Exploring the Use of Land Value Capture Instruments for Green Resilient Infrastructure Benefits: A Framework Applied in Cali, Colombia Working Paper WP19SG1

Abstract

Building resilient infrastructure is one of the major challenges cities face due to urbanization rates and climate change. Financing these investments is an additional challenge particularly for cities in low and middle-income countries. However, land value capture (LVC) can provide alternative and local finance sources. This study identifies and assesses the multiple benefits of a green resilient infrastructure (GRI) project including flood risk reduction and proposes land value capture instruments for green resilient infrastructure benefits, as a framework for financing public benefits and (partly) recovering the project investment.
The framework is applied on a GRI river project in Santiago de Cali, Colombia: the CAU
Cañaveralejo. It combines and tests existing methodologies, researching the possibilities of expanding the concept of LVC and applying it on GRI projects that contribute to flood risk reduction. The research is based on primary and secondary data collection (fieldwork, literature, collection of project documents) to conduct a hedonic pricing modelling combined with GIS and stakeholders’ workshop consultation. The study aims to assess the impact of GRI attributes to the land values of the project’s area. Also, it explores the feasibility of using different LVC instruments in the context of Cali.

Full text

July 2019
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© 2019 Lincoln Institute of Land Policy
Exploring the Use of Land Value Capture
Instruments for Green Resilient Infrastructure
Benefits: A Framework Applied in Cali, Colombia
Working Paper WP19SG1
Stelios Grafakos
Global Green Growth Institute
Alexandra Tsatsou
BluAct
Luca D’Acci
Politecnico di Torino
James Kostaras
Institute for International Urban Development
Adriana Lopez
Universidad del Valle
Nohemi Ramirez
RECOMS
Barbara Summers
Collaborative Media Advocacy Platform

Abstract
Building resilient infrastructure is one of the major challenges cities face due to urbanization
rates and climate change. Financing these investments is an additional challenge particularly for
cities in low and middle-income countries. However, land value capture (LVC) can provide
alternative and local finance sources. This study identifies and assesses the multiple benefits of a
green resilient infrastructure (GRI) project including flood risk reduction and proposes land
value capture instruments for green resilient infrastructure benefits, as a framework for financing
public benefits and (partly) recovering the project investment.
The framework is applied on a GRI river project in Santiago de Cali, Colombia: the CAU
Cañaveralejo. It combines and tests existing methodologies, researching the possibilities of
expanding the concept of LVC and applying it on GRI projects that contribute to flood risk
reduction. The research is based on primary and secondary data collection (fieldwork, literature,
collection of project documents) to conduct a hedonic pricing modelling combined with GIS and
stakeholders’ workshop consultation. The study aims to assess the impact of GRI attributes to the
land values of the project’s area. Also, it explores the feasibility of using different LVC
instruments in the context of Cali.
Keywords: green infrastructure benefits, land value capture, climate resilience finance, hedonic
model, Cali, Colombia.

About the Authors
Stelios Grafakos is a Principal Economist at the Global Green Growth Institute (GGGI), Office
of Thought Leadership, headquarters in Seoul, South Korea. In his current role, he is responsible
for GGGI’s work on economic issues of green growth. Prior to GGGI, Stelios worked as a senior
expert on urban Sustainability and climate change at the Institute for Housing and Urban
Development Studies (IHS), Erasmus University Rotterdam. His research interests and
experience lie towards the fields of environmental economics, environmental decision making
and analysis, ecosystems valuation, urban sustainability assessment and urban low carbon and
climate resilient development and planning. He has been leading several advisory, research and
capacity building projects in different countries around the world for clients such as the
European Commission, The World Bank, Asian Development Bank, Inter-American
Development Bank, United Nations Development Programme (UNDP), UN-HABITAT, the
Lincoln Institute of Land Policy and the Dutch government. Stelios holds a bachelor’s degree in
Economics from the Athens University of Economics and Business, a master’s degree in
Environmental Management and Policy from the University of Amsterdam and a PhD in
decision analysis and support from the Erasmus University Rotterdam. In addition, he has
published several peer-reviewed articles in academic journals, books, and international
conferences.
Contact: stelios.grafakos@gggi.org
Alexandra Tsatsou (MSc Urban Management and Development, IHS Erasmus University
Rotterdam, The Netherlands; MSc Architectural Engineering, Aristotle University of
Thessaloniki, Greece) is a researcher, consultant and trainer on urban planning and climate
change exploring the interface of stakeholder engagement, climate action and economic growth
as the axis for sustainable urban development. Her professional experience has evolved around
topics such as mainstreaming climate change in urban planning, co-creation of urban spaces
resilient to natural disasters, valuation of ecosystem services, urban vulnerability assessment,
sustainability and resilience benefits, multi-criteria analysis for climate change adaptation, port
cities and climate resilience, capacity assessment for climate change interventions. She has
worked in projects funded by the European Commission, UN-Habitat, the World Bank, C40,
GIZ, the Dutch government and public bodies. Currently she is coordinating a network of
European port cities (BluAct) that aim to reconnect with the sea and achieve blue growth through
blue economy entrepreneurship.
Contact: alex.d.tsatsou@gmail.com
Luca D’Acci is currently a Senior Research Fellow at the University of Portsmouth, a researcher
at Politecnico di Torino and at Erasmus University Rotterdam. He worked in projects for the
World Bank, Asian Development Bank, European Commission, Engineering and Physical
Sciences Research Council (EPSRC), PRIN (Projects of Relevant National Interest - Italy) and
the Lincoln Institute of Land Policy. He has been an academic and/or guest lecturer at Oxford
University, Cambridge University, ETH, University College London, Vienna Technology

University, Reading University, TU Delft, Utrecht University, Birmingham University, Trinity
College Dublin, University of Surrey, Bournemouth University, Heriot-Watt University, among
others. After almost 10 years among Brazil, Uruguay, Scotland, Australia, Spain, England,
Ireland and the Netherlands, he decided, in December 2017, to move to his mother Alpine
Region in North Italy where he is teaching and researching on valuations, sustainable
development and urban forms.
Contact: luca.dacci@polito.it
James Kostaras is a Senior Fellow and urban development expert at the Institute for
International Urban Development (I2UD) in Cambridge, Massachusetts. In his role at I2UD, he
has helped communities address the challenges of urban poverty, housing, rapid urbanization and
the climate adaptation in Belize, Haiti, Mexico, Colombia, Bolivia, Ecuador and Morocco.
Climate adaptation and resiliency through urban planning and sustainable community
development is a major focus of his work. As an urban planner and executive planning and
development director for over 25 years, he led a 65-person economic development agency and
launched major urban regeneration strategies that have attracted over $1 billion in public and
private investment in the cities of Boston and Somerville, Massachusetts. From 1998 to 2008,
Mr. Kostaras was a Lecturer and Design Critic in Urban Planning and Design at the Graduate
School of Design at Harvard University. He is a registered architect in the Massachusetts and a
former member of the American Institute of Certified Planners (AICP). He received his B.Arch.
from RISD and his Master of Architecture in Urban Design from the Harvard Graduate School
of Design.
Contact: kostaras@i2ud.org
Adriana Patricia López Valencia is currently teaching and developing academic research as a
professor at Universidad del Valle. Ms. López Valencia has carried out research in Germany at
United Nations University at the VARMAP section about urban vulnerability to natural and
technological hazards, and as part of her doctoral research she worked at the Université du
Québec à Montréal in Canada as a winner of the Emerging leaders of the Americas program
(ELAP). She worked as a consultant for the cities of Yumbo, Cerrito, Tulua and Mitu in
Colombia and for Energy Rating Services, an energy and sustainable design consulting firm in
the UK. Ms. López Valencia is currently working on projects related to risk management and
vulnerability assessment with GIS techniques and involving social perception, working with
children in the tactical urbanism school recently created at Universidad del Valle. Ms. López
Valencia is interested in holistic approaches to capturing economic, ecological and societal
components of an urban space as a basis for land use planning.
Contact: adriana.lopez@correounivalle.edu.co

Nohemi Ramirez Aranda is an architect graduated with honors from Universidad
Iberoamericana Torreon, and holds a Master of Science in Urban Management and Development
with a Specialization in Urban Environment, Sustainability and Climate Change from IHS
Erasmus Rotterdam. She is currently Ph.D. candidate and a fellow from the RECOMS - Marie
Curie programme working with Gent University, ILVO (Instituut voor Landbouw-, Visserij- en
Voedingsonderzoek) and Groningen University on public participatory GIS tools for building
resourceful and resilient community open space management. For six years, she specialized in
architecture and design working as a chief designer of a local firm back in Mexico. Parallel to
this work, she collaborated in municipal urban research and design. After her master program,
she gained experience developing tools and methods for capacity assessment and participatory
planning process with IHS Erasmus Rotterdam and GIZ (Deutsche Gesellschaft für
Internationale Zusammenarbeit). Further interests include quantitative and qualitative research,
GIS analysis, econometrics, and data visualization.
Contact: nohemi.ramirez@ilvo.vlaanderen.be
Barbara Summers is the Lead Community Planner for Collaborative Media Advocacy Platform
(CMAP). She manages and leads capacity building exercises for marginalized waterfront slum
dwellers in a participatory mapping and planning program in Port Harcourt, Nigeria. While
working at the Institute for International Urban Development (I2UD), she worked on expanding
the Institute’s work in climate change adaptation, resilience and capacity building initiatives in
emerging countries. Her work included managing a team of local officials to pilot the
Rockefeller Foundation’s City Resilience Index in Tanzania, developing case studies and best
practices on increasing community participation in land use planning and climate change
adaptation for cities in the Dominican Republic, as well as, mapping and data analysis to update
Iraq’s National Urban Spatial Plan. Previously, she worked with the non-profit Greensburg
GreenTown to facilitate sustainable disaster recovery and environmental sustainability at the
community level. She has a Master in Regional Planning form Cornell University and a B.A. in
Environmental Studies and Urban Sustainability from Bucknell University.
Contact: barbara.l.summers@gmail.com

Acknowledgments
The authors would like to acknowledge the support from the participants of the Urban
Management and Development (UMD) course of IHS Erasmus University Rotterdam, Diego
Giron Estrada and Juliana Giraldo Sanabria who conducted their master’s theses on parts of this
research and contributed to the data collection and analysis. Furthermore, the authors would like
to thank Carlos Morales from IHS for his support, feedback and insights provided to the project
team on land value capture instruments and Dr. Spiros Stavropoulos from Erasmus School of
Economics (ESE) for his valuable comments on the statistical analysis and application of the
hedonic pricing model. In addition, the authors would like to thank Melissa Warmenhoven for
editing the final report. Moreover, the authors would like to express their gratitude to Robin
Rajack from the Inter-American Development Bank (IADB) for the valuable comments he
provided to the first draft of the report during the LILP seminar in Mexico City in November of
2016. Furthermore, the authors would like to thank the Lincoln Institute of Land Policy for
granting this research and particularly LILP senior experts Enrique Silva and Martim Smolka for
their feedback and support through the whole process of the research project.

Contents
1. Introduction ..................................................................................................................................1
1.1 Problem Statement ................................................................................................................ 2
2. Background ..................................................................................................................................4
2.1 Case Study: CAU Cañaveralejo, Cali, Colombia .................................................................. 4
2.2 Case Study ............................................................................................................................. 8
The CAU Cañaveralejo Project ..............................................................................................11
The Cañaveralejo Canal ..........................................................................................................13
Socio-Economic Profile of the Study Area ............................................................................13
2.3 Colombia’s Legal Framework Enabling Land Value Capture ............................................ 14
Betterment Contribution: “Contribución de Valorización” ....................................................14
Unearned Increments: “Plusvalías” ........................................................................................14
Land-Based Regulatory Instruments Related to Land Value Capture in Colombia ...............15
2.4 Land Value Capture Instruments in Cali ............................................................................. 18
3. Literature Review .......................................................................................................................19
3.1 Urban Green Infrastructure (GI) and Multiple Benefits ...................................................... 19
Green Infrastructure for Urban Climate Adaptation and Resilience ......................................19
Typologies of Green Infrastructure ........................................................................................20
Urban GI and Ecosystem Services .........................................................................................20
Valuation of Ecosystem Services and GI Benefits .................................................................22
Damage Costs Avoided Concept ............................................................................................24
Hedonic Pricing Model (HPM) for Valuing GI and Flood Risk Reduction ...........................25
3.2 Green Resilient Infrastructure and LVC ............................................................................. 29
Financing Green Infrastructure ...............................................................................................29
LVC as a Finance Mechanism of Urban Climate Adaptation and Resilience ........................29
4. Methods and Data ......................................................................................................................31
4.1 Data Collection .................................................................................................................... 31
POT GIS Database ..................................................................................................................31
Data on Land Values ..............................................................................................................34
Data on Exposure to Flooding ................................................................................................35
4.2 Data Analysis: Hedonic Pricing Models ............................................................................. 39
5. Results ........................................................................................................................................43
5.1 HPM Models Results .......................................................................................................... 43
Number of Trees .....................................................................................................................45

Open Green Spaces and Vegetation Coverage .......................................................................45
Bike Lanes ..............................................................................................................................46
Pedestrian Streets ....................................................................................................................46
Exposure to Risk of Fluvial Flooding .....................................................................................47
5.2 Predicting the Potential Impact of the CAU Cañaveralejo on Land Value ......................... 50
Prediction Based on HPM1 ....................................................................................................50
Prediction Based on HPM2 ....................................................................................................51
Prediction Based on HPM3 ....................................................................................................53
Total Land Value Increase per GRI Variable .........................................................................54
5.3 Possible Application of Land Value Capture Instruments .................................................. 59
Feasibility Assessment of LVC Instruments ..........................................................................59
Challenges for the Implementation of LVC in the CAU Project ............................................61
6. Discussion and Concluding Remarks ........................................................................................62
6.1 Policy Recommendations .................................................................................................... 63
Institutional Challenges Regarding the Implementation of Land Value Capture ...................65
The Concept of ‘Avoided Costs’ as an Analog for LVC in the Case of Urban Climate
Resilience ................................................................................................................................66
Property Insurance as a Proxy for Land Value .......................................................................67
6.2 Concluding Remarks ........................................................................................................... 67
References ......................................................................................................................................70
Appendix A: Stakeholders’ Workshop ..........................................................................................78
Appendix B: Description of Lonja Creation ..................................................................................80
Appendix C: Damages Due to Past Flood Events in the Project Area ..........................................82
Appendix D: Insurance Survey ......................................................................................................83
Appendix E: List of Participants of Second Stakeholders’ Workshop in Cali ..............................84
Appendix F: Prediction of Land Value Increase Based on HPM2 ................................................85
Appendix G: Prediction of Land Value Decrease Based on HPM3 ..............................................89

1
Exploring the Use of Land Value Capture Instruments for Green Resilient
Infrastructure Benefits: A Framework Applied in Cali, Colombia
1. Introduction
Natural disasters such as floods are becoming more frequent and intense due to climate change,
while the process of urbanization, often characterized by unsustainable patterns of development,
increases the challenges faced by the population living in flood risk-prone urban areas. In most
Latin American cities, where there are high contrasts in development between different
urbanized areas, the poorest population is generally the most vulnerable to the effects of natural
disasters, given their lack of access to infrastructure and resources. Additionally, the
phenomenon of “La Niña” and changes in rainfall patterns due to climate change and climate
variability in recent years result in even greater impacts in these vulnerable areas.
The process of urbanization, including urban interventions for flood mitigation, modifies the
habitats of different species and cuts ecological connections present in environmental corridors.
Urbanization has meant a loss of native vegetation and, subsequently, an increase of impervious
surfaces, whereby surface runoff increases significantly, leading to an increased risk of flooding.
This disrupts the natural ecosystem cycles of the rivers and compromises landscape quality. The
reduction of vegetation cover required for those urban "gray infrastructure" interventions
interrupts the hydrological cycle of assimilation, infiltration and evapotranspiration. In the
process of building gray infrastructure, part of the vegetation cover is removed and the subsoil is
compacted, reducing the amount of water that can infiltrate, thereby greatly increasing the speed
with which the water runs off on the urban surfaces.
Climate change is expected to lead to changes in the frequency, intensity, duration, and timing of
extreme weather and climate events, leading to unprecedented climate-induced disasters (IPCC
2012). It will potentially magnify the existing patterns of climate-induced disaster risks and exert
further pressure on the capacities of governments and different actors to respond. UN-Habitat
(2011) stated that unsustainable and unplanned urban development can bring increased
vulnerability to climate hazards. Many cities are facing rapid growth due to urbanization, which
results in the creation of informal settlements that are often the most vulnerable to natural
disasters. One of the biggest challenges for national and local governments globally, particularly
for low- and middle-income countries, is financing climate change adaptation and resilience
interventions.
Urbanization changes the physical environment, with significant effects resulting in accelerated
densification, decrease in the capacity of soil infiltration, increased runoff retention times, and
increased impermeability of surfaces. This reduces the hydraulic capacity of drainage sectors,
which already face high volumes in rainy periods. This is the case of the south drainage system
in the city of Santiago de Cali in Colombia (referred to hereafter as Cali), constituted by the
rivers Cañaveralejo, Melendez, and Lili, which end up in the Cauca River, as well as various
artificial canals flowing into all the rivers. This area is characterized by high rates of
urbanization, and faces problems associated with flooding from rivers and canals, erosion of

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watersheds, mismanagement of solid waste, and limitations of the gray infrastructure built for
mitigation of flood hazards. All these issues are directly related to the capabilities and
understanding of the processes of urban planning and management, derived from a poor
coordination with technicians and professionals responsible for interventions, who, in turn, do
not contemplate visions and particularities of the context in which the projects are carried out.
In the future, climate change induced disasters, including floods, are expected to occur more
frequently and with more intensity, as the challenges mentioned above are exacerbated by the
impacts of climate change.
1.1 Problem Statement
Green Infrastructure (GI) is becoming a promising strategy to adapt to the impacts of climate
change, to enhance urban climate resilience, and more specifically to reduce flood risk, while
simultaneously delivering other sustainability benefits. Green corridors for flood management,
restoration of natural floodplains, and multifunctional public space for recreation and stormwater
management, all combine risk reduction attributes with multiple sustainability benefits (Brugman
2011; CCAP 2011; Grafakos et al. 2016). GI that delivers benefits related to both sustainability
and flood reduction enhances urban climate resilience.
Financing urban climate resilience is seen as a critical challenge for both now and the coming
years, particularly in low and middle-income countries, due to their constrained municipal
budgets, as well as the insufficient amounts mobilized through international funds. The impact of
floods on land and real estate values is receiving increased attention (Ingram and Hong 2011;
Pryce et al. 2011; Koning et al. 2016). However, the impact of risk reduction on land values due
to GI, especially as a ratio of the impact of the green components of the project, needs further
research. Combining the field of climate change adaptation and resilience with studies on land
policy and finance, in order to explore financing resilience through Land Value Capture (LVC),
is a new and promising concept to explore.
In Latin America and the Caribbean, LVC has already been an effective tool for municipal
governments to finance infrastructure (Smolka 2013), especially in cases where conventional
public funding is often constrained. The same mechanisms could be used to finance resilience
projects, including GRI investments. However, research and practical applications remain
limited. This argument is supported by an option for identifying the value (costs) of the GRI
intervention, in addition to the option of calculating the increment of land value added due to the
GRI project.
In order to explore the potential of using LVC for GRI investments, the GRI benefits that impact
land values should be identified, quantified and valued. The impact from this range of benefits
on surrounding land and property values has been also discussed and confirmed in literature
(Madison and Covari 2013). Connecting GI benefits to urban climate resilience could provide
sufficient evidence to support the financing of GRI projects through land-based financing
methods such as LVC (Grafakos et al. 2016; Piriani and Tolkoff 2014), and therefore provide
local governments with additional alternatives for financing urban climate change adaptation and
resilience.

3
This research aims to provide evidence of the impact of GRI on land values in Cali and explores
the impact of a GRI project on land values. The findings are context-specific, based on a
Hedonic Pricing Model (HPM) study for Cali. Although they can provide indications on how
GRI would affect land values in other cities, the results cannot be directly replicated. However,
the methods and proposed framework could be utilized in order to conduct a similar analysis in
other cities.
Moreover, the study builds on the framework suggested by James Kostaras (2015) for the use of
LVC instruments for different types of GI. It extends this framework to explore the LVC
instruments in relation to the benefits accrued by GI interventions that involve flood risk
reduction, which are in this study defined as GRI interventions. In our study we analyze these
benefits, with the addition of flood risk reduction, anticipating that land values will rise and that
the increment can be captured to fund/finance further investment on urban resilience.
This study combines prior research and empirical knowledge resulting from the analysis of a
planned GRI municipal project in the city of Cali. The project is part of the “Corredores
Ambientales Urbanos” (translated as Environmental Urban Corridors, hereafter CAU), in order
to construct a framework of “LVC instruments for GRI benefits,” which is able to assess the
multiple benefits of GRI projects in addition to risk reduction. Additionally, the study explores
the feasibility of LVC as an GRI financing mechanism, stemming from these benefits.
The application of the framework in Cali aims to address the following research objectives:
● Identify the multiple benefits of the GRI intervention, including GI/ecosystem services
related benefits and flood risk reduction benefits.
● Assess the impact of selected benefits such as flood risk reduction and other additional GI
intervention benefits on land values.
● Explore which LVC instruments can be used to capture the added value due to flood risk
reduction and the other benefits of GRI project.
● Assess the feasibility of Colombia’s LVC instruments as a source of financing GRI
projects in the context of Cali.
The current research contributes to the following fields and debates:
● The research bridges different policy fields such as flood risk reduction management,
climate change adaptation finance and land policy. Using LVC as a mechanism for
financing GI for climate change adaptation and resilience is a relatively new approach,
which could trigger further discussion at both research and policy levels.
● Impacts of resilience improvements and GI projects on land value are lesser known and
largely undocumented in Cali and other cities in Latin America. It is an open question
whether land values in these cities experience these dynamics. In Colombia in particular,
there isn’t any study applying an HPM to value GI benefits and flood risk reduction.
● The use of LVC as a climate adaptation and resilience finance instrument has hardly been
explored in the academic literature and in practice.

