gleave did a revision, actualization, and calibration of the transportation model for Bogoti
and its surroundings, including the subway project, Transmilenio expansion, the integrated public
transport system (SITP), and La Sabana Light Rail.
The FSL will connect downtown and the financial district with the Tintal neighborhood located on the
southwest outskirts of the city, and the Usaqu6n neighborhood located on the northeast side (see Figure
1). The conceptual design of the FSL contemplates a total of 29 stations. Out of the total 29 km, 25 km
will be underground, 3.5 km will run at ground level using existing rail corridors, and the remanning 0.5
km will be elevated.
1 Docurnento de Empalme, Proyecto Metro de Bogota. 2011
7
At first neighborhoods are rural land on the periphery of the city waiting to be developed. As the city
expands, this arable land becomes occupied by single-family homes which are usually owned by similar
socioeconomic or ethnic groups. After a long process of maturity and consolidation, the neighborhood
could enter either a stage of more intensive redevelopment, or of decline. Redevelopment could occur
because its location becomes a premium one, because its highest and best use shifts to a more profitable
one due to proximity to certain uses, or because it benefits from public infrastructure improvements. This
is the case of a new mass transit line. The possible downgrading could be caused by aging housing stock,
insecurity, and migration. From the two possibilities explained above, redeveloped and depressed
neighborhoods could either started again a process of renovation and densification, or enter a decline
stage.
An example in Bogoti of this process of double redevelopment, is the Chic6 neighborhood. In the early
1950s the area was a farm on the fringes of the city. Between 1951 and 1954 it was developed as a high
class suburb with single-family homes. As the city grew, and the neighborhood was no longer on the
border, its highest and best use changed from one floor houses to 4-5 story apartment buildings. Around
seven years ago, due to a higher demand for high-class well-located dwellings, and surrounding
concentration of offices, a second wave of redevelopment started. Nowadays, the former low-rise
apartment buildings and the few remaining original family homes are being demolished and replaced by
8-10 or even 15-story apartment buildings.
As happens with neighborhoods, single properties also evolve with time. They have their own life cycles
which begin when sites are developed, followed by a natural decline process. Afterward, the buildings are
demolished, and a new cycle starts over again with the redevelopment of the property (see Figure 2).
Buildings could lose value because of physical, economic or functional obsolescence. While physical
obsolescence has to do with natural aging processes, economic obsolescence refers to how a building
cannot fulfill its highest and best use due to a higher density demand for the same use, or a demand for a
new use. On the other hand, functional obsolescence is related to changes in habits and preferences such
small-space housing trends, and high-flexibility office spaces.
A property's value is equal to the sum of the land and the building values. Every time a site is redeveloped
it reaches is highest and best use. Then, as time passes, the property value gradually decreases, until it
equals the land value. As properties lose value, the option value of the land to be redeveloped increases
9
until it reaches the point R, where it is more profitable to redevelop. Redevelopment will occur when the
land price of new developments exceeds the property value in its current use minus demolition costs.
U
E
-
-------
------
L
R
R
R
Time
P= Property value
R= Construction or redevelopment points in time (typically between 30-100 years between)
C=Land redevelopment call option value
U= Usage value at highest and best use at time of reconstruction
S= Structure value
L=Land appraisal value (legal value)
K=Construction (redevelopment) cost excluding acquisition cost
Figure 2 - Property Value Components 3
2.3 Land supply
Land as a factor of production plays a key role in real estate development; therefore, it has a major impact
on housing prices. Land supply is principally determined by four factors: geography, historical land-use
patterns, the financial asset market, and policies and regulations that rule a housing market.
4
Bogotd's physical growth has been defined by its geography. On the one hand, the steep mountains on the
east have limited development, and due to various factors, such as better drainage conditions and views,
higher-income neighborhoods have historically located there. Initially, high construction costs on these
steep terrains were the reason that prevented development in this area, but as land prices rose and
construction costs were no longer a constraint, environmental protection measures are now holding back
expansion on this side of the city. On the other hand, the Bogotd River and its wetlands have restrained
the city expansion to the west side. Being a high-risk flooding zone, the river area has drawn lower
income communities, due to low land prices.
"Historical land-use" patterns refers to the agglomeration of certain activities, such as employment
concentration, government centers, and neighborhoods with historic value. When specific activities
concentrate in an area, there is a point where land becomes scarce for the specific use. The FSL's
3 Geltner D,
Miller N, Clayton J, Eichholltz P, 2014, Commercial Real Estate Finance and Investment, 3rd edition
4 Saiz A, 2013, International housing finance and economics course notes, MIT
10
As planning policies and regulations can constrain land availability, they can also increase land supply.
By wisely matching transport infrastructure projects with land-use regulations, can create new land. With
the FLS, Bogoti has a unique opportunity to renovate deteriorated large areas, which will greatly increase
housing supply, improve accessibility and mobility, reduce air pollution, and create a more equitable
social place to live in.
Land is an asset, as well as a factor of production. A property owner can either develop a site, or wait for a
higher expected appreciation of his asset, as immediate development might not be optimal. Willingness
of some investors to hold sites that could be developed, also contributes to land shortage and therefore
higher land values as developers have to pay this real option premium.
2.4 Transportation impact on real estate prices
Proximity to mass transit not only impacts the ridership of the system, but also real estate values. This
distance of influence, also known as catchment area, is determined by people's willingness to walk and
their preferences. 5This distance can vary depending on people's age, purpose of the trip, climate,
sidewalk conditions, topography, or crime rates, among others. Even though there is not a strict distance
to determine these catchment areas, according to literature reviewed, range can vary between 250 m and
1,500 m. Figure 4 summarizes a series of studies on how new transit lines have impacted real estate prices
on transit corridors.
BogotA's FSL consultant group, which elaborated the conceptual designs, estimates that the catchment
area should be 500 m (product No 42). It is not clear why the consultants chose that distance; however, as
stations are proposed every kilometer, one could argue that overlapping areas were not desirable. The
consultant group also forecast that prices would increase around 10% in places where prices are currently
very high, and 15% where current land prices are low.
After studying results for other cities and Rodrfguez and Targa's study on Transmilenio 2009, a catchment
area of 1,000 m radius around the subway station is taken as a base for this research project. It is
important to clarify that this research project did not take into account variations on the willingness to
walk under hillside conditions at the east side of the corridor, where the steep roads might increase
walking time and affect people's comfort.
5 Cervero R, Guerra E. 2013.
Is a half mile circle the right standard for TODs? Access No 42
12
As there is no certainty about how much the subway line would affect real estate prices, it is desirable to
simulate different alternatives. The present research study has evaluated four scenarios: a no-price-change
scenario, and 5%, 10%, and 15% increment scenarios.
