<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In recent years, machine learning algorithms have been used in the mass appraisal of real estate. In this study, 5 machine learning algorithms are used for residential type real estate. Machine learning algorithms used for mass appraisal in this study are Artificial Neural Networks (ANN), Random Forest (RO), Multiple Regression Analysis (MRA), K-Nearest Neighborhood (k-nn), Support Vector Regression (SVR). To test the study, real estate data collected from the central districts of Ankara, were used. The main purpose of this study is to find out which machine learning algorithm gives the best results for the mass appraisal of real estates and to reveal the most important variables that affect the prices of real estate. According to the results obtained for the city of Ankara, it was observed that the best algorithm for mass appraisal is RF in residential-type real estates, followed by the ANN, k-nn, and linear regression algorithms, respectively. According to the results obtained from the residential real estate, it was concluded that heating and distances to places of importance had the greatest effect on the value.</p>
</abstract>

In recent years, machine learning algorithms have been used in the mass appraisal of real estate. In this study, 5 machine learning algorithms are used for residential type real estate. Machine learning algorithms used for mass appraisal in this study are Artificial Neural Networks (ANN), Random Forest (RO), Multiple Regression Analysis (MRA), K-Nearest Neighborhood (k-nn), Support Vector Regression (SVR). To test the study, real estate data collected from the central districts of Ankara, were used. The main purpose of this study is to find out which machine learning algorithm gives the best results for the mass appraisal of real estates and to reveal the most important variables that affect the prices of real estate. According to the results obtained for the city of Ankara, it was observed that the best algorithm for mass appraisal is RF in residential-type real estates, followed by the ANN, k-nn, and linear regression algorithms, respectively. According to the results obtained from the residential real estate, it was concluded that heating and distances to places of importance had the greatest effect on the value.

Comparison of Machine Learning Algorithms for Mass Appraisal of Real Estate Data

Department of Real Estate Development and Management

Real Estate Management and Valuation

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Comparison of Machine Learning Algorithms for Mass Appraisal of Real Estate Data"}

In recent years, machine learning algorithms have been used in the mass appraisal of real estate. In this study, 5 machine learning algorithms are used for...

Comparison of Machine Learning Algorithms for Mass Appraisal of Real Estate Data

Data publikacji: 20 lut 2024

Zakres stron: 100 - 111

Otrzymano: 19 wrz 2023

Przyjęty: 16 lut 2024

DOI: https://doi.org/10.2478/remav-2024-0019

Słowa kluczowe
mass appraisal, machine learning algorithms, random forest, artificial neural network, real estate valuation map

© 2024 Sibel Canaz Sevgen et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Comparison of Machine Learning Algorithms for Mass Appraisal of Real Estate Data

Data publikacji: 20 lut 2024

Zakres stron: 100 - 111

Otrzymano: 19 wrz 2023

Przyjęty: 16 lut 2024

DOI: https://doi.org/10.2478/remav-2024-0019

Słowa kluczowemass appraisal, machine learning algorithms, random forest, artificial neural network, real estate valuation map

© 2024 Sibel Canaz Sevgen et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
mass appraisal, machine learning algorithms, random forest, artificial neural network, real estate valuation map