4
Different stakeholders and audiences, including the local government of the city of Cali (and
other local governments in Latin America and the Caribbean), can use the outcomes of this
research in the context of flood management and GRI financing. Additionally, the research could
be useful for the national government dealing with urban climate resilience interventions, while
exploring ways to finance them.
Figure 1: Conceptual Framework
2. Background
2.1 Case Study: CAU Cañaveralejo, Cali, Colombia
Cali is situated in the Cauca River valley, 300km southwest of the capital Bogota and
approximately 1000m above sea level. It is considered the main urban, economic, industrial and
agricultural center of the southwestern part of Colombia. Spanning 560km,2 Cali is Colombia’s
second largest city by area, after Bogotá. The population is estimated at 2.3 million residents,
making it the most populous city in this region and the third largest metropolitan area by
population in Colombia, after Bogotá and Medellín.
Due to the city’s location next to the port of Buenaventura, the only Colombian port with access
to the Pacific Ocean, Cali is considered Colombia’s “gate to the Pacific”. This location has
contributed to the city’s economic development. In Cali’s early history, its major economic
activities were based on agricultural production, including coffee and sugar production (Vásquez
Benítez 1990). This was possible due to the region’s hydric richness, which provided some of the
most fertile and productive land in the country. In addition to the biggest river Cauca, which runs

5
through the valley, six smaller rivers (Aguacatal, Cali, Cañaveralejo, Melendez, Lili and Pance)
flow through Cali, known as “the city of seven rivers”. The nearby cluster of hills, the
‘Farallones’, separate Cali from the Pacific coast and give rise to the six rivers that flow through
the city towards the Cauca river, on the city’s east edge. Due to this, another common name for
the Cali is “the city between the hills and the river”.
The growth of the city is closely related to the strong dynamics created by infrastructure like the
new railroad line (1944–1958), and its proximity to the port. The new railway line also changed
the population dynamics of the city internally, by setting a spatial barrier between the east and
the west parts. The “two Calis” have since developed at different paces and is identifiable on
maps of the city, such as the stratification map; the eastern part of Cali located next to the Cauca
River is overall less developed when compared to the west side of the city, where the city center
is located. With the industrial “boom” that occurred in the mid-1900s, a major migration pattern
to the city was generated and this urbanization has continued to the present day, accelerating the
city’s growth rate (Institut de Recherche et débat sur la Gouvernance 2013). In 1938, Cali was a
city of about 100,000 inhabitants; in less than 100 years, the city grew to be 24 times bigger.
This rapid expansion and urbanization led to the creation of settlements on the flood and
landslide prone areas around the rivers Cauca, Cali and Cañaveralejo River (Benítez 2001).
Although several interventions such as dams, dikes, canals and pumping plants were
implemented at all seven rivers between 1960 and 1980, they have in fact exacerbated flood
hazards, due to the lack of maintenance and the degradation of the natural ecosystems
(Velásquez 2011), leading to additional environmental and economic issues.
Climate change in Cali
According to the Global Water Partnership, South America is one of the richest regions in water
resources, with 28% of the freshwater resources of the world, and three of the largest river
basins; Amazonas, Orinoco and Rio de la Plata. Two of those basins (Amazonas and Orinoco)
are partially located in Colombia, a country with around 737.000 bodies of water (Campuzano et
al. 2012). In many Colombian cities, like Cali, rivers and streams are affecting urban living
conditions. Combined with urban challenges related to poverty and inequality, environment and
climate change, urban management and planning, rivers become drivers for flood risk and
vulnerability. In the past 40 years, 73% of losses and damages to housing in Colombia have been
attributed to flood events.
Cali, the “city of the seven rivers” is a great example of a city where the challenges of
responding to flood risk and the impacts from existing environmental conditions along the rivers
are eminent. According to an OSSO Corporation report (2011), from all disaster events
registered between 1970 and 2011, Cali has been most impacted by flooding events, which sum
up to 25% of the total events registered. As the city is experiencing an increase in the frequency
and strength of its rainy seasons, it faces the challenge of absorbing such shocks while
maintaining its ability to function (MacKinnon 2015), especially after events such as torrential
rainfalls. Recently, during the 2010–2011 rainy season, Colombia experienced periods of heavy
rain caused by the La Niña phenomenon. The Cauca Valley region ranked second in the number
of hazard events registered in that period (Comisión Económica para América Latina y el Caribe

6
[Cepal] 2012).
The spatial distribution of recorded disasters in Cali indicates that the events affect mostly poorer
neighborhoods of the city, which are built on dense and risky areas, such as hillsides with steep
slopes or low ground exposed to overruns of channels. However, middle income areas are also
increasingly affected. The damages have raised awareness of climate issues and, in recent years,
the city is aims to increase its resilience and tackling climate change impacts. Various strategies
approaches and disciplines of social sciences are attempting to deepen the study of the
relationship between society and nature and the impact of deterioration of green open space due
to urbanization.
Cali’s 2014 land use plan (Plan Ordenamiento Territorial; hereafter POT) put forward goals for
restoring the seven rivers, the Farallones and recovering water bodies in the city. According to
the POT, the municipality of Cali identifies that the ecosystem base is made up of “the elements
of the natural system that interrelate and govern essential ecological processes like: ecosystems,
geology, geomorphology, climate, biodiversity and water systems, and they define the strategic
determinants that condition land use, location of human settlements and morphology” (Concejo
2014, 56). The plan intends to take measures against further river deterioration, thus improving
the green corridors and supplying them with different facilities and high quality green public
spaces to improve citizens’ well-being. It highlights the specific relevance of upgrading one of
the seven rivers, river Cañaveralejo, and its potential for public space along the area.

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Image 1: The Seven Rivers of Cali. The biggest river, Cauca, is represented by the blue line.
Source: CVC-CITCE Univalle 2013
Of Cali’s seven rivers, the Cañaveralejo river at the south of the city is an example of the
deterioration that other rivers have also experienced. One of the most dramatic interventions on
the river was its canalization back in the 1950s to prevent flood risk and promote urbanization
along its banks in the city center. The riverbed changed and the natural elements on the river’s
edges were eliminated, causing the loss of environmental identity and replacing the natural
elements with a concrete bank. However, as the drainage and the infrastructure supporting the
new canal in these areas was not adequate, the areas along the canal soon started experiencing
flooding issues. In order to deal with this situation, a dam was constructed in the 1980s between
the natural part of the river and the canalized part to retain the water from entering the urban grid
through the canal and reduce flooding in the rainy seasons on the lower area of the river. The
intervention succeeded in reducing the water flow from the river toward the canal but failed at
several other aspects: flooding at the blocked river part where the more vulnerable groups reside,
environmental deterioration such as garbage accumulation, managing water runoff from hard
urban surfaces, inflow at the intersections with other canals that are part of the south drainage
system, and illegal sewage disposal in the river.
Nowadays, the environmental quality of the Cañaveralejo river corridor is low because of more

8
than 1000 illegal sewage connections discharging into the river (El País 2014), waste disposal,
residual mercury from illegal gold mining on the Farallones, discharge from pig farms, coal
mining and many other industries (Institut de Recherche et débat sur la Gouvernance 2013), and
the deforestation of its basin. These issues, in addition to the risk of flooding, conceal the
potential of this natural resource, and its value for the communities that depend on it for their
livelihoods and recreation.
2.2 Case Study
This study will focus on a specific part of the Cañaveralejo river and canal, analyzing the CAU
Cañaveralejo, a project planned (and in 2017, partly implemented) by the municipality of Cali
and local agencies, aiming to restore the Cañaveralejo river area, and part of the canal that
replaced the natural bank of the river in the urban grid of Cali (thereby changing its direction).
The case study was selected due to the area’s characteristics, including its proximity to mixed
income neighborhoods, the combination of a green and a gray part of Cañaveralejo “river,” a
combination of planned and implemented project phases, and its proximity to housing areas with
different densities, spatial attributes and social diversity. The multiple benefits (social,
environmental, economic) of the project and the added value they bring to the area make the case
promising for research on public policies and LVC instruments in relation to environmental
improvements and risk reduction. CAU Cañaveralejo is an example of an environmental urban
project that can restore ecological connectivity and upgrade environmental quality, while
simultaneously presenting opportunities for the partial recovery of the project investment (or
capital for other public investments).

9
Image 2: Overall Study Area.
Source: Authors
Image 3: Case Study Area of CAU Cañaveralejo Intervention and Cañaveralejo Canal.
Source: Authors

10
Image 4: CAU Cañaveralejo Design (River Part, Zones 1–3 in Image 3 Above).
Source: CVC/CEDING/CUNA 2014
Image 5: CAU Cañaveralejo Design (Case Study Area’s River Part, Zone 4 in Image 3
Above).
Source: CVC/CEDING/CUNA 2014

11
Image 6: Cañaveralejo Canal (Case Study Area’s Canal Part, Zone 5 from Image 3 Above).
Source: El País
The CAU Cañaveralejo Project
The CAU Cañaveralejo project concerns the part of the case study area where the river is still in
its natural form. It is overseen by Corporación Autónoma Regional del Valle del Cauca (CVC),
an entity responsible for managing the natural resources and environment of the Cauca Valley
and the main consultancy groups working on the project: CEDING S.A.S and CUNA S.A.S.,
who mainly focus on architecture and engineering-related services. The project is part of Cali’s
Corredores Ambientales Urbanos (translated to Environmental Urban Corridors) program, which
is included in the aforementioned land use plan (POT) of 2014. In that POT, the Environmental
Urban Corridors (CAU) are defined as natural systems that form a network intertwined with the
rivers and the canals of the city. The corridors aim to improve the physical space surrounding the
rivers in Cali, recompose the municipality’s ecosystems and ensure compatibility between
hydrological systems and the built environment. However, the important general objective of the
CAU program is to encourage the development of urban systems and natural elements through
empowering communities and strengthening the dynamics of human activities in urban zones
close to Cali’s rivers, whilst ensuring the sustainability of protected areas. The components of the
Cañaveralejo river environmental corridor project aim to build urban resilience by strengthening
the presence of natural systems in the urban grid, thereby minimizing flood risk and increasing
the amount and quality of public spaces in the impact area. The overall strategy aims to trigger
better social conditions in the neighborhoods around Cañaveralejo, as a result of the
aforementioned objectives.

12
Ecosystems connectivity
• Reconnection and revaluation of the ecological corridor through the landscape, ecological
restoration, reforestation and increase in wildlife.
• Smooth transition between the built and the natural environment.
• Promotion of the permanence of protected species that inhabit the areas near the river
• Conservation of different habitats in the urban context.
Integrated rainwater management
• Minimum use of hard surfaces, promoting the use of permeable materials that allow
infiltration and continuity of the water cycle.
• Introduction of sustainable urban drainage systems to mitigate flood risk.
Citizen interaction and education
• Restoration of public access to the river to reverse the status of "urban barrier" and
integrate it with the city, offering opportunities for coexistence and social control.
• Introduction of low impact mobility systems across the urban rivers to increase
accessibility.
• Integration of educational, recreational and cultural facilities in order to improve quality
of life of inhabitants.
• Promotion of productive activities, urban farming and management of night-lighting to
ensure safety.
• Creation of citizens’ awareness regarding the status and protection of the rivers and the
city’s natural resources.
Image 7: Interventions Proposed at the River Part.
Source: CVC/CEDING/CUNA 2014

13
The Cañaveralejo Canal
The canal section is a rainwater and wastewater canal which directs water to Cauca’s river after
treatment and is the portion of the corridor that receives the most relevant flooding impacts. The
canal is associated with environmental problems related to waste management, as the waste
collection plant holds a large amount of waste. In addition to the positive environmental impact
(avoiding waste disposal on the canals and preventing floods), this project also has an
educational impact, due to its visible components.
The aim of the planned project led by EMCALI in the canal section, is to enhance the hydraulic
capacity of the canal to avoid flood risk at the street intersections. This part of the project is
directed more toward conventional interventions with gray infrastructure, which include the
canal expansion with new concrete retaining walls, making the area look more like a rainwater
canal than a river.
Socio-Economic Profile of the Study Area
The city of Cali is organized into 22 communes and 355 neighborhoods. On a property scale,
strata (based on socioeconomic input) are assigned to each building, in accordance with
Colombian tax law. The main purpose of this differentiation is to be able to charge public
services, allocate subsidies and collect taxes per stratum, so that the social groups with higher
economic capacity (therefore higher stratum) contribute more to public costs and subsidies than
those in lower strata.
The selected study area along Cañaveralejo extends along four communes (10, 17, 19 and 20)
and is represented mainly by properties of stratum 3, although all strata are represented (6%
stratum 1, 10% stratum 2, 47% stratum 3, 10% stratum 4, 19% stratum 5, 1% stratum 6, 6%
municipal land or non-classified land). We can observe social inequality spatially, indicated by
stratification, with low strata adjacent to very high strata (POT 2014). The Universidad de Valle
and the Institute for Housing and Urban Development Studies (IHS) did fieldwork and
conducted demographic surveys in the area in October 2015 and July 2016. According to the
results of these surveys, most of the respondents were born in Cali and represent mainly a
mestizo ethnic composition, followed by white population. Most of the surveyed population
categorize their current employment status as ‘independent” or “cuentapropismo” (self-
employed), while there are specific neighborhoods with a predominantly retired population.
Most of the surveyed population have close family to turn to in an emergency. Sometimes that
family lives in the same neighborhood, or in other neighborhoods also located on the
Cañaveralejo river or canal, which, in case of disaster, could determine the responsiveness of that
population and their capacity to cope. The majority of the surveyed population in the middle and
lower-income areas have basic primary and secondary education, while one third of the overall
population are tertiary educated.
Regarding income, half of respondents indicated that they earn 1–2 minimum wages per month.
The majority of the population live in a house (not an apartment or gated community), while
67% are homeowners.

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2.3 Colombia’s Legal Framework Enabling Land Value Capture
Land value capture refers to the public sector recovery of the land value increments or unearned
income generated by actions other than the landowner’s direct investments. The goal is to obtain
land value increments generated by actions other than the property owner’s, such as public
investments in infrastructure or administrative changes in zoning, land use and building
regulations, for the benefit of the public interest (Smolka 2013). By definition, land value capture
is a municipal finance mechanism through which local governments generate increases in land
values by making regulatory decisions, such as changes in land use or maximum allowable
density, as well as infrastructure and transit investments, and then use the incremental land value
generated by those actions to finance infrastructure or other public benefits. Investments in
public transit and transit-oriented development and, by extension, resiliency measures including
the expansion of green infrastructure, could be examples of this.
Colombia is recognized in Latin America and around the world as a pioneer in developing a legal
framework for the use of land value capture. Since the early 20th century, the country has had
some form of levy to finance public investments that have a broader impact on the municipality.
Betterment Contribution: “Contribución de Valorización”
The earliest form, known as contribution of valorization, or betterment contribution, was
introduced through legislation in 1921. Contribution of valorization is a type of special
assessment or betterment tax that finances the cost of a public works project by creating a
proportional levy on all those who benefit from the project. In 1956, contribution levies were
expanded in the legal system to include ‘contribution for appreciation for general benefit,’ which
is based on the concept that some public investments have an impact on all of a municipality’s
inhabitants. Therefore, a contribution can be imposed on all residents, based on the proportion of
the estimated benefit to their property.
National legislation established the present framework for contribution levies in 1966 through
Decree 1604 and municipal code (Decree 1333) of 1986. Under this framework, municipal
governments can apply a valorization levy at the cost of the public works plus 30%, divided
among affected properties in proportion to the benefit they receive (de Botero and Smolka 2000).
This levy can take effect before, during, or after construction to recover the cost of the project.
Over the years, the contribution of valorization has been a major source of local funding in cities
throughout Colombia. In 1968, at the height of its use, contribution levies were responsible for
45% of all local public expenditures in Medellín; 30% of Cali’s expenditures in the early 1980s;
and in 1993, 24% of Bogotá’s local revenues (Smolka 2013).
Unearned Increments: “Plusvalías”
Building on the success of contribution levies, in 1997 Colombia introduced Law 388, which
lays out the legislative framework to implement land value capture mechanisms. The law is
centered on a decree that all municipalities must design and approve a twelve-year master plan
(Plan de Ordenamiento Territorial [POT]). To support the POT, the law introduced 17 additional
planning instruments, including the establishment of “plusvalías” or land value increments,

15
which is defined as the main financing instrument for the POT. Specifically, the law cites that
municipalities are required to capture 30% to 50% of the increased value from changes in
density, or land use, in the POT.
Law 388 outlines the ground rules for applying plusvalías to finance public investments. The law
established a methodology for calculating the land value capture, using land prices tracked by the
Institute of Geography in towns, and using the municipal cadaster in cities. This methodology
was later revised by Decree 1788 in 2004. One of the key updates in the legislation was the shift
from using the cadaster as the tax base for property values to the consumer price index (CPI).
The primary reasoning for this update is that the CPI more accurately reflects changes in the
market and therefore generally produces a higher tax base, while the cadaster may be slower in
capturing these changes.,. According to the legislation, the tax base is the estimated difference
between the commercial value of property before and after the urban intervention. The tax rate is
then calculated as between 30 to 50% of the tax base (de Botero and Smolka 2000).
Furthermore, the legislation stipulates that the tax rate must reflect socio-economic conditions of
owners. For example, a neighborhood is scored according to its access to urban infrastructure
and services, as well as the stratum or socioeconomic attributes of the occupants. The conditions
for applying plusvalías are broadly defined as the conversion of rural to urban land, increase in
density, or changes in zoning or the rate of occupation in the POT. In comparison to contribution
levies, plusvalías are a true land value capture instrument in that it is an income generating
mechanism based on the impact a project has on property values, while contribution of
valorization, on the other hand, is a cost recovery mechanism.
Despite the national legislation permitting and establishing land value capture as a legal
instrument, few municipalities have implemented plusvalías, the exceptions being Medellín,
Bogotá and Barranquilla, which have implemented it with some success. Although national
legislation has established land value capture and made it available to municipal governments,
each city’s municipal council must accept it. In Bogotá, for example, the city council did not
approve plusvalías until 2003. This was in part due to scrutiny over the legal and technical
components, as well as the fact that the most recent master plan was enacted in 2000; there was
uncertainty on whether to retroactive levies should be applied. Despite the delayed application of
land value capture, between 2004 and 2007 over $16.5 million USD were levied in Bogotá
(Acosta 2008).
As evidenced by Bogotá’s experience, municipalities are faced with a significant political hurdle
relating to the complexity of land value capture, conceptually and in terms of implementation,
which is difficult to explain to voters. In place of land value capture, mayors typically try to
implement valorization taxes, which are easier for voters to understand and have a short-term
impact, while land value capture tends to have a longer-term impact.
Land-Based Regulatory Instruments Related to Land Value Capture in Colombia
Colombia’s national legislation offers local governments a broad range of land-based tools and
regulatory instruments. These instruments have been used in several cities in Colombia as

16
mechanisms through which to implement plusvalías, valorization, and other land value capture
programs. Although national legislation permits and establishes the legal instrument of land
value capture, there has been minimal implementation of land value capture other than
valorization in Cali.
Land use plan and maximum building envelope: “Aportes por edificabilidad”
It is useful to consider how the municipal government of Cali employs land policy and
regulatory instruments available under national legislation. As in most large cities in Colombia,
the land use plan formally regulates construction and new development; although with growth
and expansion, much development is informal and, consequently, not in conformance with the
adopted land use plan. In Cali, the municipal government utilizes an “index” (similar to a floor
area ratio) to determine the maximum allowable squared meters of construction allowable on a
given plot; in addition, the maximum building height is a function of the squared meters of a
plot. This constitutes a ‘base construction index,’ a basic allowable envelope of squared meters
per plot, further shaped by maximum height limits. Under this zoning system, a provision allows
additional density (up to a prescribed limit) above the ‘base construction index,’ the ‘top
construction index’ to be awarded to builders and developers in exchange for providing
amenities, such as public open space. The base construction index plus the density bonus equal
the ‘maximum allowable building envelope.’
Plan parcial
Law 9 in Colombia’s 1989 national legislation provides a land development instrument that has
been used in Cali, known as “plan parcial”. Included in this plan is a provision for land assembly
through direct acquisition or expropriation, and the readjustment of plots after the construction of
infrastructure and services (Smolka 2013). Using this provision in the plan parcial, government
agencies act as facilitators of private sector social housing construction.
Typically, urban development agencies, such as Metrovivienda, buy and service land through
readjustment of plots, and then sell the land to private social housing builders. Law 388 of 1997
later mandated land readjustment for the purpose of achieving a higher quality site design,
including better property configurations, more efficient street layouts, and a more equitable
redistribution of benefits and costs (Smolka 2013). In many plan parcial projects, a substantial
percentage of land is designated for environmental protection, although the study team could not
identify examples where high-density residential planes parciales included the explicit
investment in green infrastructure or open green space buffers intended for flood risk reduction.
“Planes parciales” are similar to planned unit developments (PUDs) and planned development
areas (PDAs) in the US, in that they function as overlay districts to the baseline land use plan and
supersede underlying zoning provisions. Within a plan parcial, public agencies and/or private
land developers have the flexibility to adjust comprehensive city guidelines to local site
conditions (Smolka 2013) and enforce good urban design, land use, and a just redistribution of
benefits and costs in land readjustment.