T
PLACE
Amsterdam,
Netherlands
T
AN(E
PRItF CH
MANrr-
nATA
P Ir-F
33,333 buildings (2003)
Buildings <500m from the train station are 4m taller
(-40%higher). There is an extra 10%effect if the building
are located less than 500m to a highway
Madrid, Spain
1,714 housing units asking
price (2010)
At a 1,000m distance from Metro line 12, prices decrease
between 2.18% and 3.18%.
At a 1,000m distance from Cercanfas commuter rail, prices
decrease between 3.38% and 5.17%
T
Sunderland, Uk
Asking prices before (1998)
and after (2003)
Subway line had no changes in property prices
T
Eastern MA,
USA
1,896 single family sales
price (2007)
Housing prices increase between 9.6% and 10.1% in
municipalities with commuter rail train
T
S. East England,
UK
7,474 housing transaction
between (1997 and 2001)
Housing prices increase 9.3% more in areas where
transportation infrastructure projects where developed
T
Chicago, USA
17,034 single family house
transactions (1983 -1999)
Housing prices near Midway rapid transit line increase
6.89% more
T
Seul, Korea
241 apartment units (1989 2000)
Prices increase by 8.9% in a 1,000m radius around the
new subway line
T
LA County,
USA
3,803 sales price (2000)
No changes in property prices
T
Washington DC,
USA
250 rental price (1992)
Rental prices increase between 2.4% and 2.6% for every
T
Chicago, USA
79 blocks (1980 - 1990)
Residential land values increase by 17% in a 800m radius
from Metra line to the airport
T
Chicago, USA
1,485,000 units (2004)
Housing prices increase between 3% and 8%
LR
LR
Manchester, UK
San Diego, USA
795 housing units (1990s)
1,495 multifamily units
(1990s)
Housing prices increase between 2.1% and 8.1%
LR
New Jersey,
USA
14,068 housing units (1991
2008)
BogotA,
Colombia
3,976 housing units
(2001-2006)
Housing prices increase between 13% and 14% around
B
Bogota,
Colombia
130,692 housing units
Housing decrease on average 4.5% near the BRT, than
elsewhere in the city. No causality is attributed to the
system.
B
BogotA,
Colombia
494 housing units (2002)
Housing prices increase between 6.8% and 9.3% for every
5 min closer to the station
B
Los Angeles
County, USA
3,803 multifamily inots
(2000)
No changes in property prices
B
QTtmV
160m closer to the Metro station
Housing prices increase between 2% and 6%
-
Housing prices increase between 8.3% and 9.8% around
400m
500m
T is subway or commuter rail, LT is light rial, and B is bus rapid transit
Adapted from Rodriguez and Vergel 2010
6
Figure 4 - Mass transit and its impact in real estate prices
6
Rodriguez D, Vergel E. 2010. Comentarios y Anotaciones en el Marco de la Primera Lfnea de Metro de Bogoti.
13
premium to enjoy better views, and better access to light, and to avoid negative externalities such as
pollution or noise.
The main objective in real estate development is to maximize profit. Assuming no zoning restrictions, a
developer will build as many square meters as she could, as long as the cost of producing the last
marginal square meter will be less or equal to the price she can sell it for. In other words, a developer will
build until marginal costs are equal to marginal revenues.
Even though this process of profit maximization could also be applied to a unit's features and finishes,
such as type of kitchen or number of bathrooms, this research study is focus only on understanding how
building heights are related with housing costs and market prices.
Dwelling prices reflect both unit and location characteristics. To determine how people value certain
product characteristics, hedonic modeling is used to decompose unit price into structural and
neighborhood attributes. While "structural" characteristics refers to size, number of bedrooms, bathrooms,
and building age, "neighborhood" characteristics could include variables such as socioeconomic
attributes, access to public transport, crime rates, and proximity to amenities and points of interest. More
complex models could also include land-use diversity indexes or school quality. This price breakdown is
done through a linear regression model, where the structural characteristics of the unit and neighborhood
attributes, known as explanatory variables, are calculated by an ordinary least square (OLS) method. The
hedonic model explains the price of a dwelling with the following linear equation:
Yi=a+/iXi +e
where:
Yi is the market value of property i, a is the constant, Pi are coefficients, Xi are the explanatory variables,
and e is the error in the model.
3.
DATA
Data for the present study was gathered in Bogoti in January 2014, and comes from seven different
sources: Bogoti's cadastral department (Departamento Administrativo de Catastro Distrital), Bogotd's
city planning department (Departamento de Planeacidn Distrital), the national statistics agency
(Departamento Administrativo Nacional de Estadistica - DANE), Bogot~d's subway final conceptual
design document, Galeria Inmobiliaria, Construdata, and a series of interviews with local developers,
architects, and planners.
15
3.1 Subway station locations
Projected subway station locations have changed over the different design phases, not only because of
technical recommendations, but also because of political pressures. When the data was collected, there
were two versions of the FSL station locations, which differ in the location of stations 10 to 15. For the
present research, the locations of the future subway stations were taken from the final reports of the
consulting consortium in charge of the conceptual design. These final reports were downloaded from the
official subway webpage (www.metrodebogota.gov.co).
3.2 Locational explanatory variables
The city base shape files, such as the city's border, districts, neighborhoods, blocks, and lots, as well as
points of interests and amenities, and stratum data, were downloaded from the spatial data infrastructure
unit of Bogota's cadastral department -IDECA- (www.ideca.gov.co). This information was released to the
public on January 24, 2014, and is projected in MAGNA SIRGAS coordinate system.
Reported police incidents data was downloaded from the city planning department website
(www.sdp.gov.co), and corresponds to all incidents occurring between January 2010 and August 2011.
Reported incidents were classified into three categories: perceived unsafe incidents, traffic incidents, and
other incidents. Only the first two categories are used in this research model (see Appendix 25). To be
able to compare results along districts, this data was normalized by hectare (see Appendixes 1, 2) .
Employment and population data was downloaded from the national department of statistics (DANE)
website (www.dane.gov.co). The information contains number of people employed, housing units, and
population. It was aggregated according to city planning zones (Unidad de Planeamiento Zonal -UPZ),
and also normalized by hectare, so it could be compared across them (see Appendixes 3-5).
The data mentioned above was processed to obtain the locational explanatory variables. Figure 6
summarizes points of interest, socioeconomic, and safety data, as well as the computed variables in the
model.
