Mario Ortiz – Daniel Páez
lab.uniandes.edu.co
Context
Estimated investment: $25 billion
 Purpose of 4G concessions is to improve
national competitiveness….
…and extend national roads coverage
Communications investment /GDP
Transportation investment/GDP
Energy investment/GDP
National road network
Secondary road network
Tertiary road network
Privates
Source: National Infrastructure Agency (ANI)
Fuente: DNP, Ministry of transport. 2011
Context
 Primary funding for the 4G road
concessions would come from
priovatizacion of ISAGEN, the largest
power energy generator and owner
of the distribution system of the
country
Estimated sell value: $2.5 billion
Problem
 As opportunities for privatization
are reduced, developing nations
need to find new sources of funding
for public infrastructure
 All land value increases are a
consequence of national
development and public sector
investment
 Land value capture consist on
obtaining a portion of the benefits
cause by the new infrastructure on
land prices
 Several examples of land value
capture exist in urban areas
 There are opportunities to
investigate further land value
capture opportunities on rural areas
Problem
 Before land values can be
captured, a consistent,
defendable and practical
methodology is needed to
determine land value
increases due to the new
infrastructure
Infrastructure
scenarios
In this research
proposes a
methodology to
estimate land value
increments due to new
national road
infrastructure
Determination
of land value
increases
Develop land
value capture
mechanism
Objetives
Concessions
National
Roads
ESTIMATED KILOMETERS
INTERVENE
ESTIMATED TOTAL
INVESTMENT (BILLION / DIC - 11)
 To
estimate
economic
impact of new national
roads on land values
Current Concessions
Source: ANI
New Concessions
We focused on the new generation of national road concessons called 4G (forth generation)
Methodology
Design variables
and determine
model
Valorization
Analysis
according to 4G
Standard Linear
Regression
model (OLS)
Applying the
GWR Model
4G Simulation
Spatial Analysis
and Results
Based on: Páez & Currie (2012)
Previous experiences
With a similar
methodology we have
estimated increases of
land values for Bogota
Modeling with GWR
Geographically
Weighted
Regression (GWR)
Ordinary Least
Square (OLS)
 GWR provides a local model of
the variable to analyze, adjusting
the regression equation by entity.
 GWR uses the geographic
principle that things that are near
influence each other
Model
 _
= 0 +1 _ + 2 _ + 3 _ + 4  (_ )
+ 5 _ + 6 _ + 7 _ + 8 ℎ_ + 9 ℎ_
+ 10 
VARIABLE
NAME
SOURCE
YEAR
Dependent
land values
IGAC
2011
Explanatory
Rural Aqueduct Coverage
SSPD
2008
Explanatory
Industrial and Commercial Income
DNP
2009
Explanatory
Rural Electric Coverage
DANE
2005
Explanatory
School Non-Attendance
DANE
2005
Explanatory
Rural Unsatisfied Basic Needs
DANE
2010
Explanatory
Subsided Health Regimen Coverage
HealthMin
2010
Explanatory
Rural Population
DANE
2005
Explanatory
Main Road Averaged Area of Influence
OSM
2013
Explanatory
Secondary Road Averaged Area of Influence
OSM
2013
Explanatory
Tertiary Road Averaged Area of Influence
OSM
2013
Variables
RURAL UNSATISFIED BASIC NEEDS
(UBN) INDEX
100%
Coverage (%)
Rural power Coverage
80%
60%
40%
Quindío
Valle
Caldas
Guajira
Guainía
Vichada
Chocó
20%
0%
Departments
Rural potable water Coverage
Socioeconomic
Land values
 Rural Unsatisfied Basic Needs (UBN) Index
 Rural power Coverage
 Rural potable water Coverage
 Land values
Variables
Influence area of the road network by municipalities
Road Infrastructure
 Primary roads
 Secondary roads
 Tertiary roads
Road network influence radius through a
coverage area depending on the type of
road (Primary, Secondary, and Tertiary).
Average radius of 40 km per road
Primary roads
Secondary roads
Primary roads
Secondary roads
Tertiary roads
Tertiary roads
Results
 Representation of the model in 45% of
the real variation of the land value in a
rural zone.
OLS: Ordinary Least Squares 1
R² 45,6%
Regression Statistics
Multiple correlation coefficient (R)
R Square
Adjusted R Square
Standard Error
Observations
ANOVA - Variance Analysis
Degrees freedom (df) Square Sum (SS) Mean Square (MS)
 The problem with the model is related
to the influence of the variables in the
land value change in rural areas
globally, it does not allow to analyze
the inequality of land value in the
whole territory.
 Consequently, the variables without
statistical significance were removed,
resulting in a new model.
