1. Mario Ortiz – Daniel Páez lab.uniandes.edu.co

2.  Purpose of 4G concessions is to improve national competitiveness…. Source: National Infrastructure Agency (ANI) Fuente: DNP, Ministry of transport. 2011 Context …and extend national roads coverage Communications investment /GDP Transportation investment/GDP Energy investment/GDP National road network Secondary road network Tertiary road network Privates Estimated investment: $25 billion

3. 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

4. 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

5. Problem In this research proposes a methodology to estimate land value increments due to new national road infrastructure  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 Determination of land value increases Develop land value capture mechanism

6.  To estimate economic impact of new national roads on land values Objetives Concessions National Roads ESTIMATED KILOMETERS INTERVENE ESTIMATED TOTAL INVESTMENT (BILLION / DIC - 11) Current Concessions New Concessions We focused on the new generation of national road concessons called 4G (forth generation) Source: ANI

7. Methodology Design variables and determine model Standard Linear Regression model (OLS) Applying the GWR Model Spatial Analysis and Results 4G Simulation Valorization Analysis according to 4G Based on: Páez & Currie (2012)

8. With a similar methodology we have estimated increases of land values for Bogota Previous experiences

9. Geographically Weighted Regression (GWR) Ordinary Least Square (OLS) More appropriate for regional policy design Allows for a more specific analysis, not too general. Easy analysis and data collection.  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 Modeling with GWR

10. VARIABLENAMESOURCEYEAR Dependent land valuesIGAC2011 ExplanatoryRural Aqueduct CoverageSSPD2008 ExplanatoryIndustrial and Commercial IncomeDNP2009 ExplanatoryRural Electric CoverageDANE2005 ExplanatorySchool Non-AttendanceDANE2005 ExplanatoryRural Unsatisfied Basic NeedsDANE2010 ExplanatorySubsided Health Regimen CoverageHealthMin2010 ExplanatoryRural PopulationDANE2005 ExplanatoryMain Road Averaged Area of InfluenceOSM2013 ExplanatorySecondary Road Averaged Area of InfluenceOSM2013 ExplanatoryTertiary Road Averaged Area of InfluenceOSM2013 Model

11. Socioeconomic  Rural Unsatisfied Basic Needs (UBN) Index  Rural power Coverage  Rural potable water Coverage  Land values Quindío Valle Caldas Guajira Guainía Vichada Chocó Variables Rural power Coverage Rural potable water Coverage Land values

12. Road Infrastructure  Primary roads  Secondary roads  Tertiary roads Variables 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 Influence area of the road network by municipalities Primary roads Secondary roads Tertiary roads Primary roads Secondary roads Tertiary roads

13. R²45,6% Results OLS: Ordinary Least Squares 1  Representation of the model in 45% of the real variation of the land value in a rural zone.  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.

14. R²45,5%  The model does not represent a significant difference with respect to the specification and statistical significance.  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. OLS: Ordinary Least Squares 2

15. R²51,7% Geographically Weighted Regression  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. Features GWR Model Proyection 4G Standard Error Local R2

16. 4G Simulation  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 Influence area of the road network by municipalities Primary roads + 4G 4G roads

17. Results  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 km 2 ) DEPARTMENTMUNICIPALITYVALORIZATION NariñoMagüi Payan14,28% ChocóRío Iro12,32% ChocóMedio San Juan8,59% NariñoProvidencia8,40% NariñoGualmatán8,31% Highest Valorization Rural land appraisal after 4G simulation Land value difference/Km2 Net appraisal (Billion COP )

18.  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. Conclusions