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Predicción de precios de vivienda: Aprendizaje estadístico con datos de oferta y transacciones para la ciudad de Montevideo

Author

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  • Pablo Picardo

    (Banco Central del Uruguay)

Abstract

En este trabajo se presentan modelos predictivos para el precio de un activo de difícil valuación como la vivienda. Se utilizan dos fuentes de datos para la ciudad de Montevideo: una proveniente de sitios web (a través de web scraping) y otra de registros administrativos de transacciones. Se implementan tres modelos fácilmente replicables: modelo lineal, árbol de regresión y bosques aleatorios. Los resultados arrojan una mejor performance del modelo de bosques aleatorios respecto al modelo lineal hedónico, ampliamente difundido en la literatura. Se busca incorporar al análisis de predicción de precios una metodología aún escasamente difundida a nivel nacional, implementada en el software R y poner a disposición una nueva base de datos.

Suggested Citation

  • Pablo Picardo, 2019. "Predicción de precios de vivienda: Aprendizaje estadístico con datos de oferta y transacciones para la ciudad de Montevideo," Documentos de trabajo 2019002, Banco Central del Uruguay.
    • Handle: RePEc:bku:doctra:2019002
    as

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    File URL: https://www.bcu.gub.uy/Estadisticas-e-Indicadores/Documentos%20de%20Trabajo/02.2019.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

    precios de vivienda; aprendizaje estadístico; bosques aleatorios; CART; valuación de activos; precios online; datos geo-referenciados;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets

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