16
COMPUTED VARIABLE
SOURCE
Socioeconomic
IDECA (2014)
DANE (2012)
Safety
Secretaria Distrital de Planeacion BogotA (2011)
IDECA (2014)
Social stratums
Population density
Household density
Police reported incidents
Traffic accidents
Distance to police and military facilities
Public Transportation
IDECA (2014)
Distance to BRT stations
Distance to bus stops
Distance to dedicated bike lanes
Work places
DANE (2012)
IDECA (2014)
Employment density
Distance to major industrial clusters
Education
IDECA (2014)
Distance to higher education institutions
Distance to public schools
Distance to private schools
Shopping
IDECA (2014)
Distance to malls and department stores
Distance to supermarkets
Distance to drugstores
Health
IDECA (2014)
Distance to health institutions
Distance to doctor's office
Recreation and culture
IDECA (2014)
Distance
Distance
Distance
Distance
Distance
Tourism
IDECA (2014)
Distance to hotels
Distance to tourist attractions
to parks
to public sport centers
to museums
to churches
to public libraries
Figure 6 - Locational explanatory variables computed in the model
3.3 New housing supply
Galeria Inmobiliaria is the biggest private real estate data provider in BogotA, and it collects among other
data, information about new housing supply. In January 2014, new housing supply consisted of 2,216
new projects that had 49,190 units. For every project Galeria Inmobiliaria collects information about the
project such as name, address, stratum, neighborhood, total units, down-payment percentage and fiduciary
trust, and its location coordinates. The information also records if the project is affordable housing, and if
it has selling features such as a model unit and a showroom. Additionally, for every unit the data base has
information about unit size, number of bedrooms and bathrooms, garage ratio, unit type (apartment or
house), and selling price. For the spatial distribution of the new supply in January 2014 see Figure 7.
17
3.5 Development costs
The development cost structure that is used in the present research was gathered from series of interviews
with local developers, architects, and builders. Construction prices were consulted using a premium
account in Construdata (www.construdata.com), a private company which delivers construction price
information by location and social stratum in Colombia.
Development and construction costs vary by location, use, size and height of buildings, as well as many
other factors such as quality of finishes, construction technology, and other policy, economic, and political
factors. After comparing several samples of cost structures of new developments across different
socioeconomic stratums in Bogotd's housing market, it was found that cost structures are very similar (see
Figure 8). While all design and technical studies, development and sale fees, financing, taxes and other
costs remain constant as a percentage of total revenues, land and construction costs are the only factors
that vary according to socioeconomic stratums. Although land and construction costs add up to a similar
percentage of total revenues in all stratums, the difference is that land makes up a higher percentage in
higher stratums. For example in stratum 2 land corresponds to 16% and construction costs to 57% of total
development costs, while in stratum 6, land is 28% and construction cost is around 48%, of total
development costs.
The cost matrix in Figure 9 represents the average development cost by stratum and building height in the
FSL's catchment area. As the real estate development market in Bogotd does not include stratum 1, that
stratum was not considered in the cost model.
Land price refers to its transaction price. Construction costs include construction materials, labor,
equipment, contractor fees, urbanism costs, as well as a construction fee charged by the construction
company. In most cases, construction fees are paid as a percentage of the total cost of construction.
Design and technical study fees vary according to the complexity and size of the projects. All projects
require architectural design, soil engineering, structural, mechanical, electrical, and pluming engineering,
oversight, and co-property regulations. Some projects have special conditions that require specific studies,
such as security and control, traffic and acoustic studies, landscape design, and energy efficiency
consultants. The impact on the total cost of these additional technical studies is so low, that they were not
taken in consideration for the present research.
19
1
2
3
Item
Land
(16% -28%)
Construction Costs (47% - 60%)
Direct construction costs
Construction Fee
Design and technical studies fees
Architectural
Oversight
Soil engineering
Structural design
Water and sanitary designs
Electrical designs
Mechanical designs
Security and control
Co-property regulations
Other
% of Sales
28.00%
47.50%
43.00%
4.50%
2.64%
1.40%
0.75%
0.05%
0.12%
0.08%
0.06%
0.04%
0.02%
0.02%
0.10%
Item
Development fee
5 Sales fee and advertisement
6 Taxes, legal, insurance, & Other
Gain Tax (Plusvalia)
Permit fees
Property tax
Bank appraisal
0.004 tax
Legal costs
Fiduciary costs
Administrative costs
Public service fees
7 Financial costs
4
% of Sales
3.00%
4.75%
3.71%
1.00%
0.96%
0.06%
0.02%
0.25%
1.00%
0.15%
0.15%
0.12%
10.40%
1.40%
Construction credit fees
Return on investment
Total Costs
9.00%
100.00%
Figure 8 - Average development cost structure in the FSL catchment area
Development and sale fees are around 3% each, while advertisement expenditure is close to 1.75%.
There are four main taxes that apply to a housing development project in Bogotd. Gain tax applies to land
that has benefited from an increase in its value due to improvements, derived from public infrastructure
investments. Property taxes are paid to the municipality while the land is owned by the developer during
sale and construction processes. The 4 x 1,000 is a national government tax, charted to all financial
transactions, by an amount of 0.004%. Lastly, permitfees are paid to CuraduriasUrbanas,private
agencies that revise and approve construction permits.
After the 1998 real estate crisis in Colombia, almost all housing developments are financed with a presales scheme. To ensure a transparent money management process, fiduciary trusts are used as vehicles to
administer funds in real estate projects. Fiduciary trusts receive buyers incoming funds, and make
disbursements to the developers, subject to construction advancement milestones. Fiduciary costs are
approximately 0.15% of total revenues.
Other administrative costs, utilities connections fees, and legal consultancy sum up 1.27%, while
construction credit fees are on average 1.4% of total development costs. Return on investment is the
opportunity cost of capital, which in this kind of projects is around 9% of total development revenues.