17
There are three categories of planes parciales:
1. Plan parcial for urban development. Development using planes parciales has
characterized Cali’s southern expansion. The minimum requirements to create a plan
parcial are four city blocks; although, in many cases, planes parciales encompass as much
as 800 hectares (in the case of the Operación Urbanística Nuevo Usme [OUNU] project
in Bogotá).
2. Plan parcial for the regulation of development in river corridors known as POMCA
(Planes de Ordenación y Manejo de Cuencas Hidrográficas). The POMCA is a
jurisdictional instrument to manage water resources and prevent the environmental
deterioration of the watershed. The Ministry of Environment and Sustainability oversees
the implementation of POMCAs, with the stated objective of balancing social and
physical development with the protection of the environment.
3. A macro-project (“mega obra”), a type of plan parcial, is a zoning and land use
instrument used to allow projects at greater densities than allowed in the city land use
plan but controlled through design guidelines and other criteria. A plan parcial functions
in a similar way as overlay districts and Planned Unit Developments (PUDs) or Planned
Development Areas in US cities. In a macro-project, a provision known as “aportes por
edificabilidad” (‘contributions for buildability’ or ‘linkage fees’ in the US) is available to
developers. Under this provision, a real estate developer is allowed to increase building
height (beyond the maximum height limits established in the land use plan) in exchange
for payment of “aportes por edificabilidad”. In Cali, “aportes por edificabilidad” pay for
public green open space in macro-projects.
Transfer of development rights (TDR)
Colombia’s national Law 388 establishes the transfer of development rights (TDR) as a legal
instrument for development. TDR is a type of land value capture that, in effect, exchanges cash
or in-kind exactions and other types of charges, for the use or transfer of building rights to
predefined receiving areas. Smolka (2013, 42) defines TDR as “a certificate by which the city
administration compensates an owner in-kind for constraints on building rights imposed on the
property (e.g. historical preservation or environmental conservation), or when the owner
surrenders some of his land for a public interest project such as widening a road, creating a park,
or rehabilitating a slum.” Additional planning instruments related to value capture introduced in
Law 388 include provisions for the public auction of unutilized land to be used for social
housing, the right of the public to have the first option to buy the land, the public acquisition of
land at prices listed before the announcement of the project, and the enabling of land
readjustment in partial plans.
The city of Cali has not implemented TDR, although these could be useful as an incentive-based
system in guiding development away from risky areas. Cali could employ TDR to protect green
buffer zones, restrict construction in high-risk areas prone to landslides and floods or, as an
incentive, to guide growth away from vulnerable areas into more resilient districts. The
municipal government, or other designated agencies, might stipulate in-kind or monetary
compensation from developers to restore and/or expand floodplains, open space for storm
management and multi-functional parks and recreation spaces to manage stormwater drainage on

18
a project site, or in close proximity. TDR, in the case of Cali, could be instrumental in limiting
development in high-risk areas or protecting sensitive wetlands near the Cañaveralejo river, the
Melendez and Lili rivers’ corridors, which constitute the city’s southern drainage system. As
such, TDR could be an incentive to guide development into non-risk, more resilient areas in lieu
of down-zoning and limits to development rights. TDR, used in coordination with POMCA
(Planes de Ordenación y Manejo de Cuencas Hidrográficas), could be a useful tool to guide
development away from hazard buffer zones in the Cañaveralejo river corridor, toward high-
density development in low-risk areas in the southern expansion area of Cali, the zone with the
highest levels of urbanization in the city. Cali’s land use plan, hypothetically, could allow the
transfer of development rights from areas deemed at-risk or optimal for constructed wetlands,
open space for storm management, and other resiliency projects.
2.4 Land Value Capture Instruments in Cali
Cali has been a leader in implementing valorization levies in Colombia but has experienced
issues of corruption that have reduced the willingness of the population to pay for public
investments through taxation and hinders the implementation of LVC strategies.
In the early 1980s, the city received upwards of 30% of revenues from contribution levies; but by
the end of the decade, the share of valorization in municipal revenues fell to 8.9% (Peterson
2009). Then in the mid-1990s, during the administration of Mayor Mauricio Guzman Cuevas, a
public works department was established to implement valorization. Under Cuevas’ leadership,
Cali successfully built and financed several bridges and a number of parks. In 1997, Guzman
was removed from office due to political scandals involving campaign contributions from narco-
cartels. As political “collateral damage,” the concept of land value capture was tainted by
association (Pretel 2016). In 2008, in response to a declining capital budget and aging
infrastructure, Cali turned back to valorization. The then-mayor proposed constructing 21 large-
scale projects (mega obras), primarily focused on road and bridge improvements, over a three-
year period financed through valorization. Eight years later only nine of the 21 projects have
been built, and an insufficient amount of money has been collected to finance subsequent
projects.
To date, despite some success in Bogotá and other cities, plusvalías have not been implemented
in Cali. There has been some research on the potential to implement land value capture
mechanisms on future projects, including the proposed Corridor Verde project, a 22 km green
corridor running the length of city with light rail, bicycle paths, as well as market space. The
study focuses on the impact of the project on land values of adjacent properties, through several
different growth scenarios, including changes in zoning in regard to construction potential.

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3. Literature Review
3.1 Urban Green Infrastructure1 (GI) and Multiple Benefits
Green Infrastructure for Urban Climate Adaptation and Resilience
Many of the multiple GI benefits and ecosystem services that have been identified in recent
studies (Byrne et al. 2015; Demuzere et al. 2014; Foster et al. 2011) are being associated with
climate change adaptation and resilience. Ecosystem services are key components to building
urban resilience and reducing vulnerability in the form of ecosystem-based adaptation. The
contribution of ecosystem services in generating more flexible (with regard to shocks) cities is
known as ‘insurance value’ (Gómez-Baggethun and Barton 2013). Insurance value reflects “the
maintenance of ecosystem service benefits despite variability, disturbance and management
uncertainty” (McPhearson et al. 2014). Ecosystem services promote resilience by responding to a
particular disruption, such as urban vegetation that reduces surface runoff and binds soil, thus
reducing the probability of damages by flooding and landslides, as well as buffering health
impacts. Insurance values produce an intrinsic economic value to ecosystem services, as the
changes caused by shocks are costly to reverse, if possible at all (Walker et al. 2010).
Moreover, GI preservation and protection can also be considered an urban strategy to limit
populations returning to risky areas (Byrne et al. 2015) after relocation processes. The strategic
planning and implementation of GI is presented as an efficient and effective strategy to reduce
the need for gray infrastructure, therefore freeing up public funds for other community needs
(Benedict and McMahon 2001).
The Nature Conservation (2014) summarizes the reasons why GI is emerging as a very
promising strategy for climate change adaptation and building urban resilience:
• Significantly lower construction costs in comparison to gray infrastructure.
• Ability to reduce climate risks when designed and managed accordingly.
• Multiple environmental, social, and economic benefits that can support the city in
building resilience.
• Diversity of options that result from the local geographical and topographical context
(and therefore fit to every different circumstance).
• The value they have for the community, which leads to strong political support and can
be the entry point to significant opportunities for financing GI with resilience attributes.
However, the application of GI solutions for urban climate adaptation and resilience purposes is
not established yet as a widespread idea. Common reasons for that are the doubts about its
efficacy in comparison to gray infrastructure, the uncertainty regarding its implementation and
maintenance, and even the identification and embrace of the concept of GI per se.
1 In this research GI is used as a concept that is focused in those elements included in the GI project CAU
Cañaveralejo, and not in all that could be described under the definition of GI.

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Typologies of Green Infrastructure
In literature, there are many definitions of what “green infrastructure” consists of. Starting from
gardens, parks, forests, canals and wetlands as merely natural assets or recreational amenities,
green infrastructure has developed to a complex and promising tool in environmental,
sustainability and climate mitigation/adaptation strategies (Foster et al. 2011; Demuzere et al.
2014). A decisive step in this conceptual transition was the reevaluation of the public park as an
indispensable asset of the urban infrastructure network, instead of simply a recreational amenity
(Rosenberg 1996). The rationale behind this reconsideration was based on the reevaluation of the
services and benefits of public parks, which were comparable to those provided by infrastructural
investments. However, the term “green infrastructure” has expanded to include different kinds of
green, blue, permeable, natural and engineered elements. Although there are many definitions of
GI, each of them with its own perspective, for this research we use a definition from Matthews et
al. (2015); according to whom GI can be defined as “the biological resources in urban areas that
are human-modified and primarily serve an overt ecological function” and which are
“intentionally designed and deployed primarily for widespread public use and benefit.” Our
working definition of urban green resilient infrastructure is as follows: The human-modified
biological resources in urban areas that provide ecosystem services and enhance resilience by
reducing the risk of flooding.
Moreover, Naumann et al. (2011) mention the value of substitutability with gray infrastructure,
and Davies et al. (2015) point to the importance of GI being strategic, inter/trans disciplinary and
socially inclusive. Green infrastructure projects such as CAC allow local governments to make
the most of the limited public budgets and achieve multiple goals with a single investment (EPA
et al. 2014).
Urban GI and Ecosystem Services
The common denominator of all GI typologies is the multiple benefits they provide for urban
development and sustainability, in many scales and levels, through ecosystem services. GI offers
opportunities like integrating nature in the urban context, protecting the biodiversity and
landscape diversity, promoting public health and providing physical and psychological benefits
to the citizens. This is achieved by enhancing the provision of ecosystem services through
increased vegetation coverage; maintenance or creation of habitats; structuring ecological
networks to support the alleviation of ecological impacts and habitat disintegration; and
introducing sustainable landscapes and ecological resilience (Opdam et al. 2006; Tzoulas et al.
2007). Over the past decades, an overwhelming amount of research has been conducted on this
topic, especially in light of accelerated land use transformations and urbanization, which
pressures urban ecosystems and often leads to their degradation with all the consequences this
has as a result on human well-being and urban resilience (Demuzere et al. 2014; Gómez-
Baggethun et al. 2010a; Gómez-Baggethun et al. 2010b). Ecosystem services (many times
referred to as ‘natural capital’) are the benefits provided by components of nature (e.g., soil,
water, species) that contribute to our health and well-being, making human life both possible and
worth living. In recent years, the ecosystem services theory has been further developed as a way
to understand and manage natural resources (Millennium Ecosystem Assessment 2004).

21
Although many categorizations of ecosystem services can be found in the literature, a well-
established typology has been presented by The Economics of Ecosystems and Biodiversity
(TEEB 2010), dividing ecosystem services into the following four basic categories:
• Habitat or supporting services: basic processes and functions that are necessary to
produce all other ecosystem services like soil formation, nutrient cycling, photosynthesis,
water cycling, required for the upcoming services.
• Provisioning services: the products obtained from ecosystems, including food, fiber, fuel,
genetic resources, natural medicines, ornamental resources, fresh water; products that
ecosystems provide, and humans consume or use.
• Regulating services: the benefits obtained from ecosystem processes such as flood
reduction and water purification, air quality regulation, climate regulation, erosion
regulation, pollination; benefits that healthy natural systems can provide.
• Cultural services: intangible benefits people obtain from ecosystems through spiritual
enrichment, aesthetic enjoyment, reflection, recreation and religious inspiration provided
by natural landscapes.
These four categories can be divided into various subcategories, depending on the conceptual
frameworks being used in different studies. There are multiple benefits that have been identified,
both in academic literature and practices of GI and ecosystem services; table 1 below presents
the GI benefits identified in recent literature, with an emphasis on the benefits related to flood
risk and land/property values.

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Table 1: Examples of GI Benefits Identified in Recent Literature with an Emphasis on
Benefits Related to Flood Risk and Land Values.
Byrne et al.
2015
Broad public appeal, less politically contentious strategy, improved
aesthetics, increased property values, modulated ambient temperatures,
reduced stormwater runoff, cooling heat islands, reducing electricity
consumption, lowering mortality and morbidity associated with heat waves.
Demuzere et al.
2014
CO2 reduction, thermal comfort and reduced energy use, reduced problems
with flooding/peak flows/droughts, improved water quality, improved air
quality.
M’Ikiugu et al.
2012
Biodiversity promotion, cultural and historical identity, disaster prevention
and mitigation, energy saving, economic activities support, environmental
education, food/resource production, good aesthetics, improvement of local
climate, nature conservation, noise reduction, part of larger green network,
planning structure, pollutants filtration, promotion of communal activities,
public health promotion, rain water harvesting, recreation opportunity,
reduction of greenhouse gases, reduction of public infrastructure cost,
stormwater management.
Foster et al.
2011
Land-value, quality of life, public health, hazard mitigation, regulatory
compliance, aesthetic value.
CNT &
American
Rivers 2010
Reduced stormwater runoff, reduced energy use, reduced pollutants,
reduced atmospheric CO2, urban heat island, aesthetics, recreation, reduced
noise pollution, community cohesion, habitat improvement, public
education.
Forest Research
2010
Economic growth and investment, land and property values, aesthetics,
improving levels of physical activity and health, promoting psychological
health and mental well-being, facilitation of social interaction, inclusion and
community cohesion, improving air quality, sustainable drainage, urban
heat island, aesthetic quality, regeneration of previously developed land,
quality of place, a sense of place, regional and local economic regeneration.
ECOTEC 2008
Economic growth and investment, land and property values, labor
productivity, tourism, products from the land, health and well-being,
recreation and leisure, quality of place, land and biodiversity, flood
alleviation and management, climate change adaptation and mitigation.
Valuation of Ecosystem Services and GI Benefits
The valuation of ecosystem services can serve different purposes, including raising awareness;
highlighting the consequences of alternative courses of action; assessing the impacts of
ecosystems on human well-being; supporting decision-making regarding the management of

23
urban ecosystems; and overall, establishing an indicative monetary value to natural capital.
When assessing ecosystem values, a combination of methods can be applied; depending on the
service being analyzed and, in an urban context, the different scales from regional to building
perspective (Gómez-Baggethun and Barton 2013). Moreover, there is a diverse and increasing
range of valuation methods and criteria for the ecological and socioeconomic values of functions
and services that are provided by natural and semi-natural ecosystems. Even though the valuation
of ecosystem services is difficult and sometimes controversial, the potential importance that such
values can have for the economic system and policymaking is compelling. Although the innate
value of ecosystems services is obvious, failure to qualify and quantify ecosystem services can
result in an implied, perceived value of zero. In this case, ecosystems, “rather than being
‘priceless,’” can be considered “worthless” (TEEB 2010) and lead to the continuation of the
‘business as usual’ scheme, an over-exploitation and degradation of ecosystems that is both
inefficient and detrimental to the human existence (Loomis 2000).
In business-as-usual practices, ecosystem services are considered easily replaceable by grey
infrastructure and engineered services. These more-conventional strategies are often thought of
as the best option in local government planning and do not consider the price of replacing the
ecosystem services once they are gone, as the damage to urban ecosystem services involves an
economic cost in different scales. As examples, health problems can result from a lack of air
purification; carbon sequestration by urban trees; buffering of climate extremes by vegetation
barriers; and noise reduction by vegetation walls (Gómez-Baggethun and Barton 2013).
A careful selection of valuation methods is vital, as each method is relevant only to specific
services, benefits and circumstances. Regarding ecosystem services resulting from GI, authors
like Barthel et al. (2010), Ernstson et al. (2010), Schäffler and Swilling (2013) and Davies et al.
(2015) suggest that, due to the diversity of GI benefits, the assessment can be undertaken in
many different ways and at multiple, different levels, in order to fully understand the impact of
each action.
The main valuation methods for GI benefits and ecosystem services as have been discussed by
various scholars are: i) direct market value, ii) market alternatives or indirect markets, including
replacement costs method, damage costs avoided method and production function, iii) surrogate
markets including hedonic price method and travel cost method, iv) stated preference including
contingent valuation method (CVM) and choice modelling, v) participatory valuation and vi)
benefits transfer (TEEB 2010).

24
Figure 2: Economic Valuation of Ecosystem Services in Urban Planning at Different Scales.
Source: Gómez-Baggethun and Barton 2013.
Damage Costs Avoided Concept
Regarding the valuation of flood protection benefits of GI, the ‘damage costs avoided’ and has
been applied extensively. The method measures the cost of damages incurred if the protection of
ecosystem services was absent (for example, property damages avoided) or the cost of providing
a protection at a similar level, with the same benefits.
The damage costs avoided method can be applied by using two different approaches:
1) The first approach uses information regarding the potential damages that properties could
incur if there were no restoration of the natural barrier.
2) The second approach uses the information of the economic value that people spend in
flood protection, for example insurance premiums for natural disasters (i.e. flooding).
Therefore, this approach is using insurance costs as a proxy for the value of risk reduction
projects (King and Mazzotta 2000). According to MacDonald, et al. (2016), in some
urban areas exposed to flooding, people have two options: “pay higher insurance
premiums in areas with a greater likelihood of flooding or pay higher housing costs in
areas with lower probabilities of flooding”.
In the river Charles, Massachusetts, a wetland under protection not only provides a range of
water quality, recreational and economic benefits, but also protection to communities of Boston

25
and Cambridge—with an estimated 19 million USD in flood damages avoided. Additionally,
adjacent properties have shown an increase in value (Weiskel 2007). Economic flood damage
assessments are often conducted, regardless their high level of complexity, to provide valuable
information to decision makers about damage costs avoided by implementing climate resilience
measures (Mertz et al. 2010; Blanco-Vogt and Schanze 2014) and GRI interventions (Barbier et
al. 2013).
An applied avoided cost analysis could compare the incremental cost of resilience infrastructure
with the projected costs of rebuilding and recovery after destructive climate events, such as
flooding, if no investment had been made in the present. In this formulation, the cost of GRI and
other resilience measures might be significantly less than the rebuilding and repair costs to
impacted properties and, therefore, constitute saved or avoided costs (Beecher 2011).
Theoretically, this will be reflected in land values. Some increment of future land values is
preserved by virtue of current investments in resilience measures, and that increment is robust
enough to be captured for the purposes of financing green resilient infrastructure. The
“preserved” value is “added” in the sense that it represents an increment that would otherwise be
diminished by the impacts of flooding value and other severe weather events and costs of
restoring those same properties to initial value (Beecher 2011).
Alternatively, according to the second approach of the avoided cost method, in theory, under the
existence of a flood/disaster risk insurance market in the housing sector, the risk premiums
would reflect the level of flood risk in an area, and therefore could be considered as proxy
measurement of the benefit of GRI infrastructure measures that aim to reduce flood risk. In
practice there is some evidence showing that people’s perceived risk, and therefore demand for
flood insurances, is increasing after flood events and, as time passes, eventually decreases (Bin
and Landry 2013; Pommeranz and Steininger 2016).
In the study by MacDonald et al. (2016), people were asked to choose between higher land
values with no risk of events, or lower land values with high risk of events. By reducing the risk
and capturing this extra revenue in higher risk areas, land values go up; providing an increase on
tax revenue, produced by multifunctional green and resilient infrastructure. Flood insurance and
willingness to pay for flood insurance could therefore indicate the willingness to invest in
climate resilient infrastructure.
The city of Chicago became a pioneer of green alleys and streets, implementing 30 green alleys
with permeable pavement and over 200 catch-basins throughout the city. The objective of these
measures was to slow the rate of stormwater runoff, allowing urban surfaces to have natural
absorption, thus preventing flooding and therefore increasing the urban infrastructure capacity to
handle extreme precipitation events. It also showed that with avoiding the flooding of just three
homes, the investment was justified. Additionally, the trees planted are also estimated to have
returned approximately $1.50 to $3.00 USD per tree for every dollar invested (City of Chicago
2010).
Hedonic Pricing Model (HPM) for Valuing GI and Flood Risk Reduction
HPM incursion in the environmental assessment is relatively new and is gaining popularity

26
among economists and urban planners because of the benefits that it provides for monetizing
non-market values such as environmental amenities.
The provision of leisure opportunities and aesthetic enjoyment that urban GI provides to
properties ejects high impact on its values. Yet, all these benefits lack monetization and,
therefore, tend to be ignored or underestimated in urban development plans (Noor et al. 2015).
This quantitative information about the implicit non-market price benefits from GI in land value
is required to approach relevant stakeholders to encourage GI initiatives. HPM, combined with
technologies like GIS, has become a powerful tool to fill this lack of data and translate it into the
language that planners and decision makers are familiar with (Kronenberg 2015).
The following table shows research examples of assessments of GI impacts on land value
through HPM, which can provide valuable ecosystem services.
A report by the Trust for Public Land states that proximity to urban parks and open space
positively affects residential land value and suggests that commercial properties are likely to
share a similar response. According to the report, the impact of park space on property values is
primarily affected by the distance from the park and the quality of the park. Furthermore, the
study investigates the impacts of urban parks on reducing the costs of managing urban
stormwater using the amount of runoff diverted from traditional “hard infrastructure to estimate
cost reduction” (Harnic and Welle 2009). However, the impact of flood risk reduction on land
values due to GI, especially as a ratio of the impact of the green components of the project,
hasn’t been researched thoroughly and systematically.
HPM has also been widely applied for the assessment of the impact of flood events on land and
property values (Table 2). It is worth noting that Daniel et al. (2009) conducted a meta-analysis
of hedonic modelling case studies in the US and found that there is often an obfuscation of
amenity effects of proximity to water and risk exposure that causes a systematic bias in the
implicit price of flood risk. They clearly suggest to carefully distinguish between the positive
(pleasant view) and the negative (flood risk) water related amenities.
As shown in tables 2 and 3, literature offers cases in the US, Europe and Asia where green
corridors for flood management, restoration of natural floodplains, multifunctional public space
for recreation and stormwater management all combine risk reduction with multiple
sustainability benefits and increase surrounding property values (Madison and Covari 2013),
chiefly through the HPM approach. However, as mentioned, the impacts of resilience
improvements and green infrastructure projects on land value are less known, and largely
undocumented, in Latin American cities, including Cali. It is an open question whether land
values in these cities experience similar dynamics. Particularly in Colombia there is not any
study applying an HPM to value GI attributes and flood risk reduction.
HPM has become a useful tool to understand the value that urban GI adds to properties, which
can be used to promote green space investment, its preservation and allocation in cities, as well
as reducing tradeoffs between sprawl, leap frogging and urban quality of life. HPM offers an
opportunity to position GI as a priority in governance, urban and climate change issues through
its capitalization in marketable goods, making it an adequate strategy for a capitalized neoliberal
world (de Groot et al. 2012).

