Real Estate Valuation in Buenos Aires, an Interactive Tool Development

This thesis explores the application of machine learning models for real estate valuation in Buenos Aires, aiming to develop a user-centric tool to assist buyers and investors in making informed decisions. Traditional regression models, while useful, often fail to capture the complex, nonlinear relationships inherent in real estate data. Consequently, we employed advanced machine learning techniques, including XGBoost, Random Forest, and Support Vector Machines (SVM), selected for their robustness and efficiency in handling large datasets. Our input data consists of 64,358 property listings from the e-commerce platform Mercado Libre, obtained through a combination of Python scripts and the platform's own API.

Name thesis:degree_name

Master in Management + Analytics

Grantor dc:publisher

Universidad Torcuato Di Tella

Year dc:date.issued

2024

Author dc:creator

• Gonzalvo, Francisco

Advisor dc:contributor.advisor

• Iarussi, Emmanuel

• Mercado Inmobiliario
• Real Estate market
• Innovación tecnológica
• Technological innovation
• Análisis de datos
• Data Analysis

Statement dc:rights

• info:eu-repo/semantics/openAccess

Language dc:language

eng

Repository record dc:identifier.uri

https://repositorio.utdt.edu/handle/20.500.13098/12966

OAI identifier oai:identifier

oai:repositorio.utdt.edu:20.500.13098/12966