20
BUILDING HEIGHT
5
10
15
20
25
30
STRATUM 2
16.00%
59.50%
2.64%
Land
Construction Costs
Design Fees
4.75%
3.71%
Sales fee and advertisement
Taxes, legal, insurance, & other
9.40%
4.00%
Financial costs
Development fee
Average development costs
STRATUM 3
19.00% Land
56.50%
2.64%
4.75%
Construction Costs
Design Fees
Sales fee and advertisement
3.71%
Taxes, legal, insurance, & other
9.40%
Financial costs
4.00%
Development fee
Average development costs
STRATUM 4
22.00% Land
53.50% Construction Costs
2.64%
4.75%
3.71%
9.40%
4.00%
Design Fees
Sales fee and advertisement
Taxes,legal, insurance, & other
Financial costs
Development fee
Average development costs
160,000
595,000
26,400
184,000
684,250
30,360
202,400
752,675
33,396
232,760
865,576
38,405
279,312
1,038,692
46,086
349,140
1,298,364
57,608
47,500
37,100
54,625
42,665
60,088
46,932
69,101
53,971
82,921
64,765
103,651
80,957
94,000
40,000
1,000,00
108,100
46,000
1,150,000
118,910
50,600
1,265,000
136,747
58,190
1,454,750
164,096
69,828
1,745,700
205,120
87,285
2,182,125
323,000
345,610
362,891
381,035
407,707
448,478
960,500
44,880
80,750
1,027,735
48,022
86,403
1,079,122
50,423
90,723
1,133,078
52,944
95,259
1,212,393
56,650
101,927
1,333,633
62,315
112,120
63,070
67,485
70,859
74,402
79,610
87,571
159,800
170,986
179,535
188,512
201,708
221,879
94,416
72,760
76,398
80,218
85,833
1,700,00
1,81900
1,909,950
2,005448
2,145,829
2,360A12
616,000
1,498,00
665,280
1,617,840
698,544
1,698,732
733,471
1,783,669
777,479
1,890,689
831,903
2,023,037
73,920
133,000
103,880
263,200
112,000
2,800,00
79,834
143,640
112,190
284,256
120,960
3,024,000
83,825
150,822
117,800
298,469
127,008
3,175,200
88,017
158,363
123,690
313,392
133,358
3,333,960
93,298
167,865
131,111
332,196
141,360
3,533,998
99,828
179,615
140,289
355,449
151,255
3,781,377
875,000
1,767,500
92,400
166,250
129,850
329,000
140,000
3,500,00
945,000
1,908,900
99,792
179,550
140,238
355,320
151,200
3,780,000
992,250
2,004,345
104,782
188,528
147,250
373,086
158,760
3,969,00
1,041,863
2,104,562
110,021
197,954
154,612
391,740
166,698
4,167A50
1,093,956
2,209,790
115,522
207,852
162,343
411,327
175,033
4,375,823
1,159,593
2,342,378
122,453
220,323
172,084
436,007
185,535
4,638,372
68,000
STRATUM 5
25.00%
50.50%
2.64%
4.75%
3.71%
9.40%
4.00%
Land
Construction Costs
Design Fees
Sales fee and advertisement
Taxes,legal, insurance,&other
Financial costs
Development fee
Average development costs
STRATUM 6
28.00%
Land
1,260,000
1,348,200
1,415,610
1,472,234
1,531,124
1,607,680
47.50%
2.64%
Construction Costs
Design Fees
2,137,500
118,800
2,287,125
127,116
2,401,481
133,472
2,497,541
138,811
2,597,442
144,363
2,727,314
151,581
Sales fee and advertisement
Taxes, legal, insurance, & other
Financial costs
Development fee
Average development costs
228,713
213,750
178,637
166,950
452,610
423,000
192,600
180,000
4,500,000 4,81500
240,148
187,568
475,241
202,230
5,055,750
249,754
195,071
494,250
210,319
5,257,980
259,744
202,874
514,020
218,732
5468,299
272,731
213,018
539,721
229,669
5,741,714
4.75%
3.71%
9.40%
4.00%
Figure 9 - Average development costs per sqm by stratums in the FSL catchment area
21
4.
OPTIMAL HEIGHT DEVELOPMENT MODEL
4.1 Data processing
The following four-step process was carried out to organize and prepare the data for the regression model
in the present research.
First, cadastral units were filtered by use to ensure only housing units were selected from the cadastral
data base. Then, in order to get an approximation of transaction prices, all cadastral prices were divided
by 0.7, because according to the cadastral department, assessed prices are on average 70% of transaction
prices. Afterward, observations under 20 sqm and over 2,000 sqm, and with prices above $17,000,000
COP psm, as well as units below ground level, were discarded. From the initial 516,625 units in the FSL's
catchment area, only 242,049 remained for the analysis.
Second, as nearly half of the new housing supply projects lack adequate geo-referenced information, they
were geocoded using Google Maps and ArgGIS.
Third, cadastral and new housing supply databases were merged, only keeping the variables common to
both databases. The resulting joint database had a total of 243,320 observations.
Fourth, for every housing unit, two measurements were computed: the distance to the closest point of
interest, and the gravity measure. While the former measurement only considers the closest point of
interest, the latter takes into account how a household is located in relationship with all other points of
interest of the same category. As seen in the gravity equation below, points of interest will be more valued
if they are closer to the household, and less valued if they are farther away.
G = gravity measure
1
=
2
ii (dij)
i = household location
1 = point of interest location
d = distance
If the point of interest was a polygon or a line, such as parks and dedicated bike lanes, the distance was
computed using the ArcGIS Near tool. When the points of interest were points, as in the case of BRT
stops, public schools, or malls, the distance was computed in Python, using the script shown in Appendix
26.
22
4.2 OLS Regression model
The following hedonic model was specified, where price per square meter of the housing units is the
dependent variable, and structural and location variables described in Figure 10 are the explanatory
variables.
Yi=a+fiXi +e
where:
Yi is the market value of property i, a is the constant, Pi are coefficients, Xi are the explanatory variables,
and e is the error in the model.
To understand how the different housing markets in Bogoti can be explained by its structural and location
attributes, dummy variables were created for every socioeconomic stratum. Consequently, building height
and size variables were separated by stratum.
Additionally, for every point of interest variable that involved a distance calculation, a new variable with
its logarithmic transformation was created. For the preliminary models that were tested, these point of
interest variables had 4 variations: the minimum distance, the gravity measure, and the logarithmic
transformation of each of those measures.
After testing the initial models for point of interest variables, only the most statistically variable out of the
four variables was kept in the model; all others were discarded. Then, the model was run again, and the
non statistically significant variables were discarded as well. The final regression model only contains the
explanatory variables that were statistically significant.
Afterward, squared variables for the unit size and the building height were created. The purpose of
creating these squared variables was to account for nonlinear relationships the explanatory variables could
have with the dependent variable.
23
Variable
Description
T
Min
Max
Mean
Dependent variable
PRICE
Cadastral and selling price (COP)
Structural explanatory variables
HEIGHT_1
Floor unit height in stratum 1
HEIGHT_2
Floor unit height in stratum 2
HEIGHT_3
HEIGHT_4
HEIGHT_5
HEIGHT_6
HEIGHTSQ_1
HEIGHTSQ_2
HEIGHTSQ_3
HEIGHTSQ-4
HEIGHTSQ_5
HEIGHTSQ_6
SIZE_1
SIZE_2
SIZE_3
SIZE_4
SIZE_5
Floor
Floor
Floor
Floor
unit height
unit height
unit height
unit height
in stratum 3
in stratum 4
in stratum 5
in stratum 6
Square of HEIGHT_1
Square of HEIGHT_2
Square of HEIGHT_3
Square of HEIGHT_4
Square of HEIGHT_5
Square of HEIGHT_6
Floor area in sqm in stratum 1
Floor area in sqm in stratum 2
Floor area in sqm in stratum 3
Floor area in sqm in stratum 4
Floor area in sqm in stratum 5
SIZE_6
Floor area in sqm in stratum 6
SIZESQ-1
SIZESQ_2
SIZESQ_3
SIZESQ_4
SIZESQ_5
SIZESQ_6
AGE
Square of SIZE_1
Square of SIZE_2
Square of SIZE_3
Square of SIZE_4
Square of SIZE_5
Square of SIZE_6
Age of building
Std. Dev.