27
Table 2: Studies of GI Impacts on Real Estate and Land Values Using HPM.
Country
Year
Author
Results
Poland
2016
Piotr Czembrowski,
Jakub Kronenberg
Positive impacts on apartment prices related with
distance, type and size of a GI + percentage of
greenery within a 500 m radius
Malaysia
2015
M. Zainora Asmawi
Alias Abdullah
Increase between 3 to 12% in house prices based
on GI size and their proximity to the property.
Malaysia
2015
Noriah Othman,
Abdul Hadi Nawawi
GI positive contribution toward house and property
price. Concluding that GI provides benefits toward
the community in term of economic, social and
environment.
Poland
2015
Robert Zygmunt,
Michal Gluszak
Strong evidence of positive impact of GI proximity
on undeveloped property prices, 100 m increase in
distance from the green land decreases land value
by approximately 3%.
USA
2014
Marisa J. Mazzotta,
Elena Besedin and
Ann E. Speers.
Increased real estate values due to improved ES, in
particular augmented landscape and GI features.
Italy
2014
Vincenza Chiarazzo,
Luigi dell’Olio, Ángel
Ibeas and Michele
Ottomanelli
The estimated models highlighted how
environmental quality affect the prices of real
estate properties, showing positive signs if the
buildings were located near beach areas and GI.
USA
2013
I-Hui Lin, Changshan
Wu, Christopher De
Sousa
Green facilities mainly for passive recreation, with
exception of gardens, were likely to have positive
impacts on property values.
USA
2012
Jean-Daniel
Saphoresa, Wei Lic,
Comprehensive analysis to-date of GI capitalized
benefits in the housing market, recommending
targeting private owners to invest on GI.
China
2010
C.Y. Jim, W.Y. Chen
GI in the residential area was highly valued by
Hong Kong people, adding a sizable premium for
apartments located within the service area of a park
and with a view of it.
Source: Authors.

28
Table 3: Studies on the Impact of Flooding on the Value of Real Estate and Land Values.
Author
Year
Location
Method
Results
Eves
2002
Sydney,
Australia
Comparison of mean
prices of objects
influenced by flood and
objects flood free (t-
test)
Short term discount
Zhai and
Fukuzono
2003
Japan
Cross-sectional, Panel
analyses & Hedonic
Model
The flood effect amounts to -1.27%
in 2001 and -4.7% yen/m2 in 2002.
Bin and
Polasky
2004
North
Carolina, USA
Hedonic Model
Floodplain location lowers real estate
values by 5.7 %
Troy and
Romm
2004
California,
USA
Hedonic Model
Floodplain location lowers real estate
values by 4.2 %
Hallstrom
and Smith
2005
Florida, USA
Repeat sales
Decline of 19 % of housing prices in
flood zones
Bin and
Kruse
2006
North
Carolina, USA
Hedonic Model
Floodplain location lowers real estate
values by 5–10 %
Bin et al.
2008
North
Carolina, USA
Hedonic Model and
Spatial Data
Price discount depends on flood rate,
lies between 6.2–7.8 %
Pope
2008
North
Carolina, USA
Hedonic Model
Floodplain location lowers real estate
values by 3.8–4.5%
Lamond
et al.
2009
UK
Repeat sales
Temporary impact of flooding on
property values, normal market value
after 3 years
Daniel et
al.,
2009
USA
Hedonic Pricing
Methods (meta-
analysis)
The increase probability of flood risk
by 0.01 is associated with a real
estate price reduction of 0.6%
Pryce
et al.
2011
Different areas
Analyzing housing
prices in combination
with findings of
behavioral economics
and sociology risks
Uneven pattern of inertia followed by
rapid tipping-point declines
Source: Authors

29
3.2 Green Resilient Infrastructure and LVC
Financing Green Infrastructure
Numerous studies have been quantifying the benefits of green infrastructure in monetary terms,
as was discussed in the previous section, to provide arguments for financing investments on
ecosystem services and GI. Pesquera and Ruiz (1996), in their study on green financing for urban
(water sector) infrastructure projects in Colombia, underline the need for involvement of new
actors to mobilize financial resources and the importance of adding environmental attributes to
projects (green infrastructure elements providing ecosystem services), as they are crucial for the
funding opportunities these green infrastructure projects can have. Similarly, Foster et al. (2011)
explain how cities in the U. S. have managed to incentivize (and therefore finance) green
infrastructure projects:
1. By showing evidence of upfront or life-cycle cost savings when compared to alternatives
for both public and private projects.
2. By providing direct financial incentives to property owners for green infrastructure
installations.
3. By instituting laws, regulations, and local ordinances requiring implementation of green
infrastructure on private property.
4. By mandating that public projects incorporate green infrastructure to demonstrate
viability and value (e.g., street tree planting, green modifications to roads, green roofs on
public buildings).
The aforementioned studies, among others, have shown how private, municipal, or regional
funds can be successfully mobilized for the implementation of GI projects by highlighting their
multiple benefits. However, the GI projects studied do not necessarily include resilience
attributes such as flood risk reduction but are focused on the existence of green attributes that
provide various types of ecosystem services.
LVC as a Finance Mechanism of Urban Climate Adaptation and Resilience
The implementation of LVC instruments is a widespread strategy to finance (grey) infrastructure
improvements, especially in Latin America and Colombia (Smolka 2013). Kostaras et al. (2015)
argue that the same instruments can be used to finance GI interventions and present an “LVC
instruments for GI interventions” framework that describes which LVC instruments are most
suitable for each GI intervention.
Despite the research on how GI impacts land values (Asmawi and Abdulah 2015; Jim and Chen
2010; Madison and Covari 2013; Mazzotta et al. 2014), the financing of climate adaptation and
resilience measures through LVC remains untested. Literature on climate finance refers to LVC
as a potential mechanism to fund investments in urban resilience, particularly as a component of
strategies to mobilize private capital in adaptation through market mechanisms (Brugmann
2011). However, the direct use of LVC to finance climate adaptation has yet to be implemented
in practice in any significant way in Latin America and the Caribbean, with the exception of the
Curitiba Flood Protection TDR Program.

30
The Curitiba (Brazil) Flood Protection TDR Program is an exceptional example of the use of
land value capture to implement resilience solutions that might have relevance to Cali. Curitiba,
a city surrounded by rivers, experiences serious recurrent flooding. Curitiba has used TDR to
preserve the green recreational areas for flood protection and relocate slum dwellers from
informal settlements that, in large measure, occupy flood prone areas. Curitiba has created a
natural drainage system using TDR for environmental protection, instead of building hard-
engineered flood protection structures. The city has transferred development from areas
designated for conversion into parks to absorb overflow and lakes constructed to contain
floodwaters and prevent flooding downstream (termed as “TDR sending areas”). Through the
TDR mechanism, developers and owners of property in high risk or environmentally sensitive
zones obtain the right to build in designated city “receiving areas.” Research shows that the costs
of building and maintaining Curitiba’s extensive park system is estimated to be five times less
expensive than the construction of flood protection canals (referred to as “hard engineered” or
“gray” resilience solutions) (Dharmavaram 2013).
The lack of application of LVC mechanisms toward urban climate change investments presents a
challenge for this type of land policy in Cali and other cities (Kostaras et al 2015). The
application of LVC becomes more difficult when the impact of the (green or resilience)
infrastructure project on the land and real estate values is not direct and immediate. Thus,
property and land markets are very slow in reflecting risk reduction benefits (Pryce et al. 2011),
especially if they result from GI intervention (such as parks, green areas; possibly also including
recreation elements) and not from gray, visibly robust and effective infrastructure.
By assessing the environmental benefits and the flood risk reduction of GRI, land value capture
has the potential to finance urban climate adaptation and resilience.
The GI presented in this framework include risk reduction components, and therefore the
increase of land value is expected due to both the “green” elements and the flood risk reduction
that the GI intervention achieves. Still, there is a gap in the literature looking at the ratio of the
impact of GI-related benefits in land values in relation to the flood risk reduction impacts to land
values.
Figure 3 illustrates the main steps of the elaborated conceptual framework, including the main
methods applied in the study.

31
Figure 3: Elaborated Conceptual Framework and Main Steps of the Study.
Note: The red highlighted boxes indicate concepts and methods that could be applied in theory but were not applied
in this study.
Source: Authors
4. Methods and Data
4.1 Data Collection
POT GIS Database
The main data source was the “revised and adjusted land use plan” (RAPOT) GIS Geographic
Data Base (GDB) of 2013, from the municipality of Cali and other municipal organizations such
as the Administrative Department of Environmental Management (DAGMA) and the
organization responsible for preparing publicly accessible spatial data for the city
(Infraestructura de Datos Espaciales de Santiago de Cali, IDESC). The databases are available
online on the website of the municipality of Cali. By comparing the timeframes of all sources,
year 2013 was selected for the analysis, as most data is available for this year. When information
was not available for 2013, GIS layers for 2014 (POT 2014) were used. In the case of missing

32
data or mismatches on timeframe, after having exhausted other sources, estimations were made
based on files from past years. Since 2011, which is the year of our earliest data used (except for
the land value, which dates from 2010) there have not been any major shock events in Cali.
In cases of data missing from the GIS databases, additional sources are: official documents;
reports and results from the national surveys carried by Ipsos Public Affairs and the program
“Cali Cómo Vámos;” the official project design documents from “Corredor Ambiental
Cañaveralejo” made by Corporación Regional Autónoma del Valle del Cauca (CVC), Ceding
SAS and Cuna Group for sustainable engineering and architecture; urban analysis done by CVC
and The Territory Construction and Space Research Center from Universidad del Valle (CITCE);
the annual reports of “Cali en cifras” (Alcaldía de Santiago de Cali 2014); water and air quality
reports from the Administrative Department of Environmental Management (DAGMA) and
homicides and robbery events from the Social Observatory.
Table 4: Variables for HPMs and Sources of Data.
Category
Variable
Unit
Source
Land value
Land values from
Lonja
COP / square meter
GIS_#164_LonjaPropiedad
Raiz_Precio_del_suelo_x_S
ubareas_2010
Green infrastructure
Number of trees
Number of trees
GIS_#280_RAPOT_2013
ARBOLES_CENSO_CALI
Open green spaces
Square meters
GIS_#268_GIS
LAYER_Espacio Publico
Vegetation coverage
Square meters
LILP_HEDONIC_RISK/
URBAN
VEGETATIONCOVER
(.shp file UniVallle)
Pedestrian streets
Meters
GIS_#16_Ejes Peatonales
Bike lanes
Meters
http://idesc.cali.gov.co/do
wnload/pot_2014/mapa_3
1_red_basica_de_ciclo_ru
tas_priorizadas.pdf
Exposure to fluvial
flooding
Yes/no
GIS_FR_CAÑAVERALEJ
O
GIS_FR_CALI
GIS_FR_CAUCA
GIS_#421_BARIOS
GIS_#634_MANZANAS
CATASTRO

33
Contact with water
bodies in an 80 m
radius
Yes/no
GIS_#88_RIOS_dg07
GIS_#421_BARIOS
GIS_#634_MANZANAS
CATASTRO
Mobility and
accessibility
Public transport
efficiency
Number (from
index)
Encuesta Cali como vamos
2013 (Affairs 2014)
Public transport stops
Number of stops
GIS_#21_Estaciones de
parada
GIS_#631_Estaciones MIO
GIS_#421_BARIOS
GIS_#634_MANZANAS
CATASTRO
Public roads quality
Number (from
index)
Encuesta Cali cómo vamos
2013 (Affairs 2014)
Distance to CBD
Meters
Computation done in QGIS
Distance from
secondary central
locations (less than 1
km)
Meters
Computation done in QGIS
Socio-economic
Life satisfaction index
Number (from
index)
(Giraldo and Zapata Toro
2014)
Health amenities
Number of
locations
GIS_#158_Equip Salud
GIS_#585_Equip Salud
GIS_#421_BARIOS
GIS_#634_MANZANAS
CATASTRO
Cultural amenities
Number of
locations
GIS_#127_Equip Cultura
GIS_#152_Equip Cultura
GIS_#421_BARIOS
GIS_#634_MANZANAS
CATASTRO
Commercial activities
Number of
locations
GIS_RAPOT_2013
Actividades_Economicas_C
amara_Comercio_2012 any
Restaurants and hotels
Number of
locations
Floor Space Index
Number (from
index)
GIS_#164_LonjaPropiedad
Raiz_Precio_del_suelo_x_S
ubareas_2010

34
GIS_#421_BARIOS
GIS_#634_MANZANAS
CATASTRO
Safety
Homicides
Number of
homicides
(Cali 2010)
Robberies
Number of
robberies
(Cali 2011)
Environment
Noise average levels
Decibel
GIS_#622_Ruido
GIS_#421_BARIOS
GIS_#634_MANZANAS
CATASTRO
Source: Authors
Data on Land Values
There are two main sources of information for land values in Cali: the Lonja and the cadaster,
each of them with advantages and disadvantages to be used in the hedonic model.
Table 5: Comparison Between the Two Possible Sources for Land Value in Cali.
Variable
Cadaster
Lonja
Level
Land parcel
Land parcel
Value
Exact value
Price range levels (minimum-maximum)
Components included
Land and building
Land
Unit
Colombian pesos / m2
Colombian pesos / m2
Availability
Urban perimeter of Cali
Urban perimeter of Cali
The land value information source selected for this study is the digital GIS layer of the Lonja of
2010, as more recent versions are not available digitally. In the Lonja, the land values are
provided as a range between a minimum and a maximum value per “value-defined” area
(“polygon”). These polygons form value zones that may extend beyond the block borders. Going
through the Lonja, in many cases the reader can observe that land values at the frontline of a
main street or a park, with a good view, are much higher. Therefore, the Lonja is able to sense
and indicate these differences in the “value zones,” which are independent of the spatial
organizational divisions of the city (for example, city block). In our model, the land values per
block are calculated as the average land parcel value in the respective area.
The cadaster could be an alternative data source for the land value in this study, as it provides the
exact, official value of each plot. However, it does not differentiate between the value of the land
and the buildings on it, but it presents their total, combined value. Unfortunately, these two
elements cannot be separated within the same GIS layer of RAPOT. Under some assumptions
and using information from other layers in the RAPOT database (such as the built area per plot,
and the maximum area allowed to be built on the plot, in square meters), a manual separation of
land and building values could be attempted. However, the result would not be very precise, as

35
the index of occupation (IO, “índice de ocupación”: the mapping of the current built space
situation) information is not available in the RAPOT database. For this reason, and as the Lonja
represents the market land values reflecting actual “on the ground” transactions (not official
estimated ones), the Lonja was preferred over the cadaster as the land value source to be used in
the HPM.
Figure 4: Extracts from the Lonja of Cali.
Source: Lonja de Propiedad Raíz de Cali y Valle del Cauca 2008.
Data on Exposure to Flooding
Despite the high exposure of Cali to flooding due to the river Cauca and the six other rivers of
the city, which are the main sources and locations of flooding, there is no concise flood exposure
analysis for the same return period at city level. This limitation could be substituted by separate
analyses of consistent flood scenarios for the seven rivers.
However, several studies have been conducted on smaller segments of the urban area and for
different return periods; not on a river base but on a drainage system base. There is an overlap
between these studies for 1 in 100 events. Studies conducted by engineer Gustavo Barrientos of
DAGMA in 2011 for the rivers Cali, Cañaveralejo, Aguacatal, Lili, and Melendez have
calculated the exposure to flooding at 1/100 years level. The study conducted in 2013 by OSSO
Corporation and Royal Haskoning DHV for the dike of the river Cauca calculates the exposure
to flooding for a longer time horizon: up to 500 years. However, the scenario for 1/100 has also
been calculated.
Some of these studies, in various combinations with additional information and with different
focus, have been visualized on several maps that are available on the website of the municipality
of Cali, and recently published in the “Plan de Adaptación en Mitigación to CC” (Alcaldía de
Santiago de Cali 2016). However, the maps are not included in the GIS databases available
online at the website of the municipality of Cali.

36
Table 6: State of Knowledge for Threat of Flooding, Vulnerability and Flood Risk in Cali
in 2016.
Phenomenon
Threat knowledge
Vulnerability
knowledge
Risk assessment
Flooding of Cauca
river
Modeling for the
floodplain between the
Interceptor Canal Sur and
Cali river. The model
assumes the overflow of
Cauca river over the dam
in 6 sections of 150 meters
each (sections with
reduction of the original
height of the dike).
Vulnerability analysis
based on population,
housing, education and
health infrastructure,
and main elements of
water supply, sewerage
and electricity systems.
Area of analysis:
between South
Interceptor Canal and
Cali river.
Estimation of
losses in housing
(structure and
content),
education and
health
infrastructure, and
main elements of
water supply,
sewerage and
electricity
systems. Area of
analysis: between
the Canal South
River Interceptor
and Cali river.
Return period (years):
100, 250, 500
Return period (years):
100, 250, 500
Return period
(years):
100, 250, 500
Pluvial flooding
Modeling for pluvial
flooding of the east
drainage zone of Cali for
return periods of 10, 20,
50 and 100 years.
Modeling vulnerability
homes in the area
Oriental City sewer for
rains TR = 10, 20, 50
and 100 years
Calculating losses
housing (content)
for flooding in the
area Oriental City
sewer for rains TR
= 10, 20, 50 and
100 years
Return period (years):
10, 20, 50, 100
Return period (years):
10, 20, 50, 100
Return period
(years):
10, 20, 50, 100
Flooding of the
tributaries of Cauca
river
Modeling for the
South Interceptor Channel
and sections of rivers
Pance, Cañaveralejo, Lili,
Meléndez, Cali and
Aguacatal. For the last 4
rivers hydraulic capacity
analysis has also been
conducted.
Not available
Not available
Return period (years):
50, 100
Not available
Not available
Source: Adjusted and translated by the authors from Documento Técnico de Soporte POT 2014, Alcaldía de
Santiago de Cali.

37
Due to data limitations, the current analysis has been conducted with separate flood exposure
maps for each of the city’s rivers. Only the three biggest fluvial threats of the city are included:
rivers Cauca, Cali, Cañaveralejo, for return periods that vary from 50 to 500 years, according to
the available data.
The research team contacted all possible sources in order to receive or construct digital flood
exposure maps for the same return period, for all rivers; however, it has not been possible to
achieve this within the timeframe of this study. An interview with the hydraulic engineer and
flood risk expert Gustavo Barrientos, who has conducted flood risk studies on behalf of the
municipality for several rivers (among them the Cañaveralejo river), confirmed the lack of a
consistent flood risk study and map that covers all rivers and the total area of the urban
perimeter. The interview provided deeper understanding of flood risk in Cali. Rivers Cauca,
Cañaveralejo and Cali are the only natural rivers, while the others are currently in the state of
artificial collector canals. There is an important difference between natural and artificial rivers,
as the natural ones carry residuals, rocks, trees, natural elements from the mountains. The
artificial are easier to manage, within the urban area. Regarding protection against flood risk, the
guidelines provided by the POT explain that dikes and canals in the city should be designed for a
return period of 100 years, plus one meter. However, currently not all the natural rivers are
protected by dikes, while the canals are designed for a return period of 25 years. Specifically,
rivers Cañaveralejo and Cali don’t have any protection of dikes. There are two flood risk
scenarios for the city: for flood events with functioning dikes and also for failure of the dikes.
Maps that present “pluvial flooding” could be considered more realistic because they do not
include failure of dikes. However, the maps that illustrate “fluvial flooding” present more
hazardous situations. The flood risk maps of Cauca river that are included in the POT represent
one of the most extreme scenarios, with failure of dikes for a 500-years return period.
The result of the interview was the development of a city-wide flood risk map, based on
empirical expert knowledge and official studies published by the city (image 8). In this map there
are no areas without risk of flooding. This is due to empirical evidence from past events, which
proves that, in several locations, floods are observed even in areas without rivers, due to rain
(pluvial flooding) and the conditions of drainage infrastructure. Table 7 below provides an
overview of the attributes of each of the three flood risk levels (low, medium, high) that
summarize the flood risk knowledge we currently have for Cali and are illustrated in image 8.
However, although this map provides an overview of flood risk in the city, it is not and official
map but a synthesis product of primary and secondary data, and therefore was not included in the
hedonic model analysis.

38
Table 7: Compilation of Available Knowledge of Flood Risk in Cali, in Three Flood Risk
Levels.
Risk levels
Risk types
Rivers
Return years
(available studies)
Impacts
High risk level
Fluvial flood risk
and failure of dikes
(immitigable risk)
Cauca
500 (and maybe
100)
Long time to
recover, lives lost,
high economic
damages
Medium risk level
Fluvial (no failure
of dikes) and pluvial
for channels
Cañaveralejo
study of G.B.
2011, Rio
Cali,
Aguacatal,
most of the
channels in
the city
100 for rivers, 25
for channels
2-3 days recovery
time, less
economic damage,
small amount of
water to manage
(for example
50cm)
Low risk level
Mitigable risk
Melendez,
Lili, Pance
100
(not detailed flood
risk information)
flood risk
protection exists
Source: Adjusted and translated by the authors from Documento Técnico de Soporte POT 2014, Alcaldía de
Santiago de Cali.

39
Image 8: Risk Map for the Whole City.
Source: Authors, based on expert interview and secondary data.
Limitation and challenges
Challenges faced while analyzing the GIS data were changes in coding, no metadata, inadequate
information on the attributes measurement units and meanings, contradictions and data holes2
which can constrain the analysis significantly, and potentially affect the results. In such cases,
gaps are filled with data from other organizations, or approximate estimations based on data
from other years. Moreover, validation of the information used is achieved through triangulation
with other sources, researchers, experts.
4.2 Data Analysis: Hedonic Pricing Models
Multiple regression analysis in Hedonic Pricing Models (HPM) was used to estimate the
influence of variables representing green infrastructure elements, socioeconomic aspects,
2 For example, a partial tree census where some districts have not been mapped and are presented on the map as
having zero trees.

