Development of a prototype web application for estimating housing values in Mexico using machine learning models
DOI:
https://doi.org/10.36825/RITI.13.32.003Keywords:
Real Estate Valuation, Housing, Prototype, Machine LearningAbstract
The development of a prototype web application for estimating home values in Mexico using machine learning models (ML) is presented. The evolution of valuation in Mexico is described, along with the opportunity that machine learning represents to improve accuracy and efficiency. The article is based on open data from the National Housing Information and Indicators System (SNIIV) and employs Random Forest (RF) and Linear Regression (LR) models, highlighting the superiority of the former in performance. The methodology, architecture, and technological tools used to build the application are detailed, and the accuracy results are presented by state, demonstrating the technical feasibility of the prototype despite limitations such as performance variability and the lack of regulations for its application in real-life cases.
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