C 57,546.08 17,900,000.00 2,442,885.0 1,649,547.0
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
20.00
20.00
20.00
20.00
20.00
20.00
400.00
400.00
400.00
400.00
400.00
400.00
0.00
0.00
0.00
0.00
1.00
11.00
31.00
60.00
55.00
26.00
39.00
121.00
961.00
3,600.00
3,025.00
676.00
1,521.00
1 021.52
1,255.30
1,965.20
1,797.20
1,909.00
1,982.33
1,043,503.00
1,575,778.00
3,862,011.00
3,229,928.00
3,644,281.00
3,929,632.00
93.00
1.00
1.00
1.00
2,079.00
0.01
0.39
1.03
0.63
0.24
0.62
0.03
1.24
6.18
5.23
1.57
4.53
1.25
28.09
51.58
15.03
7.82
24.13
255.69
5,733.37
10,972.75
2,765.60
1,339.31
5,218.91
23.50
0.04
0.01
0.66
114.72
0.16
1.04
2.26
2.20
1.23
2.04
0.46
5.78
35.40
34.23
12.71
24.64
15.94
70.32
91.17
50.39
35.75
68.09
5,097.30
21,249.27
39,422.29
30,414.74
22,258.03
35,326.16
15.60
Building is a new supply project
0.20
Building is historical
0.11
JOINT
Building is part of a joint property
047
UNITS
Number of units in building
209.44
Location explanatory variables
STRATUM_1
Building is stratum 1
0.00
1.00
0.01
0.09
STRATUM_2
Building is stratum 2
0.00
1.00
0.20
0.40
STRATUM_3
Building is stratum 3
0.00
1.00
0.40
0.49
STRATUM_4
Building is stratum 4
0.00
1.00
0.16
0.37
STRATUM_5
Building is stratum 5
0.00
1.00
0.07
0.25
STRATUM_6
Building is stratum 6
0.00
1.00
0.16
0.37
POPDENSITY
People per Ha by district
1.19
498.81
192.34
121.79
DWELLDEN
Housing units per Ha by district
0.28
110.45
57.90
26.44
SAFETY
No of police incidents per Ha by district
0.00
2.87
1.16
0.71
TRAFFIC
No of traffic accidents per Ha by district
0.00
12.55
4.22
3.04
POLICE
Distance to police and military facilities
0.18
591.36
312.98
1,797.76
BRT
Distance to a BRT station
0.50
17,982.57
1,436.82 1,959.44
CYCLING
Distance to a dedicated bike lane
0.39
25,302.77
1,100.74
3,441.20
EMPDEN
Employes per Ha by district
0.06
312.17
73.92
77.19
INDGRA-LOG
Log of gravity to an industrial clusters
-6.07
3.25
-5.00
1.67
H_EDU
Distance to a higher education center
2.64
12,200.26
1,772.99
1,517.93
PUBSCHOOL
Distance to a public school
2.39
505.43
2,152.45
449.22
PRISCHOOL GRA Gravity to private schools
6.43E-06
0.18 8.72E-05 4.49E-04
MALLLOG
Log of distance to a mall
-2.85
3.69
2.74
0.35
MARKETLOG
Log of distance to a supermarket
-0.85
3.67
2.70
0.37
DRUGSTORE
Distance to a drugstore
0.00
146.49
1,041.02
105.99
PARKGRALOG
Log of gravity to a park
2.02
-4.31
-3.34
0.31
SPORTGRALOG
Log of gravity to a sport center
-7.02
-5.87
-3.36
0.42
MUSEUM
Distance to a museum
12,937.18
1.45
2,959.01
2,283.74
CHURCH
Distance to a church
6.23
3,188.63
434.90
249.16
LIBGRALOG
Log of gravity to a public library
-6.88
-2.52
-6.13
0.42
HOTELLOG
Log of distance to a hotel
-4.91
2.71
4.11
0.58
TOURISTLOG
Log of distance to a tourist attraction
0.21
3.63
2.83
0.40
N = 242,460 D = dummy variable, C = continuos variable, US 1 = 2,000 COP, and all distances are measured in meters.