40
mobility/accessibility and environmental attributes on land values in the city of Cali. In the
respective HPMs that were structured, land value is the dependent variable, and all other
indicators are the independent variables. Multiple regression allows a ceteris paribus analysis
that controls for other variables (crime, amenities, accessibility, among others) that
simultaneously affect the dependent variable (land value). As the dependent variable in this case
is the land value, variables related with the intrinsic characteristic of the flats or houses, such as
furniture, balconies, air conditioning, etc., are not included in the model. The variables chosen
for the model are the ones connected with extrinsic factors that affect land values based on the
position of the land unit in the city, such as: presence of green infrastructure, crime, distance
from the city center, proximity to public transport, etc.
Image 9: Dependent and Independent Variables of HPMs.
Source: Authors
The HPM method was used to conduct analysis at three levels, in order to explore the relations
between the variables in more detail:
1. HPM1: at the scale of the neighborhood, for the total of the urban perimeter.
2. HPM2: at the scale of the urban block, for the total of the urban perimeter.
3. HPM3: all urban blocks within the urban perimeter that are in contact with water bodies
in a radius of 80 m, which is an average depth for a standard block.
The equation used in all models is the following (Wooldridge 2003):

41
The main purpose of the analysis at the block level is to approach in detail those critical
indicators that did not show significance at neighborhood scale and check if the ones that did
indicate significant impact changed their sign of significance. Similarly, the purpose of
structuring a more focused model at the block level, for blocks that are in contact with water
bodies, is to isolate the variable of exposure to the risk of flooding and explore it in detail.
Before concluding to the three models that are presented below, more versions were explored
using the stepwise method. For the final model selection, we avoided to add independent
variables which intuitively do not have a partial effect on y in order to reduce the problem of
multicollinearity: being led to a higher variance of the estimator, but a less efficient model.
However, the primary aim of our models is not the prediction of y, which is the land value, but
the estimation of the beta coefficients of specific independent variables: the ones that will be
altered from the CAU Cañaveralejo project. Therefore, our attention can be shifted to the
statistical significance (p-values) of those variables, rather than the adjusted r2 of the full model.
For this reason, we explored models in two spatial levels (neighborhood and block) and worked
with a general sample that covers the whole urban perimeter, and an additional selective sample
consisting of blocks that are in contact with water bodies, where the risk of flooding is higher.
The three selected models presented below have differences in the selection of independent
variables, the coefficient and significance of each variable, and the adjusted r2 of the model; but
these three models are the best ones, as they were constructed with a selection of indicators in
order to answer the different research questions, and partially at the level of R squared (see
summary of all models in table 8).
As mentioned, our focus for this analysis is on the variables that will be altered from the CAU
Cañaveralejo project, which is considered a GI intervention. In our models, we have grouped
those variables under the category “green infrastructure,” in order to isolate the impact of GI on
land value. The other variables are used as control variables, as no changes are expected to them
from the CAU Cañaveralejo project. The selection of variables is based on the literature review
(Balchin et al. 2000; Duarte and Tamez 2009; Glaeser et al. 2001; Tita et al. 2006; Yang et al.
2016). In models HPM1 and HPM2 the green infrastructure variables “open green spaces” and
“vegetation coverage” are used interchangeably, as the variable “open green spaces” describes
square meter of public green areas, parks and green squares accessible to people as mapped in
the POT database, and the variable “vegetation coverage” describes square meters of vegetation
coverage as they were mapped in GIS by Universidad de Valle using satellite images.
“Vegetation coverage” includes all green spaces visible in the satellite image and is, therefore,
more detailed than the variable “open green spaces,” reflecting the current situation on the
ground. The differences between the coefficients of the two variables are presented in the
“results” section. Other differences in the variables of the two models are the exclusion of the
mobility and accessibility variable “public transport stops” and the socio-economic variables
“life satisfaction index” and “health amenities.”
We developed HPM3 to analyze the proximity to water bodies in more detail and understand
whether the positive effect is due to the existence of water in a range of eye contact, which
would correlate with the view of bodies of water. Model HPM3 explores mainly the issue of the
blocks’ exposure to flood risk and focuses only on this green infrastructure variable. In addition,

42
this model focuses spatially only on the blocks that are in contact with water bodies, meaning
within 80 meters from a river, canal or “quebrada” in the whole city of Cali. The value of 80
meters reflects the average length of a city block in Cali, and the selection of the block in HPM3
ensures that properties on them have visual contact with the bodies of water.
Image 10: HPM3 Sample, Zoom at the Case Study Area.
Source: Authors
In model HPM1, the variables are expressed as the total or average value (depending on the
variable) within the area of each neighborhood. For example, the variable “number of trees”
describes the number of trees within the limits of each neighborhood. In general, a neighborhood
in Cali is an area with an average radius of 265.7 meters. We have 328 observations of
neighborhoods in model HPM1, which is the number of neighborhoods in Cali excluding
neighborhoods with missing data and the “corregimientos,”. Similarly, in models HPM2 and
HPM3, variables are expressed as the total or average value at the block level. A block in Cali
has in average radius of 43 m. In HPM2 we have 10543 block observations, which cover the
whole urban perimeter of Cali, excluding 3339 blocks that are located in the “corregimientos”
outside the defined urban perimeter. HPM3, which focuses only on the blocks that are in direct
contact with water bodies within a radius of 80 m, has less observations (2282). A critical aspect
when using this method for the input of values per spatial unit (neighborhood or block) in the
models, is that the radius may be considered, and we do not include the influence of items that
are outside the limits of this specified area (such as trees or areas of green space,) but may in fact
be directly adjacent to its outline.
Land value, the dependent variable in all models, is expressed as the average price of the land on
the neighborhood or the block in Colombian Pesos (COP) per square meter of land (COP/m2).
4.3 Feasibility Assessment of Land Value Capture Instruments
On November 8, 2016 the research team carried out the second workshop to work closely with

43
institutional representatives who are linked to the CAU Cañaveralejo. Based on focus groups
discussions, the experts’ representatives of institutions (DAGMA, CVC, Alcaldía de Cali) and
community leaders assessed the feasibility of LVC instruments available in Cali and their
possible application in the study area to capture the added value resulting from the CAU
Cañaveralejo project. Furthermore, they explored the possibility of financing the GRI using LVC
instruments.
Another objective of this workshop was to present the preliminary results from the hedonic
pricing model to validate the data and outputs of the study.
5. Results
5.1 HPM Models Results
The following section presents the results of the three selected models, analyzing differences per
variable. Land value is the dependent variable in all models. The GI variables that are included in
the models are: number of trees, vegetation coverage, bike lanes, pedestrian streets and exposure
to fluvial flooding.
The neighborhood model HPM1 showed, in general, which factors correlate significantly with
the land value across Cali. The control variables groups aligned with the expected results
predicted by the literature. Variables such as mobility, socioeconomic factors, as well as safety,
scored high on significance and are shown to have notable impacts on the land value at the
neighborhood level. This was also observed in the GI group, where trees, open green space and
pedestrian streets have high significant and positive impacts on land values. Indicators like bike
lanes, exposure to fluvial flooding and noise did not figure in the significant results. However,
they could be further addressed and discussed at the block level analysis in models such as
HPM2 and HPM3. Indeed, the results from the block analyses helped to verify indicators that at
a neighborhood scale had already shown significance, and to clarify the behavior of other
variables, such as bike lanes, average noise and, more importantly, exposure to fluvial flooding
risk.
Overall, bike lanes, average noise and exposure to flooding were the three indicators that did not
show significant impacts in land value at neighborhood level, but did so at the block level, where
their coefficients were both positive and highly significant. Consistent with the neighborhood
results, in the block model all the control variables, as well as the rest of GI variables (trees,
vegetation coverage, pedestrian streets), showed high significance and positive impact on land
values.

44
Table 8: Comparative Table: HPM1, HPM2, HPM3.
HPM1
HPM2
HPM3
Level of analysis
Neighborhood
Block
Block
Spatial focus
Urban
perimeter
Urban perimeter
In contact with
water bodies
Green infrastructure
Number of trees
38.39***
307.16***
-
Open green spaces
0.00*
-
-
Vegetation coverage
-
2.67***
-
Pedestrian streets
10.84*
27.17*
-
Bike lines
18.21
92.27***
-
Exposure to fluvial
flooding
14777.03
19338.91***
-31778.29***
Mobility and
accessibility
Public transport
efficiency
422710.14***
656394.36***
-
Public transport stops
-997.06
-
-
Public roads quality
1113707.16***
308599.65***
-
Distance to CBD
-7.97**
-20.21***
-4.07**
Distance from
secondary central
locations (less than 1
km)
-
-
26816.08***
Socio-economic
Life satisfaction index
39363.67**
-
-
Health amenities
1952.89**
-
-
Cultural amenities
10043.52***
31566.90***
39092.23***
Commercial activities
141.25***
1836.49***
2052.91***
Restaurants and hotels
-
-
13526.23***

45
Floor Space Index
91.91**
26.52***
-
Safety
Homicides
-3149.62***
-1896.59***
-2422.28***
Robberies
-1208.13***
723.87***
1035.63***
Environment
Noise average levels
-2097.18
-1169.80***
-1911.97***
Observations
328
10543
2282
Adjusted r2
Constant and
significance
VIF
0.53
-
4228079.23***
1.41
0.30
-2662994.85***
1.15
0.19
406213.35***
1.34
Level of significance: *p<0.1; **p<0.05; ***p<0.01
Number of Trees
Results both at the neighborhood and block levels (HPM1 and HPM2) indicate that the “number
of threes” variable has a positive and significant impact. However, the impact of each tree on
land value appears to be eight times higher when we explore this variable at the block level. This
result confirms the findings of The University of Texas (2008), Wolf (2007), Hastie (2003) and
also Donovan and Butry (2010), who used an HPM to show that a single tree added in average
7,130 USD to the value of the property located directly in front of it, plus additional value to
neighboring properties within a 30 m radius. In the latter study, the average combined value is
12,828 USD to the properties lying within that radius. In the case of Cali, and according to the
results of HPM1, each tree has an impact of 38.39 COP per m² of neighborhood land in contact
with it. This impact is eight times higher (307.16 COP) when we examine the variable at the
block level in HPM2. Taking, as an example, the neighborhood El Ingenio and this calculation,
the value of trees in the neighborhood as it is projected on land is almost 27 million COP (8
million USD). However, as Benotto (2002) discusses, “the combined economic value of the
network of urban trees is bigger than the net sum of the individual trees,” which means that the
actual impact of the trees on land values across Cali might be larger than the models’ results
show.
Open Green Spaces and Vegetation Coverage
The variable “open green spaces” is used to describe square meters of public green areas, parks
and green squares accessible to people and was included only in HPM1, at the neighborhood
level. Similarly, “vegetation coverage” is a variable that was used to measure the presence of
greenery at the block level. The indicator considers public and private green spaces, as well as
small vegetation areas between streets. Although at the neighborhood scale the beta coefficient
“for open green spaces” is 0.00, this variable was found to be significant at the block level. The
HPM2 regression coefficient showed that 1 m² of vegetation coverage could increase the land
value per square meter of land by $2.67 COP. Compared to the variable “number of trees”, the
vegetation coverage coefficient seems low. However, the presence of vegetation across the city

46
is much bigger than trees. This explains the big impacts observed in neighborhoods like
Parcelaciones Pance in commune 22, where 25% of the neighborhood surface is covered by
vegetation, representing a high percentage of the neighborhood’s land. These results are
consistent with authors like Morancho (2003), Poudyal et al. (2009), Damigos and Anyfantis
(2011), Melichar and Kaprov (2013), who showed the relevance of green areas on property
values depending on their surface and proximity to the plot.
Bike Lanes
Bike lanes showed a positive significant impact on land value at the block level (HPM2;) and a
smaller, not significant impact at the neighborhood level (HPM1.) The research of Racca and
Dhanju (2006) indicated that, on a neighborhood scale, bike lanes caused a slightly significant
negative impact on property values. In similar studies carried out in Toronto and New York City
(NYC) the conclusion was that at neighborhood scale the impact was almost negligible (NYC
Department of Transportation 2013; Sztabinski 2009). However, when the analysis was carried
out at a street/block scale, residential and commercial properties registered an increase on land
value and sale prices, respectively, due to the addition of new bike lanes. Moreover, in the case
of NYC, at first the inclusion of a bike lane was opposed by small businesses and homeowners in
nearby areas. However, after the construction of the bike lane and the resulting “enhancement of
the streetscape” (NYC Department of Transportation 2014, 39) the traffic flow, economic vitality
and property values were improved. This supports the results in HPM2, where bike lanes reached
high significance and a positive impact of $92.27 COP in the average price per m² of land for
each meter of bike lane created.
Cali has a small number of bike lanes across the city (less than 25 km in total,) which are
distributed in 58 neighborhoods and 536 blocks. Among these blocks, the average contact
between a block and a bike lane is 50m. This led us to think that, as a result of the HPM, bike
lanes would not affect land values significantly in the city of Cali. However, when analyzed at a
block scale through HPM2, the impact that bike lanes have on their adjacent blocks became
clear. The radius of impact of bike lanes (150 m on average) is large enough to affect the blocks
in direct contact with it. As mentioned, based on the results of HPM2, each meter of bike lane
has an impact of $92.27 COP per m² on values for properties in the same block, which is
consistent with the results showed by the NYC Department of Transportation (2014). In the case
of Cali, an additional reason that possibly reduces the impact of bike lanes on land values, apart
from the small number of bike lanes in the city, is that they are mainly introduced as recreational
and not as transport infrastructure. Therefore, bike lanes appear isolated and segmented, roughly
connected to a bigger network. To maximize their benefits, it would be necessary to introduce
them in combination with the aforementioned “enhancement of the streetscape” (NYC
Department of Transportation 2014, 39) which is greenery and tree planting, elements that can
increase land values even as stand-alone variables. A similar strategy in New York City resulted
in rents increasing along pedestrian and bicycle paths by 71% (APTA 2010), and local
businesses registered a 49% increase in retail sales (NYCDOT 2013).
Pedestrian Streets
This variable scored a significant and positive impact in both HPM1 and HPM2 models. The

47
coefficient at the block level is higher, indicating that each additional meter of pedestrian street
correlates with an increase of $27.17 COP on the value of a square meter of land on the same
block. This positive impact on land value is consistent with the results from CEOs for Cities
(2009), which showed a positive correlation between pedestrian infrastructure and property
prices. Their report concluded that an increase of one point in the walkability score could have
an impact between a $500 USD and $3,000 USD increase in property values. The importance
that pedestrian streets have for property values is directly connected to the accessibility and
convenience that comes with them, which is having all the necessary amenities and services at
walking distance from the property.
In our models the impact of pedestrian streets seems low compared to the tree coefficient and
less significant statistically. However, it is important to consider that Cali is car-oriented and
relies on motorized transportation vehicles, neglecting pedestrian infrastructure. Nevertheless,
the results from the HPM show the market’s tendency to prefer properties with this type of
connectivity due to the multiple benefits that come with it (Litman 2014). When the pedestrian
infrastructure allows users to walk comfortably without competing for space against motorized
vehicles it results in more people walking, a signal that the area is safe and interesting (Jacobs
1961). It can be concluded from the effect of pedestrian streets on land value and their presence
in Cali that, as most cities around the globe, there is a “high demand and low supply for human-
friendly streets” (APTA 2010).
Exposure to Risk of Fluvial Flooding
Exposure to fluvial flooding was included in all models as a dummy variable indicating that a
block is either covered by a flood risk polygon on the risk map (yes) or not (no). The results in
HPM1 indicated it as a variable with a positive coefficient, but not significant. The more detailed
HPM2 at the block level returned a positive but significant result for the same variable. This
means that, contrary to what would be expected, a property located in an area exposed to fluvial
flooding is worth $19,338.9 COP/m² more than one located in an area without risk of flooding, in
the condition that all the other indicators are equal. However, this finding could indicate that
there is a confounding effect between the positive and negative aspects of water features, and
therefore these water-related amenities should be distinguished (Daniel et al. 2009).
The more focused HPM3 model, where the indicator “contact to water bodies” was introduced,
the results of the effect of risk of flooding on land values became negative and still highly
significant (-31778.29***). Such model, examining contact to water bodies, could help
differentiate between the benefits of a property that is close to a river and provides a pleasant
view without being exposed to flooding, and one with exposure to flooding but no benefit of a
pleasant view (Eves 2002; Daniel et al. 2009). This finding confirms several studies (Bin et al.
2008, Posey and Rogers 2010, Pryce et al. 2017; Koning et al. 2016) who found a decrease of
11% and 8,6% respectively in the land value of properties located in a floodplain or a flood
prone area, based only on the exposure of the property to this risk and not additional factors.

48
Table 9: Regression Output HPM1 at Neighborhood Level. Variables are ordered in
descending order according to coefficients.
HPM1
Variable
Indicator
β Coefficient
Unit
Variables that
correlate with
increase of
land values
Mobility and
accessibility
Public roads quality
1113707.16
(105878.49)
scale 1–5
Mobility and
accessibility
Public transport
efficiency
422710.14
(58129.69)
scale 1–5
Socioeconomic Life Satisfaction Index
39363.67
(17306.24)
number
Green
Infrastructure
Exposure to fluvial
flooding
14777.03
(15885.17)
yes/no
Socioeconomic Cultural amenities
10043.52
(2259.49)
number of locations
Socioeconomic Health amenities
1952.89
(822.78)
number of locations
Socioeconomic Commercial activities
141.25
(31.65)
number of locations
Socioeconomic
Floor space index
(FSI)
91.91
(44.62)
m²
Green
Infrastructure
Number of trees
38.39
(12.93)
number of trees
Green
Infrastructure
Bike lines
18.21
(26.18)
meters
Green
Infrastructure
Pedestrian lines
10.84
(5.76)
meters
Green
Infrastructure
Open green spaces
0.00
(0.00)
m² green public and
private spaces
Variables that
correlate with
decrease of
land values
Mobility and
accessibility
Distance to CBD
-7.97
(3.51)
meters
Mobility and
accessibility
Public transportation
stops
-997.06
(952.66)
number of stops
Security Robberies
-1208.13
(423.75)
number of robberies
Environment Noise pollution
-2097.18
(1385.28)
decibels
Security Homicides
-3149.68
(833.95)
number of
homicides
Note: Std. Error in parentheses

49
Table 10: Regression Output HPM2 at Block Level. Variables are ordered in descending order
according to coefficients.
Note: Std. Error in parentheses
Table 11: Regression Output HPM3 at Block Level. Variables are ordered in descending order
according to coefficients.
HPM3
Variable
Indicator
β Coefficient
Unit
Variables that
correlate with
increase of land
values
Socioeconomic Cultural amenities
39092.23
(8258.13)
number of
locations
Mobility and
accessibility
Distance from
secondary central
locations (less than
26816.08
(3429.98) meters
HPM2
Variable
Indicator
β Coefficient
Unit
Variables that
correlate with
increase of land
values
Mobility and accessibility
Public transport
efficiency
656394.36
(21511.32)
scale 1–5
Mobility and accessibility
Public roads quality
308599.65
(12580.51)
scale 1–5
Green Infrastructure
Exposure to fluvial
flooding (EFF)
19338.91
(4528.32)
yes/no
Socioeconomic Cultural amenities
31566.9
(4086.27)
number of
locations
Socioeconomic
Commercial
activities
1836.49
(120.73)
number of
locations
Security Robberies
723.87
(70.24)
number of
robberies
Green Infrastructure Number of trees
307.16
(88.38)
number of trees
Green Infrastructure Bike lines
92.27
(33.79)
meters
Green Infrastructure Pedestrian lines
27.17
(14.18)
meters
Socioeconomic Floor space index
26.52
(4.01)
m²
Green Infrastructure
Vegetation
coverage
2.67
(0.35)
m² green public
and private spaces
Variables that
correlate with
decrease of
land values
Mobility and accessibility
Distance to CBD
-20.21
(0.66)
meters
Environment Noise pollution
-1169.8
(202.22)
decibels
Security Homicides
-1896.59
(136.07)
number of
homicides

50
1 km)
Socioeconomic
Restaurants and
hotels
13526.23
(3005.48)
number of
locations
Socioeconomic
Commercial
activities
2052.91
(685.55)
number of
locations
Safety Robberies
1035.63
(156.52)
number of
robberies
Variables that
correlate with
decrease of land
values
Mobility and
accessibility
Distance to CBD
-4.07
(1.84)
meters
Environment Noise
-1911.97
(412.01)
decibels
Safety Homicides
-2422.28
(279.67)
number of
homicides
Green Infrastructure
Exposure to fluvial
flooding
-31778.29
(7257.79)
yes/no
Note: Std. Error in parentheses
5.2 Predicting the Potential Impact of the CAU Cañaveralejo on Land Value
This section will discuss the potential impact of the CAU Cañaveralejo project on land values in
adjacent properties. In the previous sections, this study explored the impact of variables such as
mobility, socio-economic factors, safety, environmental aspects and GI features on land values
across Cali. After the HPM results, it was possible to calculate the correlation (regression
coefficients) of specific GI elements on land value. Using these coefficients and the
quantifications of the planned changes to these GI elements, suggested by the CAU Cañaveralejo
design, we can predict the monetary impact of the project on the areas adjacent to the
Cañaveralejo interventions. Three predictions are made based on each HPM and the significant
variables found.
Prediction Based on HPM1
This prediction is performed at the neighborhood level, for the two significant variables of
HPM1: number of trees and pedestrian streets. Table 12 shows that the potential impact that
CAU can have varies depending on the neighborhood that is being observed, based on how each
neighborhood is benefitted by the project. Not all neighborhoods are affected in the same way.
Depending on their location, some neighborhoods will benefit by two or four features included in
the CAU Cañaveralejo project, which would reflect a higher impact in the average price per m²
of land in that neighborhood.
Looking at the forecast results, not per neighborhood but per variable, we also observe
differences on how they can affect land values. For example, “number of trees” is the variable
that scored the highest regression coefficient among the GI variables in the HPM1 model,
indicating its significance at the city level. However, the number of new trees that are proposed
by the CAU Cañaveralejo project is small, and they would be concentrated in one area. Thus, the
impact of this variable is finally very low, providing just $62,660.64 COP of added value. The