0.680051286
0.462469751
0.456793612
1.006832351
958
Regression
Residual
Total
10
825,9328241
82,59328241
947
959,9846797
1,013711383
957
1785,917504
F
Significance F
81,47613196 2,1907E-120
ANOVA - Variance Analysis
Interception
Primary roads
Secondary roads
Tertiary roads
Industrial and Commercial Income
Aqueduct Coverage
Electric Coverage
Rural Unsatisfied Basic Needs
School Non-Attendance
Health Coverage
Rural population
Coefficients
Standar Error
t Statistic
17,8152
0,1515
117,6078
Probability
-
0,1291
0,0218
5,9319
0.0000
(0,0150)
0,0157
(0,9611)
0,3367
(0,0016)
0,0027
(0,6001)
0,5486
0,0318
0,0060
5,2776
0.0000
0,0024
0,0020
1,1948
0,2325
0,0027
0,0020
1,3167
0,1882
(0,0111)
0,0028
(4,0262)
0.0001
(0,0600)
0,0097
(6,1973)
0.0000
0,0052
0,0026
2,0452
0.0411
0,0001
0,0000
16,3817
0.0000
 The model does not represent a
significant difference with respect to the
specification and statistical significance.
OLS: Ordinary Least Squares 2
R² 45,5%
 Without the insignificant variables there
is an explanation of 45% of the variation
of land value in rural zones.
 The OLS model does not allow the use of
variables that were considered important
in the beginning (influence of secondary
and tertiary roads, and the public utilities
coverage).
 The analysis of these variables is
important for a country with high
geographical diversity, this being the
reason they will taken into account in the
GWR model.
Regression Statistics
Multiple correlation coefficient (R)
R Square
Adjusted R Square
Standard Error
Observations
0.6775
0.4580
0.4555
1.0080
958
ANOVA - Variance Analysis
Degrees freedom (df) Square Sum (SS) Mean Square (MS)
Regression
Residual
Total
6
819,6559
136,6093
951
966,2616
1,0160
957
1785,9175
Interception
Primary roads (Line Density)
Commercial Income (Log)
Rural Unsatisfied Basic Needs
School Non-Attendance
Health Coverage
Rural population
F
Significance F
134,4516
0.0000
Coefficients
Standar Error
t Statistic
Probability
17,7669
0,1469
120,9616
0.0000
0,1159
0,0074
15,5587
0.0000
0,0329
0,0059
5,6197
0.0000
(0,0149)
0,0020
(7,4116)
0.0000
(0,0501)
0,0085
(5,9232)
0.0000
0,0093
0,0017
5,6030
0.0000
0,0001
0,0000
16,9095
0.0000
Geographically Weighted Regression
R² 51,7%
Features GWR Model
Proyection 4G
Local R2
 The GWR model increases the explanation of the land
value variable with respect to the independent variables
(45% to 50%)
 The sum of the residual squares is lesser than the OLS
model, it shows a lower level of error in the spatial
analysis.
 Higher explanation of the land value and lower prediction
error in the Andean and Pacific regions. On the contrary,
the Caribbean and Orinoquía regions show a low level of
explanation and a higher measure of error.
Standard Error
4G Simulation
Influence area of the road network by
municipalities
 Road network and 4G concessions
 Analysis by department
 Land value and net valorization in rural areas
comparison from the current road network
and the inclusion of 4G projects
Primary roads + 4G
4G roads
Results
Rural land appraisal after 4G simulation
 High net valorization of land in the central zone, where the
majority of the projects are concentrated
 Departments with higher valorization: Cundinamarca, Valle del
Cauca, Quindío and Risaralda. Additionally Bogota Distrito
Capital.
 Boyacá, Risaralda and Nariño show the highest percentage
valorization with respect to their original value (>2%)
 Cundinamarca and Valle show the highest valorization (approx.
$10 million COP of additional value per km2)
Highest Valorization
Land value difference/Km2
Net appraisal (Billion COP)
DEPARTMENT
Nariño
Chocó
Chocó
Nariño
Nariño
MUNICIPALITY
Magüi Payan
Río Iro
Medio San Juan
Providencia
Gualmatán
VALORIZATION
14,28%
12,32%
8,59%
8,40%
8,31%
Conclusions
 The 4G concessions have a generally positive economic impact in the country, however the additional value is not equitable
in the regions and is mainly concentrated in the central zone, gradually diminishing outward toward the eastern and
northern periphery.
 The GWR model shows the main explanation of the land value and a lower prediction error in the Andean and Pacific
regions. In contrast to that, in the Caribbean and Orinioquía regions it shows a lower power of explanation and a higher
measure of error.
 The 4G road concessions have a high economic impact over the land valorization generally in rural areas.
 The GWR model allows to execute a more in depth analysis of the importance of each explanatory variables for every point
in Colombia than the OLS model does.
 In a country with high geographical, cultural, demographical, and other types of diversities; the relevance of a variable can
change radically from one place to another.
 GWR is a great tool to help make decisions in terms of infrastructure.
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Evaluación de impacto de proyectos de infraestructura