NEWSUPPLY
HISTORICAL
Figure 10 - OLS mean observations
24
Variable
Coefficient
Std. Error
T statatistic
Constant
Structural explanatory variables
638,297.40
58831.370
HEIGHT_1
211,663.90
27,246.960
7.770
HEIGHT-2
HEIGHT_3
HEIGHT_4
HEIGHT_5
-1,205.00
-3,359.17
29,726.69
30,003.77
2,788.393
1,116.817
1,430.595
3,347.995
-0.430
-3.010
20.780
8.960
HEIGHT_6
87,963.20
1,587.287
55.420
HEIGHTSQ_1
HEIGHTSQ_2
HEIGHTSQ_3
-26,728.32
-218.90
155.38
5,514.609
363.556
59.628
-4.850
-0.600
2.610
HEIGHTSQA
HEIGHTSQ5
-425.11
-1,593.11
73.949
255.116
-5.750
-6.240
HEIGHTSQ_6
SIZE-1
-3,477.13
-4,276.09
104.381
246.533
-33.310
-17340
SIZE_2
-4,128.41
64.600
-63.910
-3,101.27
-3,614.09
2,655.98
2,152.81
4.06
5.05
1.90
2.24
-2.53
-1.33
-15,497.80
2,100,316.00
-60,459.45
37.589
52.323
81.812
43.936
0.507
0.139
0.054
0.061
0.079
0.052
86.188
45,663.420
9,418.484
-82.500
-69.070
32.460
49.000
8.020
36220
35.100
36.690
-31.930
-25.340
-179.810
46.000
-6.420
JOINT
25,501.69
4,423.902
5.760
UNITS
3050
6.139
4.970
624,630.10
1,013,008.00
38,340.460
38,345.970
16.290
26.420
STRATUM_4
1,894,975.00
38,588.730
49.110
STRATUM_5
STRATUM_6
POPDENSITY
2,270,263.00
2,892,562.00
1,561.45
39,875.130
38,898.630
42.456
56.930
74.360
36.780
-7,45652
-105,083.60
133.605
4,794.332
-55.810
-21.920
12,96832
-109.22
1,159.683
3.894
11.180
-28.050
-6.500
SIZE_3
SIZE_4
SIZE_5
SIZE_6
SIZESQ_1
SIZESQ-2
SIZESQ-3
SIZESQ_4
SIZESQ_5
SIZESQ-6
AGE
NEWSUPPLY
HISTORICAL
Significance
10.850
Location explanatory variables
STRATUM_1
STRATUM_2
STRATUM_3
DWELLDEN
SAFETY
TRAFFIC
POLICE
-14.19
2.182
CYCLING
5.05
2.014
2.510
EMPDEN
581.90
26.481
21.970
-255,89750
59.82
231.74
5,043,466.00
64,482.85
-156,851.00
282.47
5,108.856
2.216
4.118
2,219,584.000
3,533.236
4,049.170
11.821
-50.090
27.000
56.270
2270
18.250
-38.740
23.900
BRT
INDGRALOG
H_EDU
PUBSCHOOL
PRISCHOOLGRA
MALLLOG
MARKETLOG
DRUGSTORE
**
**
-64,409.40
3,946.066
-16.320
SPORTGRALOG
MUSEUM
CHURCH
LIBGRALOG
HOTEL_LOG
TOURIST LOG
123,658.30
-82.52
-167.39
-146,921.70
-154,622.60
12,453.61
3,882.276
1.499
5.391
4,091.014
3,274.800
5,137.566
31.850
-55.060
-31.050
-35.910
47.220
2.420
N= 242,460 -Adjusted R-square 0.91 - *, *
*** denotes statistical significance at a 90%, 95%, and 99% level of confidence
PARKGRALOG
Figure
**
11 - OLS Regression results
25
OLS regression results are shown in Figure 11. Most of the explanatory variables' signs coincided with
expected results. While building story height is valued in all stratums, unit size adds value in high
stratums, and decreases value in low stratums. Building age, historical conservation, and the number of
reported perceived unsafe police incidents have a negative impact on prices. With stratum, prices also
increase. New supply projects, being part of a joint property building, and being closer to a police station
or military facility also have a positive relationship with prices. Even though traffic accident results seem
counterintuitive, they can probably be explained because higher income neighborhoods have more cars,
and therefore more traffic accidents occur.
Based on the reviews from previous studies, distance to the BRT was expected to have a greater impact
on housing prices. Even though proximity to the BRT is statistically significant, and has a positive impact
on housing prices, the regression model results show that a unit will decrease by less than 2% of its price
when it is 1,000 meters away from a BRT station.
4.5 Marginal revenues
The present study had the objective to estimate marginal revenues of dwelling buildings as a function of
the following three variables: building height, unit size, and proximity to mass transit. As proximity to the
BRT system results had such low impact on housing prices, this variable was discarded. Marginal
revenues were then calculated only as a function of building height and unit size. For these two variables,
marginal revenues were computed, taking into account socioeconomic stratums using the following
quadratic equations:
P* = a + #X + Pi Height_* + Pi Height2 _*+ e
P* = a + /3X +
/3i Size_* + Pi Size 2_*+ e
where:
P is the market property value, a is the constant or Y intercept,
P are the variable coefficients, Height is
the building height, Size is the area of the unit, Xi are all other explanatory variables, * is the stratum of
the unit, and e is the error in the model.
To compute the marginal revenues in both models, all explanatory variables (except building age), were
added to find the Y intercept. Then, building height and unit size were computed using their corresponding
stratum variables. In all cases, mean observations of building height and unit size for every stratum were
used (see Appendix 28).
26
5.
CONCLUSIONS
5.1 Main findings and further steps
Two of the three studies reviewed about Transmilenio showed positive significant relationships between
real estate prices and proximity to the system. In the present research, proximity to the BRT system had a
lower effect than expected. These unexpected results were statistically significant, but did not have any
major impact on housing prices. On average, property values decrease less than 2% when dwellings are
at a distance of 1,000 m from the BRT stations. It is possible that better results could have been obtained,
if the explanatory proximity variable to the BR[ system, had been measured by stratums, as lower
socioeconomic groups rely more on mass transit.
If the subway line does not impact real estate prices, 12 to 19-story buildings will maximize land values
and development revenues, depending on the socioeconomic stratum. In the case where land values
increase 15%, optimal building heights would be between 17 and 25 floors.
For stratums
1 to 4, marginal prices decrease as the unit size increases, while for the upper two stratums,
marginal prices increase as the size of the dwelling increases.
Even though the construction price model was done carefully, in order to validate the precision of the
model, it will be desirable to calibrate it with a construction cost analyst expert in Bogotd's development
market. It will also be desirable to include in the model marginal costs as a function of unit size, in order
to find out average optimal unit size per socioeconomic stratums.
The model also relies on the accuracy of cadastral prices being on average 70% of transaction prices.
Using a real transaction price data base will also improve the precision of the model.
32
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DC: National Association of Realtors & Urban Land Institute
6.
Cervero R, Guerra E. 2013. Is a half mile circle the right standard for TODs? Access No 42
7.