51
impact of the new pedestrian streets was overall the highest, providing an added value of
$127,769.64 COP.
The percentage shown in the last column of the prediction table is the proportional increase in
comparison with the actual average price per m² of land. For example, if the CAU Cañaveralejo
project is implemented, the intervened area in the neighborhood Cuarto de Legua - Guadalupe
would have an increase in its land value of 1.0%, and so on. This number is based on the original
price of land in each neighborhood. Therefore, if the absolute impact of the CAU project is large
in comparison to the impact in other neighborhoods, but its original average land price per m² is
high, the percentage of increase could be low.
Table 12: Prediction of Land Value Increase Based on HPM1. Total increase of value 2.5%.
Source: Authors
Prediction Based on HPM2
This prediction is performed at the block level for the significant variables of HPM2 (number of
trees, vegetation coverage, pedestrian streets, bike lanes) and for blocks having changes on GI
variables due to the implementation of the CAU Cañaveralejo project. The impact of the
exposure to fluvial flooding on land values, although significant, is not calculated. According to
its coefficient in HPM2, increased exposure to fluvial flooding correlates with higher land
HPM 1 Summary
Cuarto de Legua - Guadalupe $ - $ 3,961 $ 1,190,452,275 $ 11,331,842 1.0%
Nueva Tequendama $ 3,916 $ 7,941 $ 1,270,257,625 $ 34,974,024 2.8%
Camino Real - Joaquín
Borrero Sinistera
$ 3,916 $ 9,152 $ 982,412,800 $ 31,447,338 3.2%
El C olise o $ - $ 2,890 $ 648,042,500 $ 3,943,342 0.6%
Canaveral $ - $ 3,499 $ 714,116,006 $ 6,767,711 0.9%
Sect. Cañaveralejo
Guadalupe Antigua
$ - $ 16,917 $ 1,786,766,230 $ 110,868,043 6.2%
U.D.A. Calindo Plaza de
Toros
$ - $ 5,338 $ 1,114,470,000 $ 13,219,677 1.2%
Belisario Caicedo $ - $ 1,278 $ 394,138,125 $ 2,184,534 0.6%
Venezuela - Urb
Cañaveralejo
$ - $ - $ 446,333,408 $ - 0.0%
TOTAL $ 7,832 $ 50,976 $ 8,546,988,969 $ 214,736,511
Land value
increase
(%)
Land value incre ment
Adde d v alue
due to ne w GI
Neighborhood
Trees
Pe de s trian
Line s
Current land value
per ne ighborhood
(COP)
Adde d v alue pe r
neighborhood
(COP)

52
values. However, in HPM3 this variable was analyzed further, and when detached from the
feature “eye contact with water,” its coefficient was negative. For this reason, the prediction of
its monetary impact on land values is not be calculated for HPM2, but only for HPM3.
Table 13: Summary of the Prediction of Land Value Increase in Million COP Based on
HPM2. (Predictions per block in Annex 6).
Source: Authors
HPM 2 Summary
Cuarto de Legua -
Guadalupe $ - $ 942 $ 9,928 $ 16,822 $ 9,231 2.2% $ 483.0
Nueva Tequendama $ 31,330 $ 50,465 $ 19,903 $ 2,155 $ 17,309 4.0% $ 239.5
Camino Real - Joaquín
Borrero Sinistera
$ 31,330 $ 22,905 $ 22,940 $ 3,313 $ 16,098 3.9% $ 752.3
El C oliseo $ - $ 24,629 $ 7,245 $ 5,743 $ 18,808 4.0% $ 394.4
Canaveral $ - $ 38,613 $ 8,769 $ 267 $ 23,824 6.5% $ 154.0
Sect. Canaveralejo
Guadalupe Antigua
$ - $ 96,267 $ 42,402 $ 5,199 $ 23,978 8.8% $ 3,353.7
U.D.A. Calindo Plaza de
Toros
$ - $ 82,334 $ 13,379 $ 17,951 $ 113,665 8.4% $ 1,753.5
Belisario Caicedo $ - $ 27,040 $ 127,770 $ 11,432 $ 10,419 4.5% $ 180.7
Venezuela - Urb
Canaveralejo
$ - $ 8,411 $ - $ 39,065 $ 2,793 2.0% $ 484.8
TOTAL $ 62,661 $351,607 $252,335 $ 101,946 $ 236,124 $ 7,795.8
Land value
increase
(COP/m²)
Land value
increase
(%)
Adde d v alue
per bloc k
(million
COP)
Adde d v alue due to ne w GI (in COP)
Land value incre me nt
Neighborhood
Trees
Bik e lane s
Pe de s trian
stre e ts
Vege tation
coverage

53
Prediction Based on HPM3
Table 14: Summary of the Prediction of Land Value Increase in Million COP, Based on
HPM3. (Predictions per block in Annex 7).
Source: Authors
HPM 3 s umma ry
Jorge Zawadsky Canal part 1,404 1.0% $ 44.6
La Selva Canal part 17,277 5.0% $ 549.0
Departamental Canal part 201 0.1% $ 6.4
Panamericano Canal part 7,446 4.0% $ 236.6
Las Granjas Canal part 9,511 12.0% $ 302.3
San Judas Tadeo I Canal part 27,021 2.0% $ 858.7
Primero de Mayo Canal part 22,625 3.0% $ 719.0
Santa Anita - La Selva Canal part 25,288 2.0% $ 803.6
Canaverales - Los Samanes Canal part 19,382 4.0% $ 615.9
El Limonar Canal part 12,431 2.0% $ 395.1
Urb . Militar River part 18,857 2.0% $ 599.2
Cuarto de Legua - Guadalupe River part 3,505 2.0% $ 111.4
Nueva Tequendama River part 28,642 1.0% $ 910.2
Camino Real - Joaq uín Bo rrero S inistera River part 15,374 1.0% $ 488.6
Camino Real - Los Fundadores River part 1,865 0.3% $ 59.3
El C oliseo River part 3,734 1.0% $ 118.7
Canaveral River part 6,632 1.0% $ 210.8
Sect. Canaveralejo Guadalupe Antigua River part 53,183 2.0% $ 1,690.1
U.D.A. Calindo Plaza de Toros River part 13,492 0.3% $ 428.8
Belisario Caicedo River part 17,004 2.0% $ 540.4
Venezuela - Urb Canaveralejo River part 30,488 1.0% $ 968.9
TOTAL $ 10,657.3
Flood Ris k re duction
Land va lue increme nt
Neighborhood
Proje ct part
Are a of
flood risk
reduction
(m2)
Land value
increase
(%/m²)
Land value
incre ment
(million
COP)

54
Table 15: Prediction Overview of Land Value Increase Due to Flood Risk Reduction Based
on HPM3.
Current land
value (million
COP)
Value increase
(million COP)
Increase
(%)
Canal Part
$201,814
$4,763
2.4%
River Part
$2,088,619
$26,589
1.3%
Case study area (total)
$2,290,433
$31,353
1.4%
Source: Authors
Total Land Value Increase per GRI Variable
The three HPM models were run with aggregated data of the official data of the POT database,
and with the limitations that were discussed in the data collection section (different return years,
different levels of detail, missing risk data for some of the rivers). Exposure to flooding was
introduced in the models as a dummy “yes/no” variable: yes, where there is risk of any level, and
no, where there is not any risk of flooding indicated in the POT flood risk maps. In addition,
based on the interview with the hydraulic engineer / flood risk expert, we compiled an integrated
flood risk map for the city of Cali, that summarizes the flood risk in three overall levels: low,
medium and high (see image 7). Nevertheless, while reviewing this map, the expert confirmed
that the areas that we have indicated with “yes” in our models are areas with “medium” level of
risk in the aggregated map, while the areas indicated as “no” are areas with “low” level of risk,
and therefore the difference between the two variables reflects a level of flood risk. Regarding
risk reduction, the expert concluded that we can state confidently that the interventions of the
project will reduce the flood risk at the case study area (around the CAU Cañaveralejo
intervention and along the canal) from medium to low level due to:
1. The CAU Cañaveralejo project characteristics: increase of permeable surfaces, new trees,
water storage in bike lanes, floodplain.
2. The improvement of the canal (gray infrastructure) which is renovated to have better
water carrying capacity.
3. Other municipal actions aimed at improved as waste management.
4. Moreover, the rivers that have been included in our HPMs are the ones with the highest
risk of flooding as they are natural rivers without dikes.

55
Image 11: Main Factors Responsible for the Reduction of Flood Risk (from Medium to
Low) Around CAU Cañaveralejo.
As a result, we conclude that we can use the outcomes from HPM3 to calculate how the land
values along the river and canal will be increased by flood risk reduction due to the project and
additional flood risk reduction interventions at the area.
Overall, we observe that the number of trees and the flood risk are the only GI variables that are
highly significant in two out of the three HPM models, while exposure to flood risk is also found
as significant twice, but as its coefficients indicate, is highly controversial ranging from very
positive to very negative.
The effect of GI is more visible at the block level, and of flood risk in a more detailed analysis
within the block level, where the positive impact of being in eye contact with water can be
separated from the risk of flooding.
The next illustrations show the total land value that is expected to be increased due to the GRI
attributes of the CAU project, namely bike lanes, pedestrian streets, trees, vegetation coverage
and flood risk reduction. As shown in figure 5, out of the GI attributes, bike lanes would have the
largest impact to the land values of the overall project area.

56
Figure 5: Expected Land Value Increase, in COP, Due to the GI Attributes of the CAU
Cañaveralejo Project Based on HPM2.
Source: Authors

57
Image 12: Overall Increase of Land Value Due to the GI and Flood Risk Attributes of CAU
Project.
Source: Authors
Figure 6: Land Value Increase, in COP, at the River Part of the CAU Project Due to GI
and Flood Risk Reduction Interventions Based on HPM2.
Source: Authors
Regarding expected land value increases at the river portion of the project, we estimate that the
GI interventions will have larger impact than reduction of risk. The ratio of impact to land values
between GI attributes and flood risk reduction along the river is about 4/3.

58
Figure 7: Land Value Increase, in COP, in the Case Study Area Due to GI Attributes and
Flood Risk Reduction Interventions Based on HPM2 and HPM3.
Source: Authors
Regarding the expected land value increase in the overall project area, flood risk reduction
results to a higher Regarding the expected land value increase in the overall project area, flood
risk reduction results in a higher impact than the GI elements of the project. The ratio of land
value impact between GI attributes and flood risk reduction in the whole project area is about
4/5.
It is important to note that the values resulted in these prediction calculations reflect part of the
overall value of all the sustainability and resilience benefits that open spaces and ecosystem
services provide to the neighborhood and the city. The SRBA applied during the first
stakeholders’ workshop indicated many of these benefits (Annex 1). Due to quantification and
monetization challenges of some of the other sustainability benefits, we can conclude that the
values would be higher than predicted if all these benefits were included in the assessment and
valuation.
The results of this forecast analysis show the potential effect of the CAU on the land value of the
areas to be intervened. It also provides relevant information about which GI attributes affect the
most, which can be used to reconsider and increase their presence in the project. The use of these
results can be beneficial for private stakeholders and public actors since they quantify the
potential increase in land value that could be directly related to a potential increase in property
taxes, future municipal revenues, and the time it would take to recover the initial investment.

59
Figure 8: Spatial Distribution of the CAU Cañaveralejo GRI Project Interventions and
Land Value Increment Per Neighborhood
Source: Authors (link to high resolution image: https://issuu.com/alex.ts./docs/benefits_canaveralejo)
5.3 Possible Application of Land Value Capture Instruments
Feasibility Assessment of LVC Instruments
In November 8, 2016, participants of the workshop concluded that the “aportes por
edificabilidad” (charges on development rights) would be the most effective, suitable LVC
mechanism to finance green resilient infrastructure and conventional hard engineered
investments in risk reduction in the Cañaveralejo river. This was a consensus and reflected the
political concerns about past corruption.
In addition to “aportes por edificabilidad”, participants considered other types of LVC
instruments, including valorization contributions (betterment levies); the model of “plusvalías”
implemented in Bogotá and other cities; “valorización predial” (property tax reassessments), land
value taxation, which could work in Zone 5, the hard engineered Cañaveralejo canal that
channels rainwater and wastewater to Cauca river after being treated; and the “fondo de
adaptación” (climate adaptation fund), currently used as financing mechanism in plan Jarillon,
could be used to support Cali’s hydraulic capacity and flood risk mitigation strategies.

60
Participants recommended the “aportes por edificabilidad” because, as an incentive-based rather
than tax-based system, it could attract private investment. Public-financed investment in
effective green resilient infrastructure could be a catalyst that inspires confidence from private
investors in the areas along the Cañaveralejo. The “aportes por edificabilidad”, established in the
POT 2014, could be an incentive to invest in the area and develop related projects, which would,
in theory, increase land values.
In contrast, participants, including local stakeholders, considered tax-based approaches as less
effective. This was in reaction to the negative experience of the municipality of Cali’s 2009 tax
for “21 Mega-obras,” the anticipated 5-year construction of 21 mega-projects.
Workshop participants expressed an interest in involving the private sector in flood risk
reduction in support of initial investments by the public sector on a “voluntary” basis by offering
incentives rather than “punitive’ measures such as taxation and plusvalías, which risk having the
effect of discouraging investment in the Cañaveralejo corridor. “Aportes por edificabilidad” is a
discretionary zoning mechanism that allows the public sector to negotiate with private
developers to build or fund the construction of public space and infrastructure, including green
resilient infrastructure, in exchange for the rights to build at a higher density. The examples of
MioCable and Spain library in San Domingo Medellín demonstrates the effectiveness of “aportes
por edificabilidad” (with proper public sector management—a critical component of the success
of these projects). In effect, “aportes por edificabilidad” theoretically (and empirically based on
the examples in Medellín and Cali’s MioCable) triggers a “virtuous circle” of beneficial
investment in urban development and in resiliency.
Valorization (betterment contribution)
Valorization is a type of tax that finances the cost of a public project by creating a proportional
levy on all those who benefit from the project.
Strengths
Weaknesses
• The contribution imposed on residents is
divided among affected properties and is
calculated in proportion to the benefit
they receive.
• This levy can be applied before, during
or after construction to recover the cost
of a project.
• It is a cost recovery mechanism, not an
income generating instrument.
• Cali voters are skeptical of valorization
taxes based on past experiences of
corruption in municipal government,
where the planned projects were not
fully implemented.
Plusvalías (unearned increments)
Plusvalías are taxes defined by law as the main financing instrument for urban interventions in
Colombia. They reflect the estimated difference between the commercial value of property
before and after the intervention.

61
Strengths
Weaknesses
• The tax rate is informed by the
socioeconomic conditions of property
owners.
• It is a strong income generating
mechanism for local governments.
• Plusvalías are challenging for voters to
understand as their impacts are visible
over a longer period.
• Property owners pay plusvalías when
they apply for building permits, so
informal settlements are excluded.
• To date, Cali has never implemented
plusvalías.
• Cañaveralejo is not indicated as an area
where plusvalías can be implemented in
the land use plan.
“Aportes por edificabilidad” (Density bonus, or charges on additional building rights)
In the land use plan, a provision allows additional density above the ‘base construction index’ to
be awarded to developers in exchange for providing amenities, such as public open space.
“Aportes por edificabilidad” is a mechanism that allows the public sector to negotiate with
private developers to build or fund the construction of public space and infrastructure, including
green resilient infrastructure, in exchange for the rights to build at a higher density.
Strengths
Weaknesses
• “Aportes por edificabilidad”
theoretically and empirically (based on
the examples in Medellín and Cali)
triggers a “virtuous circle” of beneficial
investment in resilient urban
development.
• This instrument is perceived by
stakeholders as an incentive rather than a
punitive measure.
• General challenges as all LVC
instruments in Cali
Challenges for the Implementation of LVC in the CAU Project
According to the workshop participants the following challenges were identified for the LVC
implementation in the CAU project area:

62
Lack of Trust / Willingness to Pay Taxes
To implement LVC, which can have benefits in terms of financing resiliency, the municipal
government needs to generate public confidence. The prior experiences with corruption make the
public skeptical about the capacity of government to administer plusvalías programs or
valorization taxes. Government will need to make an upfront investment in pilot projects and
other initial interventions to inspire public confidence.
Coordination of Many Stakeholders
Due to the large number of stakeholders, resiliency projects in Cali will require the establishment
of a public agency or institution to coordinate the large number of institutional stakeholders and
manage projects in an integrated way to respond to both flood risk reduction as well as land
management and urban development. This capacity or function does not currently exist in Cali.
One alternative could be a collaboration in which stakeholders (including public agencies and
other institutions) collectively finance initial projects without using LVC or asking citizens to
pay through valorization. This could be a first step in a much broader program of “aportes por
edificabilidad.”
Low Investment Costs
The relative low cost of the CAU Cañaveralejo project does not need additional or alternative
financing because, at present, CVC and DAGMA have the public funding to support this project.
For this reason, LVC instruments for project financing are not being considered. Future stages of
construction, however, might require additional financing to supplement the current funding
allocation. The project benefits from a program in which private companies and institutions with
properties along the CAU Cañaveralejo adopt and maintain public spaces (largely green spaces),
in some cases, in exchange for tax reductions by the city (DAGMA).
6. Discussion and Concluding Remarks
Our analysis identifies the urban factors that affect land values across Cali. The block level
analysis confirmed the positive and high significant impact of GI on land values in line with the
findings of Wise et al. (2010) and Clements et al. (2013). A relevant result from this block
analysis was that EFF, contrary to the results from (Bin et al. 2008; Pope 2008; Pryce et al. 2011)
scored a highly significant and positive impact on land value. The cause of these results was
directly connected with an interference between the exposure to flood risk and contact with a
water body indicator, also discussed by Daniel et al. (2009). To understand the relation among
these two variables, we carried out a third model (HPM3) where contact to bodies of water was
controlled. This analysis demonstrated that a block directly in contact with a river with no risk of
flooding accounts for a positive impact on the land value due to the pleasant view that the river
provides (Luttik 2000; Daniel et al. 2009). However, if a block has direct contact with a river
with flood exposure, the positive impacts related with the pleasant view are not large enough to
compensate the negative impact of being in risk of flooding, which leaves a negative impact on
the land value.

63
The hedonic results provided the regression coefficients for each one of the GI variables used to
predict CAU’s potential impact on land value if implemented as projected. The results showed
an overall increase of 7.8 billion COP distributed across 48 blocks in 9 neighborhoods, meaning
an average increase of 5.4% (table 13) on the land value in the intervened area due to the GI
attributes and 1.4 % due to the flood risk reduction.
Currently, the presented estimations are being updated to cover the complete extent of the study
area and quantify all the benefits that will affect land values. In addition, the total amount of the
added land value at the neighborhoods surrounding the Cañaveralejo river is being calculated,
based on the area (square meters) that are affected by each significant factor identified through
the regressions.
The obtained results in the forecast analysis show the potential effect that the CAU project can
have on the land value of the areas to intervene (both in absolute values and in percentage). It
also provides relevant information on which GRI attributes affect land value the most, which can
be used to reconsider and increase their presence in the project. Furthermore, the results of the
forecasting provide information about the level of land value increase due to the GI attributes of
the project and due to the flood risk reduction (figure 7). The ratio of land value increase due to
the GI attributes and flood risk reduction is approximately 4/5. Since the canal part of the project
would probably require higher investment, (part of) this investment could be covered from
capturing the land value increase due to the GRI attributes of the project. The use of these results
can be relevant and beneficial to private stakeholders and public agencies since they show the
potential land value increases in land value that could be captured by potential increases in
property taxes, future municipal revenues and the time it would take to recover the investment
cost of the project.
6.1 Policy Recommendations
This section discusses how the research results can support and be turned into public policy. The
positive correlation between GI and land value across Cali found in this study can work as
valuable hints for real estate developers and government stakeholders on land investments.
Moreover, the application of these findings in public policy can help prevent the tradeoffs
between densification and green urban spaces commonly seen in cities without awareness of GI
benefits. The results presented in Chapter 5 conclude that GI has a significant positive impact on
land value across Cali in addition to the impacts from control variables such as mobility or
safety. This finding can be the basis of policy framework for GI, where green spaces can be
protected from the tradeoffs of urban growth and sprawl (Philipsen 2015). It would also be
necessary to take into account the potential impact of increasing GI to a higher level in areas
with a GI deficit. According to the preliminary results from the tree census carried out by
DAGMA and CVC, 17 out of the 22 communes in Cali are below the percentage of trees per
inhabitant suggested by the World Health Organization (Cali et al. 2016), meaning an
approximate deficit of 470,000 trees in the city. Communes 20, 21, 1, 14 15 and 18 are the ones
with the highest tree deficit as a consequence of the uncontrolled urban expansion and population
growth. Therefore, these communes would be the best option to start a GI intervention with
potentially better outcomes in comparison with the ones that could be obtained in communes 17

64
and 19 that have no tree deficit.
After controlling for contact to bodies of water, as suggested by Daniel et al. (2009) the variable
“exposure to flood risk” showed to have a negative impact on land value, which needs to be
approached besides the local interventions suggested by CAU. According to Konrad (2003),
Sampson (2008) and Pineo (2009) permeable surfaces can reduce runoffs and increase the
absorption capacity, reducing fluvial and pluvial flood risk. This means that a program to
promote changes in the existing impermeable surfaces plus a reform in the construction
regulations for new developments to include permeable surfaces instead of traditional non-
permeable pavements would be beneficial for reducing flood risk.
Among the multiple benefits that GI has (Bottalico et al. 2016; EPA et al. 2014; Van Den Berg et
al. 2015), its potential to minimize crime is something to take into consideration for Cali’s
policy, especially in neighborhoods like Siloé, Mojica y Potrero Grande, which have the highest
crime rates (Cali 2010). In the case of trees, Donovan and Prestemon (2012) pointed out a crime
reduction correlated with them, since thieves feel a tree implies a property “is better cared for
and, therefore, subject to more effective authority/vigilance than a comparable house with fewer
trees.” Also, neighborhoods with higher levels of public trees, vegetation and illumination
motivate people to walk more, which has an impact in crime since more people walking means
the area is safe, which creates a positive feedback loop attracting more people and giving a
higher sensation of safety (Gehl 2010; Litman 2014).
GI and mobility were the variables that have the highest impacts on land value, representing an
opportunity to explore them in a more integrated way. An option could be to combine the river
network with green corridors and a sustainable public transport system parallel to the GI
network. This GI-transport network could guarantee access to the CBD even from the farthest
points in the city, but more importantly, it could work as a catalyzer for job creation, urban
development and accessibility to amenities and social housing.
Sustainable transport, in combination with a green/blue network of infrastructure across the
seven rivers of Cali, can become a green transport-oriented development (GTOD) by generating
economic corridors and boosting urban growth in the city. Their impact as a whole network
would be “more than the sum of its parts.” Therefore, all this growth would generate revenues
that could be collected through land value capture instruments and betterment taxations.
However, upgrading land without taking proper precautions with current owners could create a
problem as much as a solution.
Gentrification is a common externality when these types of policies are “successfully”
implemented. Using GI as a tool for land improvement without appropriate policy precautions
can enable stakeholders and people in privileged positions to act with impunity, displacing
lower-income homeowners, renters, and racial minorities. This happens because with the land
upgrading, neighborhoods become more expensive, forcing original owners to move out since
they cannot afford the increase in services cost, according to Harvey (2012). It is imperative to
take all the stakeholders into consideration when planning an integrated policy. If important
parties are not aware of the decisions taken, GI projects can turn into a green disguise for
gentrification, that far from helping the vulnerable sectors it can worsen their living situation.

