Carlos Lucca
MASTER OF URBAN AND REGIONAL PLANNING. 1998Especialidad: Desarrollo Urbano y RegionalGraduate School of Public and International Affairs (GSPIA).University of Pittsburgh – USA MAGISTER EN ADMINISTRACIÓN PÚBLICA 1993Especialidad: Desarrollo Municipal y RegionalInstituto de Investigación y Formación en Administración Pública (IIFAP)Universidad Nacional de Córdoba – Argentina.CERTIFICADO EN ESTUDIOS LATINOAMERICANOS. 1998Center for Latin American Studies (CLAS). University of Pittsburgh – USACERTIFICADO EN PLANEAMIENTO Y ECONOMÍA DEL TRANSPORTE. 1991Centro de Estudios del Transporte (CETRAN).Universidad Nacional de Córdoba – Argentina.INGENIERO CIVIL. 1990Facultad de Ciencias Exactas Físicas y Naturales.Universidad Nacional de Córdoba - Argentina.EXPERIENCIA LABORAL.Trabajos de consultoría en cuestiones vinculadas al desarrollo local para el Banco Interamericano de Desarrollo, el Programa de las Naciones Unidas para el Desarrollo, la Corporación Andina de Fomento, la Subsecretaría de Asuntos Municipales del Ministerio del Interior, el Gobierno de la Ciudad de Buenos Aires, el Gobierno de la Provincia de Córdoba, la Municipalidad de Córdoba y diversos gobiernos locales y provinciales en Argentina. Trabajos de consultoría en cuestiones de planeamiento estratégico para organizaciones de bien público y empresas privadas.ACTIVIDAD DOCENTE EN EL ÁMBITO UNIVERSITARIO. Director Ejecutivo del Instituto de Investigación y Formación en Administración Publica de la Universidad Nacional de Córdoba (2004-2014). Profesor Titular (interino) en el Área de Desarrollo Local (IIFAP-UNC). Profesor Titular por Concurso de la Cátedra de Geografía Económica y Social (Licenciatura en Geografía – FFyH-UNC). Docente en la Maestría en Gestión y Desarrollo Habitacional (FAUDI-UNC) y en la Maestría en Administración Pública (UNSE). Docente de grado y postgrado en la Universidad Nacional de Villa María (UNVM).
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Papers by Carlos Lucca
finance public infrastructure, to name just a few public policies that require correct valuations of land. However, in Latin America, outdated cadastral values are common to most cities. The reasons for this can be found in the complexity of the mass appraisal process, lack of institutional and fiscal capacity to undertake it and bureaucratic resistance to its implementation.
The objective of this paper is to present a mass appraisal methodology that uses only free and open data to achieve robust urban land valuations. Information from the OpenStreetMap Project is used to generate several land variables. In addition, the Global Human Settlement Layer of the European Commission is used to determine
the level of consolidation of urban sprawl. Land value data were obtained from the Mapa de Valores de América Latina, a collaborative initiative that systemizes more than 68,000 data from more than 900 cities.
This information is used to train three tree-based machine learning models: Random Forest, Quantile Random Forest and Gradient Boosting Model. The results support the viability of the proposed strategy, simplifying the
mass appraisal process in terms of costs, time and complexity of the information used.
finance public infrastructure, to name just a few public policies that require correct valuations of land. However, in Latin America, outdated cadastral values are common to most cities. The reasons for this can be found in the complexity of the mass appraisal process, lack of institutional and fiscal capacity to undertake it and bureaucratic resistance to its implementation.
The objective of this paper is to present a mass appraisal methodology that uses only free and open data to achieve robust urban land valuations. Information from the OpenStreetMap Project is used to generate several land variables. In addition, the Global Human Settlement Layer of the European Commission is used to determine
the level of consolidation of urban sprawl. Land value data were obtained from the Mapa de Valores de América Latina, a collaborative initiative that systemizes more than 68,000 data from more than 900 cities.
This information is used to train three tree-based machine learning models: Random Forest, Quantile Random Forest and Gradient Boosting Model. The results support the viability of the proposed strategy, simplifying the
mass appraisal process in terms of costs, time and complexity of the information used.