DiPasquale and Wheaton. 1996. "Economics and Real Estate Markets". Prentice Hall
8. Du H, Mulley C. 2007. The short-term land value impacts of urban rail transit: Quantitative evidence from
Sunderland, UK. Land Use Policy 24
9. Forrest D, Glen J, Ward R. 1996. The Impact of a Light Rail System on the Structure of House Prices. Journal
of Transportation Economics and Policy: 30
10. Geltner D, Miller N, Clayton J, Eichholltz P, 2014, Commercial Real Estate Finance and Investment, 3rd
edition, OnCourse Learning
11. Gibbons S, Machin S. 2005. Valuing rail access using transport innovations. Journal of Urban Econ. 57
12. Kim K, Lahr M. The Impact of Hudson-Bergen Light Rail on Residential Property Appreciation. Urban Studies
In press 27
13. Lehner M. 2011. Modeling housing prices in Singapore applying spatial hedonic regression
14. McDonald JF, Osuji CI. 1995. The effect of anticipated transportation improvement on residential land values.
Regional Science and Urban Economics 25
15. McMillen DP, MacDonald JF. 2004. Reaction of house prices to a new rapid transit line: Chicago 's Midway
line, 1983-1999. Real Estate Economics 32
16. Mejia-Dorantes L, Givoni M, Rietveld P, Koomen E. 2010. Assessing the impact of rail stations in Amsterdam:
A third dimension approach. Madrid: ETSI de Caminos, Canales y Puertos
17. Mejia-Dorantes L, PaezA, Vasallo JM. 2010. Analyzing house prices to assess the economic impacts of new
public transport infrastructure: Madrid Metro Line 12. Madrid: ETSI de Caminos, Canales y Puertos
33
18 Mendieta J, Perdomo J. 2007. Especificaci6n y estimaci6n de un modelo de precios hed6nico espacial para
evaluar el impacto de Transmilenio sobre el valor de la propiedad en BogotA. Documentos CEDE
19. Munoz-Raskin R. 2009. Walking accessibility to bus rapid transit: Does it affect property values? The case of
BogotA, Colombia. Transport policy 17 (2010)72-48
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23. Rosenthal, Stuart, Helsley, Robert. 1993. "Redevelopment and the Urban Land Price Gradient". Journal of
Urban Economics. Vol 35: pp 18 2 - 20 0
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27. Zegras C, Grillo C, Jiang S. 2013. Sustaining mass transit through land value taxation? Prospects for Chicago
34
Appendix
A.1 Spatial distribution of dwelling density
Dwellings per Hectare
0
1.00 -20,00
20 01 - 40 00
4001 -6000
M60 01 - 80.00
80 01 - 110 00
A.2 Spatial distribution of people density by UPZ
~,1
People per Hectare
1i - 50
, ,fj51 -100
M101 - 200
201 - 300
301 - 500
35
A.3 Spatial distribution of employment density by UPZ
Employment per Ha
0- 10
11 - 25
26 -50
51 - 100
M101 -200
201 - 350
ii
0
2.0
04 r
0
2E
5Km
I
I
A.4 Spatial distribution of reported traffic accidents by UPZ
Traffic accidents per Ha
0 00- 050
051 -1 00
101 - 2 50
2 51 -5-,00
5 01 - 10 00
1001 -1500
I
36
..............
-........................
.........
..
- -......
.....
-
A.5 Spatial distribution of reported unsafe incidents by UPZ
I
F
<A
-J
Reported incidents per Ha
00 - 0 50
051--10
11 - 1,5
1.6 - 2.0
21 -25
O
2.6 - 30
2,5
I
I
0
2,5
5
I
Km
A.6 Spatial distribution of Transmilenio lanes and stops
j
/
7
p7$
~
./ /.~
II
,r
~
1
,'A>
X
\
I
I
I
-
Transrmlenio stops
Transmtlenio lanes
I
I
I
5 Km
37
A.7 Spatial distribution of bus stops
03..
.
Sir
.41
*
~
.
* *v** ~;*
I
-
~
:
* *:~*
~
*iK4
*
*'
'.
.*.
*~..*
SITP Bus stops
*.
~
-11A.d
.
: ILa *,
..IS
.*
7
-*.,
0
2,5
5 Km
A.8 Spatial distribution of dedicated bicycle lanes
*1i
38
A.9 Spatial distribution of parks
Parks
0
25
5Km
0
25
5 Km
A.10 Spatial distribution of public sport facilities
-
Public sport facilities
39
....
.....
..
..
..
..
A.11 Spatial distribution of museums
-
0
I
Museums
25
5 Km
25
5Km
A
I
A.12 Spatial distribution of churches
*1
-
Churches
0
I
I
40
A.13 Spatial distribution of public libraries
Public
-
hbraries
0
I
5Km
25
A.14 Spatial distribution of public schools
*
*
-.
S*
*
-.
*
-
*
.
-
-
*2
-
-
-
.
*
-
*
*
-
-
*
.*.
''a
13
*
.**
*
*
*
1~.
-
**
*
I
*
*
*
*
*..**'.'.*
*.
**
2.5.a
'I
Public schools
41
A.15 Spatial distribution of non public schools
.,.4
*.1 ..
*
-
*
.
.
.*
..
:
..
a..
..
s
. .
-
-* ;
''..
J..
*
*
-o.
* .-,*.
-*
.*
'
. - '.
'y
.
*
%.
"..
.
.
:
Non public schools
,
0
25
5 Km
0
2.5
5 Km
A.16 Spatial distribution of higher education centers
7
-
Higher education institutions
I.
*
*.
.4
I
I
I
42
A.17 Spatial distribution of health institutions
IM.X
-
.
-...--.-.
J,
*
.
.
--
.
-
.
%....-4
*I
.*.
-
JV.j
44
4.
-
Health
institutions
0
2.5
I
I
5 Km
I
A.18 Spatial distribution of drugstores
4r.
j
*
*i~PAL
***..'.3..If **.*i..:.
tq'a
*Ask-
,s~. .;~
~J~
4
1k;
.
**.
4
lb'
0
- Drugstores
I
25
I
5
Km
K
43
.........
.....................
.............
- ..
.............
......
...
..
...
..-............................
-....
..................
.....................................
-..............
...............
..........
................
-.....
....
.
A.19 Spatial distribution of malls
I
*
*4~
*
'I
-
*.
-
0
Maps
25
5 Km
I~~
2.5
5 KM
I
A.20 Spatial distribution of markets
f,.
I,-
S.
i
..
0.
- Supermarkets
0
I
I
44
..
........
..
....
......
..
..
......
....
..
.. .........
A.21 Spatial distribution of hotels
-irk.*
'.
4
-
.
Hotels
0
25
5Km
0
25
5 Km
A.22 Spatial distribution of tourist attractions
I
~
1:.'*
*
E
**
.1~~
*
*s~W~.