65
However, there are options to prevent gentrification taking over, such as realty transfer tax, low
income housing tax and anti-speculation tax, which can be used as protection for local residents
and housing diversity (Rose 2015; PCAC 2015). The state has to ensure that developers stick to
these regulations, making them keep in mind that projects must offer property diversity,
including a percentage of affordable housing for the vulnerable sectors.
Institutional Challenges Regarding the Implementation of Land Value Capture
The challenges of implementing land value capture for conventional purposes in Cali have been
discussed previously in this report; implementation for the purposes of financing green
infrastructure and other resilience measures would almost certainly present additional
complexities. The same institutional challenges encountered in land value capture schemes
would potentially present impediments to a resilience-based land value capture system. This
study’s hedonic model may point to a dynamic private sector-driven land market in Cali with a
robust real estate market and rapid appreciation of land prices. Although such land appreciation,
in principle, generates new land value that can be captured, the Cali municipal government might
not have the capacity to leverage this. Research suggests that dismal fiscal performance in Cali
and other cities in Latin America constrain the capacity of local governments to manage and use
land as a financing mechanism and resilience building resource (Kostaras et al. 2015).
As noted earlier in the report, municipal governments in Colombia have been slow to adopt land
value capture despite national legislation.is a big political hurdle, in part, because of its
complexity, in concept and in terms of implementation, is hard to explain to voters. In place of
land value capture instruments, mayors in Cali have typically tried to implement a valorization
tax, which is easier for voters to understand and has a short-term impact while the benefits of
land value capture are longer term. As noted earlier, in 2008, Cali re-considered valorization but
was constrained in its capacity to collect enough valorization taxes in the initial phases of the
mayor’s ambitious initiative to build 21 macro-projects (“mega-obras”) to finance projects in the
subsequent phases.
Consequently, valorization invited criticism in many quarters. In terms of valorization tax, the
city only collected 12000 USD through valorization over a year and a half period (Pretel 2016).
Many landowners have not paid owed valorization taxes and the lowest stratum has been unable
to pay the levy. The number of paying residents, including those who initially paid the levy, has
waned in ensuing years, largely due to dissatisfaction in the progress of the large-scale projects.
The perceived evidence of an ineffective program is a disincentive for taxpayers to comply and
pay owed taxes.
To encourage upfront payment of the contribution levy, Cali has a policy in which the landowner
receives a 50% discount on the tax, the entirety of it paid in one lump sum. Despite the evident
benefit of this policy, it has not been well publicized to residents and has been minimally
utilized. Additionally, all revenues from valorization are applied to a designated fund that can
solely be used for public open space and parks. People in the expansion areas, particularly in the
south of the city, living in the new macro-projects largely subsidize, in effect, public open space
and green areas city-wide. This has presented potential political resistance to valorization.
Another obstacle to implementing the city’s land use plan through the aggressive use of land

66
value capture is the absence of a department in the city government that administers land value
capture, including the enforcement and collection of valorization taxes. The housing department,
which is currently charged with this task, with only two staff does not have the capacity for this
purpose.
Recommendations to improve the capacity of municipal government in Cali to administer and
implement land value capture initiatives in general, and in the construction of resiliency
infrastructure, include the following (Pretel 2016):
• Create an agency within government; delegate the power to implement and manage land
value capture programs to this agency. It should be responsible for distributing the
proceeds from land value capture to several city departments (transit, public works—not
just parks) for the purpose of financing their operations and executing projects.
• “Empresa Municipal de Renovación Urbana” (EMRU) is a mixed public-private urban
redevelopment agency established in Cali. EMRU receives no money from the municipal
government and is entirely lacking in resources. Municipal government could give
EMRU the budget and institutional capacity to undertake land value capture-funded
urban redevelopment projects, which could include green resilient infrastructure and risk
reduction projects (such as the Río Cañaveralejo project). The Municipal Infrastructure
Department in Cali’s government is responsible for land value capture, but it is
understaffed to administer the valorization tax program. In response, a separate team
within the municipal government could be organized with the explicit responsibility to
establish land value capture rates—a complex exercise subject to challenge and,
therefore, staff expertise in this area would be needed to respond.
• Empower a separate tax collect team. In Bogotá, Mayor Peñalosa, acting on the belief
that people will not pay taxes without some incentive or punitive measures, established a
blacklist of taxpayers in arrears and submitted it to banks, which denied credit to those
individuals.
A more complex analysis of land value capture as a means to directly finance green resilient
infrastructure might require a reconceptualization of the concept of land value in the context of
climate risk (Kostaras et al. 2015). In the case of Cali, specifically the Rio Cañaveralejo project,
the following factors should be considered:
The Concept of ‘Avoided Costs’ as an Analog for LVC in the Case of Urban Climate Resilience
As noted in the paper Financing Urban Climate Adaptation through Land Value Capture in
Latin America and the Caribbean (Kostaras et al. 2015):
Financing urban climate adaptation through land value capture, in some respects, requires
an inversion of the fundamental premise of the concept: rather than creating value,
investments in adaptation serve to preserve value that would otherwise be diminished or
paid. Some increment of the land value that is being preserved and protected by climate
adaptation interventions is mobilized as a source of funding to mitigate impact of
flooding and other climate-driven events.

67
In this formulation, the cost of GRI might be significantly less than the rebuilding and repair
costs to impacted properties and, therefore, constitute saved or avoided costs (Beecher 2011).
Using this paradigm, avoided costs might be reframed as ‘added value.’ Theoretically, this will
be reflected in land values. This has been tested in CAU Cañaveralejo GRI project and proved
that the project, due to its green and resilience attributes, could increase future land values by 5,4
and 1,4 % respectively. Therefore, in principle some increase of future land values can be
preserved by virtue of current investments in GRI measures and that increment is robust enough
to be captured for the purposes of financing GRI projects (Beecher 2011).
Property Insurance as a Proxy for Land Value
In theory, insurance is a useful proxy for determining the climate-impact on land values and, by
extension, the added or preserved increment of value that could be captured.
A critical determination is whether insurance is a timely proxy for risk assessment or, in fact,
lags in terms of signaling levels of risk and hazard information to local land markets. As noted in
Financing Urban Climate Adaptation through Land Value Capture in Latin America and the
Caribbean (Kostaras et al. 2015), efficient property markets, in theory, consider a broad range of
market risk factors that are reflected in the pricing of debt and equity investment and insurance
rates for residential and commercial real estate. An impediment to this analysis proved to be the
fact that insurance markets in Cali are not yet well developed as only a small percentage of the
Cali population (about 7–10%) has housing-related insurance (Annex 4). In theory, increasing
climate-related risks to property will be priced into the flood risk premiums and reflected in
current and future land values, forcing the reconsideration of land uses and insurance policies
(ULI 2013).
6.2 Concluding Remarks
This study has attempted to answer the following research questions through the application of
an integrated method of analysis combining HPM and GIS:
• Can GRI, particularly at-risk areas, increase real estate values in the Rio Cañaveralejo
corridor?
• What types of benefits can derive from GRI projects, apart from risk reduction, in the
case of the Rio Cañaveralejo project? Which benefits of GRI projects can impact land or
real estate values in Cali?
• How can resilience (risk reduction) and other benefits of GRI projects be captured using
Land Value Capture instruments? Can Colombia’s land value capture mechanisms for
general purposes be used effectively to finance GRI projects in the institutional context of
Cali?
Can GRI, particularly in at-risk areas, increase real estate values in the Rio Cañaveralejo
corridor?
The application of land value capture instruments to finance GRI and risk reduction has been
highlighted as a promising idea. For policymakers, however, the practicality of this concept

68
depends on a quantitative analysis that demonstrates that investments in green resilient
infrastructure and flood reduction will, in fact, increase land values and that the increment can be
captured to finance further investment on urban resilience. To date, there have been few
examples of the use of land value capture to explicitly fund investment in climate adaptation
measures in Latin America and the Caribbean, specifically GRI (Kostaras et al. 2015).
This study quantitatively demonstrates a useful increase in land values attributable to capital
investments in resilience and risk reduction in the CAU Cañaveralejo project in Cali, Colombia
— a necessary basis for any land value capture strategy. Land value increases are attributable to
investments in resilience measures such as the implementation of sustainable urban drainage
systems (SUDS), green corridors for flood management, restoration of natural floodplains, and
multifunctional public space for recreation and storm water management. As a policy
consideration, this dynamic has allowed for an exploration of mechanisms that capture the
additional increment of land value attributable to flood risk reduction and other measures
proposed in the CAU Cañaveralejo project from the perspective of Cali’s land and fiscal policies
and institutional context.
What types of benefits can derive from GRI projects, apart from risk reduction, in the case of the
Rio Cañaveralejo project? Which benefits of GRI projects can impact land or real estate values
in Cali?
In zones 1–3, the non-channelized “river” segment of Cañaveralejo, GRI including bike lanes,
pedestrian paths, trees and vegetation significantly increased land values in the communes
(neighborhoods) along these zones. Flood risk reduction in these zones further increased land
values. (See Figure 8: Spatial distribution of the CAU Cañaveralejo GRI project interventions
and land value increment per neighborhood). Risk reduction interventions also increased in the
communes (neighborhoods) in Zone 5, the Cañaveralejo canal corridor, the area where
investments have been made in hard engineered concrete channels without the benefit of added
green resilient infrastructure. This analysis suggests that added investments in green resilient
infrastructure in Zone 5, in the form of a hybrid a grey/green project, would increase land values
in these neighborhoods. In summary, investments in risk reduction through green resilient
infrastructure, hard engineered “grey” infrastructure, or a hybrid of both, will increase land
values in the Cañaveralejo neighborhoods and, consequently, a land value capture approach
could be used, in principle, to finance risk reduction.
How can resilience (risk reduction) and other benefits of green infrastructure resilience projects
be captured using land value capture instruments? Can Colombia’s land value capture
mechanisms for general purposes be used effectively to finance green infrastructure resilience
projects in the institutional context of Cali?
As a practical matter, the feasible land value capture mechanisms are limited by technical,
administrative and political challenges previously discussed in section 5.3. In fact, stakeholders
and other respondents conclude that LVC mechanisms that are incentive-based, such as “aportes
por edificabilidad”, will be more effective than LVC that uses taxes and betterment levies, such
as valorization, in generating resources to finance urban resilience.

69
Cali’s experience with land value capture, valorization in particular, echoes that of other Latin
American cities. In Cali, the framework of “institutional infrastructure” suggests that land policy
instruments could augment the capacity of local government to implement resilience strategies
and problems of limited institutional and technical capacity could be resolved. The challenges to
land value capture for conventional purposes become more complicated in the case of resilience
financing in Cali and other cities impacted by recurrent flooding and other climate-driven events.

70
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Appendix A: Stakeholders’ Workshop
At the initial stages of the research, a workshop was organized at Universidad del Valle in Cali
with stakeholders of the Cañaveralejo area to discuss environmental and socio-economic issues,
scrutinize the planned intervention and its impacts (benefits or tradeoffs) it would bring.
Participants of the workshop were community leaders, citizens, representatives of the municipal
environmental agency (DAGMA), representatives of the municipal water company (EMCALI),
the architects of the CAU Cañaveralejo project (CUNA) and university students.
Green collaborative mapping
During the workshop, the architect of the CAU Cañaveralejo presented the project and its design
elements, the network of bike lanes with underground rainwater storage, the controlled flood
plain, improved lighting features, as well as potential impacts on the environmental and social
conditions of the area, also in eventual flooding situations. The workshop’s participants went
through the project drawings in detail over big scale prints, where they could pinpoint risky,
problematic or good locations, explaining the reasons for this classification. This green
collaborative mapping exercise was part of a process to get to know the project before discussing
the potential sustainability and resilience impacts. The map was digitized and the outcomes,
presenting a participatory current situation analysis of the Cañaveralejo river, can be accessed at
the green maps website database3. Overall, three locations in the study area were prioritized from
the participants’ input as the most vulnerable to flood risk.
Identification of sustainability and resilience benefits
Following the collaborative mapping, participants engaged in a discussion on the CAU
Cañaveralejo project and its impacts, inspired by case studies on green infrastructure approaches
for flood risk reduction in Latin America. The outcomes of this discussion were captured using
the SRBA checklist—a list of possible environmental, social, economic and institutional benefits
as identifiers of expected impacts of urban green interventions. The workshop participants filled
in the checklist in three smaller focus group discussions and identified a short list of expected
positive impacts, which are mainly social, but also environmental, economic and institutional.
Regarding the impact on land and real estate values, participants anticipated the land values to
increase because of the area upgrading, the new facilities and better quality of life due to the
expected benefits. Although they considered this a positive outcome because their property
would gain additional value, it entailed a threat: the assignment of higher strata to the properties
in the area, leading to higher taxation. The residents agreed that they would like the value of real
estate to increase without impacting the land values, leading to gentrification; and without
impacting the amount of taxes they would have to pay.
3 http://www.opengreenmap.org/greenmap/rio-Cañaveralejo

79
Table A1: Benefits That Scored More Than 2.7/3 at the Workshop.
Environmental
benefits Water
Conservation of water resources
Stormwater retention
Resource
conservation
Conservation of natural resources
Biodiversity
Protection of biodiversity
Climate change
Improved microclimate regulation
Social benefits
Health, safety and
risk reduction
Reduction in heat island effect
Welfare
Support to community development
Improved quality of life
Improved recreational facilities
Promotion of environmental equity
Aesthetic improvements
Reduced foul smell
Improved quality of open public
spaces
Increased quantity of open public
spaces
Education &
capacity building
Enhanced educational services
Economic benefits
Growth
Increased investment opportunities
Reduced impact on land values
Institutional
benefits
Knowledge
Creation of awareness
Networks
Cooperation between multiple
stakeholders

80
Appendix B: Description of Lonja Creation
Description of the content and process of creating the “estudio del valor del suelo”, translated to
English as “study for the value of land”
Content of the Lonja document
Unlike previous studies, the Study of land value (Estudio del valor del
suelo) in Cali 2008 takes as a starting point for the land value analysis
“regulatory polygons” established in the five urban parts in which the city
was divided in the land use plan (POT) of the municipality of Santiago de
Cali, approved by agreement 069 of October 26, 2000, since in the
previous studies only those sectors or areas that had higher real estate
dynamics and also some commercial corridors were analyzed; i.e. it did
not cover the area of the whole city, as it happens in this study.
The basis for the allocation of market values for the areas defined in the
regulatory polygons are the land use characteristics (heights, occupancy
rates, indices of construction, insulation of land infrastructure, etc.) of
each normative polygon, as they are established in normative records. […]
The process of creating the Lonja
The study of the value of urban land in Cali 2008 was made by the
committee of appraisals of the market of real estate of Cali and Valle del
Cauca (La Lonja de Propiedad Raiz de Cali y Valle del Cauca), an
interdisciplinary group of professionals composed of twenty-seven
appraisers with extensive experience in this activity and belonging to the
National Register of RNA appraisers. For this purpose, groups of 2 or 3
appraisal committee members were formed, and the regulatory polygons of
each urban piece were distributed to the groups. For the determination of
the units’ market values, databases of transactions or the residual method
for lots of undeveloped land, were taken into consideration.
On the other hand, it is important to note that this study does not provide
specific values, but value ranges (minimum and maximum) of the
normative polygons, in order to cover different ranges of value presented
in the different sub-areas. This study delivers values for different sectors by
taking into consideration extrinsic factors (location, regulations,
stratification, basic public services, complementary services, etc.) but is
not considered a property appraisal since these values may vary depending
on the intrinsic characteristics (superficial extension, form, front, depth,
front-depth ratio, specific location, etc.) of each plot of land. The value of
urban land occupied by buildings of institutional type such as churches,
schools, governmental buildings, green areas, rivers, clubs and parks, are
not included in this stud as they are not considered tradable and therefore

81
not are not assigned commercial value.
It is also important to mention that this study focuses on one point in time,
no analysis of comparative values in time (no inflation is calculated on the
values) with previous years are included, although it is expected that future
studies may reflect also on this type of analysis. Finally, we should mention
that this study is based on the official regulations provided by the
administrative department of city planning, some of which have been
subjected to revisions or amendment processes by the same department.
Source: Translated by the authors from the Lonja de Propiedad Raíz de Cali y de Valle del Cauca. 2008

82
Appendix C: Damages Due to Past Flood Events in the Project Area
The study sought information on the frequency of extreme events in the area and the damages
incurred (in monetary terms) in the event, as well as the insurance company responsible for
payment.
Figure C1: Damage Costs from Past Flood Events (According to Insurance Companies)
Combining with secondary data acquired from DesInventar, a Disaster Information Management
System that holds a database of past disaster events, a total of 15 flood events were found from
2000 to 2013. According to DesInventar, the total damage losses from 2000 to 2013 in the study
area were approximately $253,510 USD (DesInventar 2017). It is important to note that there are
no cost damage figures for all the events. Therefore, this information is providing us the minimum
damage costs that have occurred due to past flood events. Furthermore, this damage costs
indication is provided by official sources (e.g. government, insurance companies). Considering
that in Cali there is not an elaborated flood risk insurance market, in conjunction with the fact that
the costs for rebuilding and recovering from flood disasters are bear by the citizens, these figures
underestimate significantly the real damage losses due to past flood events.

