Modelos automatizados de valoración (AVMs)
Resumen
Los Modelos de Valoración Automatizada (AVMs) son herramientas basadas en software que estiman el valor de propiedades mediante algoritmos y grandes volúmenes de datos. Utilizados en bienes raíces para tareas como préstamos hipotecarios y análisis de inversiones, mejoran la eficiencia y consistencia de las valoraciones en comparación con métodos tradicionales. Los AVMs se basan en datos históricos de ventas, características de las propiedades y tendencias del mercado, entre otros, para generar valores estimados. Existen varios tipos de AVMs, incluidos los Modelos Hedónicos, de Emulación de Tasación, de Índice, Combinados y en Cascada, cada uno con sus propias metodologías. Este documento explora el desarrollo y comparación del rendimiento de diferentes AVMs, como Regresión de Vectores de Soporte (SVR), Bosques Aleatorios (RF), Potenciador de Gradiente Extremo (XGBoost) y Redes Neuronales Profundas (DNN). Además, se presentan casos prácticos utilizando Regresión Lineal Múltiple (MLR) con datos del Condado de King, Washington, EE. UU. Se destacan avances tecnológicos, integración en la industria inmobiliaria y consideraciones éticas sobre privacidad de datos y sesgo en la tasación. Automated Valuation Models (AVMs) are software-based tools designed to estimate the value of properties using complex algorithms and large amounts of data. Widely used in real estate for purposes such as mortgage lending and investment analysis, they improve the efficiency and consistency of property valuations compared to traditional appraisal methods. AVMs leverage historical sales data, property characteristics, market trends, public records, and geographical information to generate estimated values. There are various types of AVMs, including Hedonic Models, Appraisal Emulation Models, Index Models, Blended Models, and Cascade Models, each with unique methodologies. This document examines the development and comparison of the performance of various AVMs, including machine learning models such as Support Vector Regression (SVR), Random Forests (RF), Extreme Gradient Boosting (XGBoost), and Deep Neural Networks (DNN). Additionally, practical cases are presented using Multiple Linear Regression (MLR) with data from King County, Washington, USA. Findings highlight technological advancements, integration into the real estate industry, and ethical considerations regarding data privacy and bias in appraisal.
Trabajo Fin de Grado
Modelos automatizados de valoración (AVMs)Titulación / Programa
Grado en Administración y Dirección de Empresas con Mención en InternacionalMaterias/ categorías / ODS
K4NPalabras Clave
Automated Valuation Models (AVMs), Hedonic Models, Appraisal Emulation Model, Index Models, Blended Models, Cascade Models, Regression Analysis, Machine Learning Algorithms, Multiple Linear Regression (MLR), Support Vector Regression (SVR), Random Forests (RF), eXtreme Gradient Boosting (XGBoost), DAutomated Valuation Models (AVMs) are software-based tools designed to estimate the value of properties using complex algorithms and large amounts of data. Widely used in real estate for purposes such as mortgage lending and investment analysis, they improve the efficiency and consistency of property valuations compared to traditional appraisal methods. AVMs leverage historical sales data, property characteristics, market trends, public records, and geographical information to generate estimated values. There are various types of AVMs, including Hedonic Models, Appraisal Emulation Models, Index Models, Blended Models, and Cascade Models, each with unique methodologies. This document examines the development and comparison of the performance of various AVMs, including machine learning models such as Support Vector Regression (SVR), Random Forests (RF), Extreme Gradient Boosting (XGBoost), and Deep Neural Networks (DNN). Additionally, practical cases are presented using Multiple Linear Regression (MLR) with data from King County, Washington, USA. Findings highlight technological advancements, integration into the real estate industry, and ethical considerations regarding data privacy and bias in appraisal.