*
4
-
Tourist attractions
45
A.23 Spatial distribution of police stations and military facilities
-
Police stations and miltary facilities
0
2.5
I
5 Km
I
0
25
5 Km
I
I
I
A.24 Spatial distribution of major industrial centers
.1
. *g
-
Major industrial sites
i
46
A.25 Reported police incidents in 2010/01 - 2011/08
CODE
DESCRIPTION
REPORTS
PERCEIVED UNSAFE INCIDENTS
904 Realized robbery
150
905 Robbery in progress
528
906 Sexual violence
910 Personal injuries
911 Shooting
210
6,699
580
922 Narcotics
76
923 Begging
46
932 Disturbance of the peace
934 Brawl
968 Juvenile gangs
976 Missing or lost person
120
5,074
11
22,787
TRAFFIC ACCIDENTS
942 Traffic accidents
87,605
OTHER INCIDENTS
602 Height fall
10,459
604 Respiratory
23,832
613
Unconscious / respiratory arrest
617 Gastrointestinal symptoms
901 Dead
924 Sick
926 Drunk
928 Floding
930 Landslide
931 Fire
941 Mental disorder
967 Minor in a inappropriate establishment
TOTAL
19,073
8,480
55
29,206
1,114
911
1,323
3,047
10,621
3
232,010
47
A.26 Python script to calculate euclidian distances
import csv
import math
householdsfile = "houses.csv"
businessesfile = "businesses.csv"
def get-distance(house, business):
xO, yO = house['x'], house['y']
xl, yl = business['x'], business[y']
xO, yO = float(xO), float(yO)
xl, yl = float(xl), float(yl)
return math.sqrt( (xl - xO)**2 + (yl - yO)**2)
def getgravity(distance):
return 1 / distance**2
def get-distances(house, businesses):
distances = [get_distance(house, business) for business in businesses]
gravity = sum([get-gravity(d) for d in distances])
min distance = min(distances)
house['gravity'] = gravity
house['min-distance'] = min_distance
def get-rows(path):
rows = []
with open(path,'r') as f:
reader = csv.DictReader(f)
for row in reader:
rows.append(row)
return rows
def writecsv( data, path):
oneitem = data[O]
with open(path,'w') as f:
writer = csv.DictWriter(f, onejitem.keyso)
writer.writeheadero
writer.writerows(data)
def mainO:
houses = get rows(householdsfile)
businesses = get-rows(businesses-file)
for house in houses:
get-distances( house, businesses)
write csv( houses, 'calculatedhouses.csv')
maino
48
A.27 OLS mean observations by stratum
Variable
Constant
Coefficient
638,297.40
Mean observation by socioeconomic stratum
Stratum 4 Stratum 5 Stratum 6
0
0
0
0
0
0
Structural explanatory variables
211,663.90
HEIGHT_1
-1,205.00
HEIGHT_2
Stratum 1
1.51
0
Stratum 2
0
1.972331
Stratum 3
0
0
-3,359.17
0
0
2.587793
0
29,726.69
30,003.77
87,963.20
-26,728.32
-218.90
155.38
-425.11
-1,593.11
-3,477.13
-4,276.09
-4,128.41
-3,101.27
-3,614.09
2,655.98
2,152.81
4.06
5.05
1.90
2.24
-2.53
-1.33
-15,497.80
2,100,316.00
-60,459.45
25,501.69
30.50
0
0
0
2.89
0
0
0
0
0
140.43
0
0
0
0
0
28,675.67
0
0
0
0
0
13.30
0
0
0.09
15.86
0
0
0
0
6.260245
0
0
0
0
0
142.3634
0
0
0
0
0
29055.51
0
0
0
0
18.2049
0.0103335
0.0013959
0.2910477
67.70027
0
0
0
0
0
15.54354
0
0
0
0
0
129.883
0
0
0
0
0
276294
0
0
0
30.54845
0.0163451
0.0111279
0.5713532
134.45
3.814761
0
0
0
0
0
31.8559
0
0
0
0
0
91.54089
0
0
0
0
0
16841.42
0
0
19.80563
0.0695957
0.0439604
0.9050194
163.0854
0
0
0
3.439338
0 3.796304
0
0
0
0
0
0
0
0
0
22.64186
0
27.8057
0
0
0
0
0
0
0
0
0
112.9759
0 147.8416
0
0
0
0
0
0
0
0
0
19351.76
0 31979.59
16.8253 19.83921
0.1534898 0.0707862
0.0063558 0.0048618
0.953965 0.9772527
128.3246 74.44369
1
0
0
0
0
0
129.72
34.18
0.54
1.78
654.48
1,288.24
388.23
12.73
-5.48
2,940.43
198.49
0.00
3.02
3.15
252.63
-3.36
-6.27
5,355.49
956.07
-6.14
2.98
3.23
0
1
0
0
0
0
329.4261
77.387
1.786583
1.95796
559.7379
967.302
350.3918
31.42513
-5.195909
3557.904
229.2039
0.0001592
2.855164
3.094486
116.846
-3.367395
-6.340527
6123.083
591.758
-6.240274
3.003726
3.237061
0
0
1
0
0
0
222.0139
68.1225
1.251064
3.948663
574.1577
1237.044
650.5065
66.32051
-4.955442
2102.947
311.2508
0.000103
2.761356
2.675124
122.3786
-3.231209
-5.864138
2709.047
362.476
-6.097827
2.981945
2.853385
0
0
0
1
0
0
111.2139
47.22534
1.056798
6.030701
480.884
1170.826
1589.833
105.3271
-4.813632
362.7401
446.6397
0.0000454
2.700647
2.541296
133.7298
-3.445361
-5.524798
1302.343
339.2842
-5.953472
2.19967
2.526559
0
0
0
0
1
0
80.73779
29.79102
0.5262321
4.193892
527.6389
2917.123
3451.596
99.98783
-4.533123
510.8341
854.5162
0.0000455
2.582008
2.475621
170.4007
-3.391017
-5.709198
1949.166
413.5464
-6.270346
2.43783
2.746809
HEIGHT_3
HEIGHT_4
HEIGHT_5
HEIGHT_6
HEIGHTSQ_1
HEIGHTSQ_2
HEIGHTSQ_3
HEIGHTSQA
HEIGHTSQ_5
HEIGHTSQ-6
SIZE_1
SIZE _2
SIZE_3
SIZE_4
SIZE_5
SIZE_6
SIZESQ_1
SIZESQ_2
SIZESQ_3
SIZESQ_4
SIZESQ_5
SIZESQ-6
AGE
NEWSUPPLY
HISTORICAL
JOINT
UNITS
0
0
Location explanatory variables
STRATUML1
STRATUM_2
STRATUM_3
STRATUM_4
STRATUM_5
STRATUM 6
POPDENSITY
DWELLDEN
SAFETY
TRAFFIC
POLICE
BRT
CYCLING
EMPDEN
INDGRA-LOG
H_EDU
PUBSCHOOL
PRISCHOOLGRA
MALLLOG
MARKETLOG
DRUGSTORE
PARKGRALOG
SPORTGRALOG
MUSEUM
CHURCH
LIB GRA LOG
HOTEL_LOG
TOURIST LOG
624,630.10
1,013,008.00
1,894,975.00
2,270,263.00
2,892,562.00
1,561.45
-7,456.52
-105,083.60
12,968.32
-109.22
-14.19
5.05
581.90
-255,897.50
59.82
231.74
5,043,466.00
64,482.85
-156,851.00
282.47
-64,409.40
123,658.30
-82.52
-167.39
-146,921.70
-154,622.60
12,453.61
0
0
0
0
0
1
86.77878
33.44136
0.6131417
5.908802
806.1813
2138.708
1653.456
104.4576
-5.209075
702.3348
1239.875
0.0000197
2.622178
2.486818
237.9027
-3417978
-5.706513
1705.844
498.2813
-6.215311
2.323657
2.597847
49