83
Appendix D: Insurance Survey
A survey was conducted targeting the insurance companies of Santiago de Cali, especially the
ones that provide services to households along Cañaveralejo river. According to the Colombian
Federation of Insurers; Fasecolda (Federación de aseguradores colombianos), Colombian
Association of Insurance Brokers; Acoas (Asociación Colombiana de Corredores de Seguros) and
the Insurance Information Institute, there are around twenty prestigious home insurance
companies that provide service in Santiago de Cali. The top ten for the past years were among the
targeted companies:
1. Suramericana de Seguros
2. Seguros del Estado
3. Mapfre
4. Seguros Bolívar
5. Allianz Seguros SA
6. Liberty Seguros
7. Previsora
8. AXA Colpatria Seguros S.A.
9. QBE Seguros
10. La Occidental
Multi-risk home insurance is a fairly complex tool because of the large amount of coverage
offered, which can complicate acquiring a policy. A home insurance policy may be different from
another, depending on the company that issues it or the needs of the insured. In any case, the
insured may find a suitable policy for their needs and financial capacity. Additionally, citizens are
able to create a custom-built package that includes individual preferences.
According to the survey conducted, although individual home insurance is becoming more
popular in Santiago de Cali, the percentage of the population with home insurance is very low,
barely reaching a range between 7 to 15 percent.
This can be corroborated with the statistics gathered from a study conducted by Fasecolda, which
shows that the percentage of households with home insurance in Colombia (including flood risk)
is less than 5%. Although this study was conducted in 2007. Using the statistical study led by
Statista4 an increment of up to 25% of households would be insured in Colombia by 2020. The
survey from Universidad del Valle also contributed to these findings as their study only found 7%
of the sample to have insurance.
4 http://www.statista.com/

84
Appendix E: List of Participants of Second Stakeholders’ Workshop in Cali

85
Appendix F: Prediction of Land Value Increase Based on HPM2
HPM2 Added value due to new GI (in COP) Land value increment
Neighborhood Block ID Trees Bike lanes Pedestrian
streets
Vegetation
coverage
Current
land value
(COP/m²)
Land
value
increase
(COP/m²)
Land
value
increase
(%/m²)
Current land
value per
block (COP)
Added value
per block
(COP)
Cuarto de Legua -
Guadalupe
191910121.2
0.0
942.0767
8040.4
1825.2
450000.0
10807.7
2.40%
4554549000.0
109309176.0
Cuarto de Legua -
Guadalupe
191965273.7
0.0
0.0
1887.5
14867.2
371666.7
16754.7
4.51%
8255164374.2
372307913.3
Cuarto de Legua -
Guadalupe
191919434.4
0.0
0.0
0.0
129.4
426666.7
129.4
0.03%
4548730787.8
1364619.2
Nueva
Tequendama
19214375.71
921.5
7508.0
1885.3
901.6
412500.0
11216.4
2.72%
578205328.2
15727184.9
Nueva
Tequendama
19215179.33
4914.6
15554.9
1138.4
0.0
425000.0
21607.9
5.08%
1791268205.4
90996424.8
Nueva
Tequendama
19215223.4
1228.6
15311.3
3741.0
3.8
425000.0
20284.7
4.77%
1648596055.7
78638031.9
Nueva
Tequendama
19216081.37
307.2
0.0
0.0
0.0
425000.0
307.2
0.07%
2109321635.0
1476525.1
Nueva
Tequendama
192123934.9
16586.6
9860.9
11367.9
1196.5
455000.0
39011.9
8.57%
541355538.1
46394169.6
Nueva
Tequendama
192127174.6
7371.8
2230.2
1770.1
52.7
441250.0
11424.8
2.59%
242140515.1
6271439.3
Camino Real -
Joaquin Borrero
Sinistera
19224375.71
614.3
0.0
0.0
0.0
400000.0
614.3
0.15%
1750284000.0
2625426.0
Camino Real -
Joaquin Borrero
Sinistera
19225949.52
614.3
0.0
0.0
0.0
400000.0
614.3
0.15%
1515878186.3
2273817.3

86
Camino Real -
Joaquin Borrero
Sinistera
192227174.6
30101.7
14602.7
22144.9
3313.4
441250.0
70162.6
15.90%
4571377462.8
726849016.6
Camino Real -
Joaquin Borrero
Sinistera
19223628.32
0.0
5372.0
530.6
0.0
400000.0
5902.6
1.48%
571566731.4
8459187.6
Camino Real -
Joaquin Borrero
Sinistera
19223765.74
0.0
2930.5
264.6
0.0
400000.0
3195.1
0.80%
1506296000.0
12050368.0
El Coliseo
198521658.2
0.0
21464.8
6120.6
4979.7
500000.0
32565.0
6.51%
5274596891.0
343376257.6
El Coliseo
198510121.2
0.0
3163.9
1124.0
763.2
450000.0
5051.2
1.12%
4554549000.0
51010948.8
Canaveral
199274454.1
0.0
25399.2
6546.9
29.8
338333.3
31975.8
9.45%
634053672.5
59918072.1
Canaveral
199218739
0.0
13214.0
2222.2
236.8
400000.0
15673.0
3.92%
2399131832.8
94045967.8
Sect.
Cañaveralejo
Guadalupe
Antigua
199574454.1
0.0
63276.9
14610.4
73.8
338333.3
77961.1
23.04%
7823323306.4
1802493689.8
Sect.
Cañaveralejo
Guadalupe
Antigua
199529247.8
0.0
16499.7
11991.5
1629.6
250000.0
30120.8
12.05%
1181481900.6
142368569.0
Sect.
Cañaveralejo
Guadalupe
Antigua
199544371.6
0.0
16490.5
14780.2
106.8
315000.0
31377.5
9.96%
13977076050.0
1392116774.6
Sect.
Cañaveralejo
Guadalupe
Antigua
19957468.17
0.0
0.0
1020.0
2164.7
315000.0
3184.6
1.01%
1268359170.7
12810427.6
Sect.
Cañaveralejo
Guadalupe
Antigua
199511064.4
0.0
0.0
0.0
1182.5
307500.0
1182.5
0.38%
906804680.8
3445857.8

87
Sect.
Cañaveralejo
Guadalupe
Antigua
19959365.01
0.0
0.0
0.0
41.3
110000.0
41.3
0.04%
1030151100.0
412060.4
U.D.A. Calindo
Plaza de Toros
199979611
0.0
42875.1
0.0
7448.6
450000.0
50323.7
11.18%
4552544486.2
508974473.6
U.D.A. Calindo
Plaza de Toros
1999188348
0.0
37072.2
0.0
2554.7
450000.0
39626.9
8.81%
11401880420.6
1004505665.1
U.D.A. Calindo
Plaza de Toros
199910121.2
0.0
2387.0
13379.1
7948.2
450000.0
23714.2
5.27%
4554549000.0
240024732.3
Belisario Caicedo
20022799.78
0.0
16360.4
589.3
3923.8
135000.0
20873.5
15.46%
377970300.0
58434208.4
Belisario Caicedo
200218602.4
0.0
5485.5
0.0
164.3
347500.0
5649.8
1.63%
4584825405.8
74732654.1
Belisario Caicedo
200229247.8
0.0
5193.9
2614.6
7216.8
225000.0
15025.3
6.68%
702288902.8
46912898.7
Belisario Caicedo
20025338.2
0.0
0.0
0.0
126.8
215000.0
126.8
0.06%
1084400178.0
650640.1
Venezuela - Urb
Cañaveralejo
20982334.57
0.0
8005.3
0.0
1553.6
110000.0
9558.9
8.69%
256802700.0
22316154.6
Venezuela - Urb
Cañaveralejo
20981913.25
0.0
406.0
0.0
723.0
110000.0
1129.0
1.03%
210457500.0
2167712.3
Venezuela - Urb
Cañaveralejo
20989365.01
0.0
0.0
0.0
11187.9
110000.0
11187.9
10.17%
1535165.0
156126.3
Venezuela - Urb
Cañaveralejo
209834175.4
0.0
0.0
0.0
7268.6
145000.0
7268.6
5.01%
456999845.4
22895692.3
Venezuela - Urb
Cañaveralejo
20985086.85
0.0
0.0
0.0
4247.7
110000.0
4247.7
3.86%
559553500.0
21598765.1
Venezuela - Urb
Cañaveralejo
209874454.1
0.0
0.0
0.0
3568.7
357500.0
3568.7
1.00%
26617347900.0
266173479.0
Venezuela - Urb
Cañaveralejo
209830695.4
0.0
0.0
0.0
3706.3
110000.0
3706.3
3.37%
477370057.9
16087371.0
Venezuela - Urb
Cañaveralejo
209844371.6
0.0
0.0
0.0
2733.2
212500.0
2733.2
1.29%
9428979875.0
121633840.4
Venezuela - Urb
20981138.16
0.0
0.0
0.0
1428.9
110000.0
1428.9
1.30%
125197600.0
1627568.8

88
Cañaveralejo
Venezuela - Urb
Cañaveralejo
20983908.54
0.0
0.0
0.0
676.3
110000.0
676.3
0.61%
223465943.5
1363142.3
Venezuela - Urb
Cañaveralejo
20982700.48
0.0
0.0
0.0
494.2
145000.0
494.2
0.34%
200680887.9
682315.0
Venezuela - Urb
Cañaveralejo
2098531.88
0.0
0.0
0.0
452.6
110000.0
452.6
0.41%
58506800.0
239877.9
Venezuela - Urb
Cañaveralejo
20983252.31
0.0
0.0
0.0
442.8
110000.0
442.8
0.40%
255205926.6
1020823.7
Venezuela - Urb
Cañaveralejo
20981878.35
0.0
0.0
0.0
280.9
145000.0
280.9
0.19%
138284903.4
262741.3
Venezuela - Urb
Cañaveralejo
209829247.8
0.0
0.0
0.0
215.1
135000.0
215.1
0.16%
3948462450.0
6317539.9
Venezuela - Urb
Cañaveralejo
20984035.85
0.0
0.0
0.0
67.8
145000.0
67.8
0.05%
395029386.9
197514.7
Venezuela - Urb
Cañaveralejo
20983116.78
0.0
0.0
0.0
17.9
145000.0
17.9
0.01%
348436358.2
34843.6
TOTAL
144,195,03
6,917.89
7,795,752,
705.57

89
Appendix G: Prediction of Land Value Decrease Based on HPM3
Neighborhood
Case study
area part Block ID
Current
land value
(COP/m²)
Current land
value per block
(COP)
Area
exposed
to flood
risk
(m
2
)
Increased
value per
block (COP)
Jorge Zawadsky
Canal part
10074337.22
1000000.0
4337220000.0
76.7
2436759.3
Jorge Zawadsky
Canal part
10074337.22
300000.0
1301166000.0
453.3
14405098.9
Jorge Zawadsky
Canal part
10073730.24
300000.0
1119072000.0
469.2
14910055.9
Jorge Zawadsky
Canal part
10073707.31
300000.0
1112193000.0
405.1
12874020.8
La Selva
Canal part
10106807.78
275000.0
1872139500.0
2869.3
91181447.5
La Selva
Canal part
10106782.76
275000.0
1865259000.0
2886.3
91722631.8
La Selva
Canal part
101019247.9
300000.0
5774391000.0
8202.5
260661423.7
La Selva
Canal part
10107217.31
275000.0
1984760250.0
3318.8
105466742.2
Departamental
Canal part
10115245.43
400000.0
2098172000.0
83.2
2644907.1
Departamental
Canal part
10115195.02
500000.0
2597510000.0
37.3
1183741.3
Departamental
Canal part
10115195.02
400000.0
2078008000.0
80.2
2549254.4
Panamericano
Canal part
10133579.08
1000000.0
3579080000.0
606.1
19261774.9
Panamericano
Canal part
10133579.08
400000.0
1431632000.0
2973.0
94475267.3
Panamericano
Canal part
10133866.52
400000.0
1546608000.0
3866.5
122871393.9
Las Granjas
Canal part
10169511.89
275000.0
2615769750.0
9511.9
302271598.9
San Judas Tadeo I
Canal part
101751422.6
225000.0
11570089500.0
150.3
4775005.9
San Judas Tadeo I
Canal part
101751422.6
210000.0
10798750200.0
1223.0
38864530.9
San Judas Tadeo I
Canal part
101711716.8
275000.0
3222125500.0
1934.7
61480822.1
San Judas Tadeo I
Canal part
10171710.01
275000.0
470252750.0
640.6
20357808.1
San Judas Tadeo I
Canal part
10172473.36
275000.0
680174000.0
2473.4
78599151.4
San Judas Tadeo I
Canal part
10171828.76
275000.0
502909000.0
786.7
24999345.2
San Judas Tadeo I
Canal part
10171700.01
275000.0
467502750.0
752.9
23926510.1
San Judas Tadeo I
Canal part
10174577.06
260000.0
1190035600.0
4149.3
131858294.3
San Judas Tadeo I
Canal part
10177832.01
260000.0
2036322600.0
5136.1
163215204.1
San Judas Tadeo I
Canal part
10178486.2
210000.0
1782102000.0
3382.1
107476719
San Judas Tadeo I
Canal part
10177587.67
275000.0
2086609250.0
3129.2
99441260.6
San Judas Tadeo I
Canal part
10171916.44
275000.0
527021000.0
1916.4
60901186.1
San Judas Tadeo I
Canal part
10171389.23
275000.0
382038250.0
1346.1
42775802.8
Primero de Mayo
Canal part
17022646.66
400000.0
1058664000.0
2646.7
84106329
Primero de Mayo
Canal part
17023329.56
400000.0
1331824000.0
837.5
26613682.3
Primero de Mayo
Canal part
17023420.34
400000.0
1368136000.0
3420.3
108692556.4

90
Primero de Mayo
Canal part
170211710.7
400000.0
4684296000.0
7507.7
238582821.2
Primero de Mayo
Canal part
170210989.6
400000.0
4395872000.0
5354.2
170147002.5
Primero de Mayo
Canal part
170210989.6
750000.0
8242260000.0
2082.4
66173840
Primero de Mayo
Canal part
17029404.52
400000.0
3761808000.0
776.4
24671075.4
Santa Anita - La
Selva
Canal part
177824278.2
400000.0
9711292000.0
3427.7
108926126.9
Santa Anita - La
Selva
Canal part
177824278.2
750000.0
18208672500.0
4430.8
140802294
Santa Anita - La
Selva
Canal part
177824140.2
400000.0
9656108000.0
8326.0
264587313.7
Santa Anita - La
Selva
Canal part
17786654.59
400000.0
2661836000.0
4891.0
155427934.2
Santa Anita - La
Selva
Canal part
17786560.92
500000.0
3280460000.0
4212.9
133878440.2
Canaverales - Los
Samanes
Canal part
17863940.82
275000.0
1083725500.0
3053.7
97039775.3
Canaverales - Los
Samanes
Canal part
178622544.5
275000.0
6199759500.0
2960.3
94073589.7
Canaverales - Los
Samanes
Canal part
178611716.8
275000.0
3222125500.0
6906.5
219477395.5
Canaverales - Los
Samanes
Canal part
17869008.1
275000.0
2477227500.0
3233.7
102762409.7
Canaverales - Los
Samanes
Canal part
17863911.71
275000.0
1075720250.0
3227.7
102569833.3
El Limonar
Canal part
178716423
400000.0
6569216000.0
3078.9
97842177.1
El Limonar
Canal part
17877044.94
400000.0
2817976000.0
3549.7
112803713.8
El Limonar
Canal part
178729563.1
400000.0
11825256000.0
5802.8
184403696.8
Urb. Militar
River part
191813136.9
425000.0
5583182500.0
5772.4
183438272.3
Urb. Militar
River part
191811064.7
450000.0
4979155500.0
4208.8
133747513.6
Urb. Militar
River part
19183177.59
400000.0
1271036000.0
8.2
261535.3
Urb. Militar
River part
19183177.59
425000.0
1350475750.0
2817.5
89535967.6
Urb. Militar
River part
191811278.6
425000.0
4793434750.0
3880.5
123316289.9
Urb. Militar
River part
191811278.6
1500000.0
16918005000.0
2169.2
68934737.8
Cuarto de Legua -
Guadalupe
River part
191965273.7
0.0
0.0
121.7
3866146.8
Cuarto de Legua -
Guadalupe
River part
191910121.2
450000.0
4554549000.0
101.6
3227085.4
Cuarto de Legua -
Guadalupe
River part
191910121.2
0.0
0.0
3282.0
104296030
Nueva Tequendama
River part
19215179.33
425000.0
2201215250.0
3065.1
97404907.8

91
Nueva Tequendama
River part
19216081.37
425000.0
2584582250.0
672.9
21384246.9
Nueva Tequendama
River part
192127174.6
465000.0
12636202950.0
687.8
21856154.5
Nueva Tequendama
River part
192127174.6
400000.0
10869852000.0
6.7
214185.7
Nueva Tequendama
River part
192127174.6
475000.0
12907949250.0
498.2
15830990.7
Nueva Tequendama
River part
192127174.6
425000.0
11549217750.0
12.5
397864.2
Nueva Tequendama
River part
192123934.9
465000.0
11129765700.0
3661.1
116344768.7
Nueva Tequendama
River part
192123934.9
475000.0
11369115500.0
4937.0
156889735.5
Nueva Tequendama
River part
192123934.9
425000.0
10172366500.0
2276.2
72332154.8
Nueva Tequendama
River part
19215223.4
425000.0
2219945000.0
5223.4
165990720
Nueva Tequendama
River part
19214375.71
400000.0
1750284000.0
54.6
1735412.4
Nueva Tequendama
River part
19214375.71
425000.0
1859676750.0
3050.7
96944440.4
Nueva Tequendama
River part
192115121.1
400000.0
6048464000.0
4496.2
142880276.4
Camino Real -
Joaquin Borrero
Sinistera
River part
192227174.6
465000.0
12636202950.0
1857.1
59016415.7
Camino Real -
Joaquin Borrero
Sinistera
River part
192227174.6
400000.0
10869852000.0
12938.7
411168489.7
Camino Real -
Joaquin Borrero
Sinistera
River part
192227174.6
475000.0
12907949250.0
18.0
572962.6
Camino Real -
Joaquin Borrero
Sinistera
River part
192227174.6
425000.0
11549217750.0
13.5
427735.8
Camino Real -
Joaquin Borrero
Sinistera
River part
19224375.71
400000.0
1750284000.0
4.8
150946.9
Camino Real -
Joaquin Borrero
Sinistera
River part
19223765.74
400000.0
1506296000.0
56.9
1809455.8
Camino Real -
Joaquin Borrero
Sinistera
River part
19223628.32
400000.0
1451328000.0
358.9
11405546.1
Camino Real -
Joaquin Borrero
Sinistera
River part
19222976.35
400000.0
1190540000.0
125.8
3997073.3
Camino Real - Los
Fundadores
River part
192313398.9
500000.0
6699455000.0
337.4
10722630.6
Camino Real - Los
Fundadores
River part
192319439.3
400000.0
7775744000.0
248.3
7889913.8
Camino Real - Los
Fundadores
River part
19233177.59
400000.0
1271036000.0
104.8
3330364.8

92
Camino Real - Los
Fundadores
River part
19233177.59
425000.0
1350475750.0
247.1
7850826.5
Camino Real - Los
Fundadores
River part
192314848
400000.0
5939212000.0
827.0
26279056.9
Camino Real - Los
Fundadores
River part
1923120.2
400000.0
48080000.0
100.5
3194989.3
El Coliseo
River part
198521658.2
500000.0
10829110000.0
292.2
9285934.1
El Coliseo
River part
198521658.2
0.0
0.0
3060.2
97248240.8
El Coliseo
River part
198510121.2
450000.0
4554549000.0
0.0
953.4
El Coliseo
River part
198510121.2
0.0
0.0
381.4
12119604.2
Canaveral
River part
199274454.1
0.0
0.0
1.3
42265.1
Canaveral
River part
199274454.1
400000.0
29781648000.0
6630.8
210714532
Sect. Cañaveralejo
Guadalupe Antigua
River part
199511064.4
300000.0
3319326000.0
90.2
2866719.5
Sect. Cañaveralejo
Guadalupe Antigua
River part
19957468.17
315000.0
2352473550.0
435.8
13848661
Sect. Cañaveralejo
Guadalupe Antigua
River part
199529247.8
300000.0
8774361000.0
124.9
3969426.2
Sect. Cañaveralejo
Guadalupe Antigua
River part
199529247.8
135000.0
3948462450.0
41.3
1313714.5
Sect. Cañaveralejo
Guadalupe Antigua
River part
199529247.8
315000.0
9213079050.0
9259.5
294249805.1
Sect. Cañaveralejo
Guadalupe Antigua
River part
199574454.1
400000.0
29781648000.0
190.2
6044230.8
Sect. Cañaveralejo
Guadalupe Antigua
River part
199574454.1
315000.0
23453047800.0
2612.0
83006164.6
Sect. Cañaveralejo
Guadalupe Antigua
River part
199544371.6
315000.0
13977076050.0
40419.3
1284455601
Sect. Cañaveralejo
Guadalupe Antigua
River part
19959365.01
110000.0
1030151100.0
10.1
319371.8
U.D.A. Calindo Plaza
de Toros
River part
199979611
450000.0
35824990500.0
4507.5
143240642.2
U.D.A. Calindo Plaza
de Toros
River part
199910121.2
450000.0
4554549000.0
5795.0
184155508.3
U.D.A. Calindo Plaza
de Toros
River part
199910121.2
0.0
0.0
523.3
16628625.8
U.D.A. Calindo Plaza
de Toros
River part
1999188348
450000.0
84756627000.0
2666.4
84732043.5
Belisario Caicedo
River part
20024606.99
295000.0
1359062050.0
718.5
22833972.5
Belisario Caicedo
River part
20025338.2
295000.0
1574769000.0
2276.3
72337557.1
Belisario Caicedo
River part
20025338.2
135000.0
720657000.0
2.3
72772.3

93
Belisario Caicedo
River part
20023881.04
295000.0
1144906800.0
1680.1
53391022.8
Belisario Caicedo
River part
200218602.4
400000.0
7440992000.0
3024.1
96101362.4
Belisario Caicedo
River part
20022799.78
135000.0
377970300.0
2799.8
88972220.8
Belisario Caicedo
River part
200229247.8
135000.0
3948462450.0
4825.0
153329931.5
Belisario Caicedo
River part
200229247.8
315000.0
9213079050.0
1678.0
53323017.3
Venezuela - Urb
Cañaveralejo
River part
209829247.8
0.0
0.0
0.0
0
Venezuela - Urb
Cañaveralejo
River part
209829247.8
135000.0
3948462450.0
12.1
384517.3
Venezuela - Urb
Cañaveralejo
River part
209830695.4
0.0
0.0
211.5
6722061.7
Venezuela - Urb
Cañaveralejo
River part
209830695.4
0.0
0.0
6.0
190352
Venezuela - Urb
Cañaveralejo
River part
209830695.4
110000.0
3376499500.0
11.7
372759.3
Venezuela - Urb
Cañaveralejo
River part
20983252.31
110000.0
357754100.0
510.0
16206292.3
Venezuela - Urb
Cañaveralejo
River part
20981138.16
110000.0
125197600.0
1026.0
32604843.3
Venezuela - Urb
Cañaveralejo
River part
20981913.25
110000.0
210457500.0
1909.2
60669522.4
Venezuela - Urb
Cañaveralejo
River part
20982334.57
110000.0
256802700.0
2334.6
74188642.5
Venezuela - Urb
Cañaveralejo
River part
20983908.54
110000.0
429939400.0
0.3
9533.5
Venezuela - Urb
Cañaveralejo
River part
209844371.6
315000.0
13977076050.0
2817.0
89519125.2
Venezuela - Urb
Cañaveralejo
River part
209844371.6
110000.0
4880883700.0
0.2
4766.7
Venezuela - Urb
Cañaveralejo
River part
20985086.85
110000.0
559553500.0
5086.9
161651394.5
Venezuela - Urb
Cañaveralejo
River part
20989365.01
110000.0
1030151100.0
9254.6
294095680.4
Venezuela - Urb
Cañaveralejo
River part
20984035.85
110000.0
443943500.0
108.3
3439999.9
Venezuela - Urb
Cañaveralejo
River part
209874454.1
0.0
0.0
16.1
510994.9
Venezuela - Urb
Cañaveralejo
River part
209874454.1
400000.0
29781648000.0
1765.7
56110291.1
Venezuela - Urb
Cañaveralejo
River part
209874454.1
315000.0
23453047800.0
842.4
26770984.8
Venezuela - Urb
River part
209834175.4
0.0
0.0
2283.1
72552378.3

94
Cañaveralejo
Venezuela - Urb
Cañaveralejo
River part
209834175.4
180000.0
6151584600.0
1140.3
36237419.7
Venezuela - Urb
Cañaveralejo
River part
209834175.4
110000.0
3759301700.0
14.8
471272
Venezuela - Urb
Cañaveralejo
River part
20982700.48
110000.0
297052800.0
890.4
28295071.6
Venezuela - Urb
Cañaveralejo
River part
2098531.88
110000.0
58506800.0
247.0
7850508.8
TOTAL
773384155700.0
10657277923