The Interpretative Effects of Normalization Techniques on Complex Regression Modeling: An Application to Real Estate Values Using Machine Learning
The performance of machine learning models depends on several factors, including data normalization, which can significantly improve its accuracy. There are many standardization techniques, and none is universally suitable; the choice depends on the characteristics of the problem, the predictive tas...
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MDPI AG
2025-06-01
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| author | Debora Anelli Pierluigi Morano Francesco Tajani Maria Rosaria Guarini |
| author_facet | Debora Anelli Pierluigi Morano Francesco Tajani Maria Rosaria Guarini |
| author_sort | Debora Anelli |
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| description | The performance of machine learning models depends on several factors, including data normalization, which can significantly improve its accuracy. There are many standardization techniques, and none is universally suitable; the choice depends on the characteristics of the problem, the predictive task, and the needs of the model used. This study analyzes how normalization techniques influence the outcomes of real estate price regression models using machine learning to uncover complex relationships between urban and economic factors. Six normalization techniques are employed to assess how they affect the estimation of relationships between property value and factors like social degradation, resident population, per capita income, green spaces, building conditions, and degraded neighborhood presence. The study’s findings underscore the pivotal role of normalization in shaping the perception of variables, accentuating critical thresholds, or distorting anticipated functional relationships. The work is the first application of a methodological approach to define the best technique on the basis of two criteria: statistical reliability and empirical evidence of the functional relationships obtainable with each standardization technique. Notably, the study underscores the potential of machine-learning-based regression to circumvent the limitations of conventional models, thereby yielding more robust and interpretable results. |
| format | Article |
| id | doaj-art-cc234e02b59b4e7a88b16757da5601d3 |
| institution | OA Journals |
| issn | 2078-2489 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Information |
| spelling | doaj-art-cc234e02b59b4e7a88b16757da5601d32025-08-20T02:21:12ZengMDPI AGInformation2078-24892025-06-0116648610.3390/info16060486The Interpretative Effects of Normalization Techniques on Complex Regression Modeling: An Application to Real Estate Values Using Machine LearningDebora Anelli0Pierluigi Morano1Francesco Tajani2Maria Rosaria Guarini3Department of Architecture and Design, “Sapienza” University of Rome, 00196 Rome, ItalyDepartment of Civil, Environmental, Land, Building Engineering and Chemistry (DICATECh), Polytechnic University of Bari, 70126 Bari, ItalyDepartment of Architecture and Design, “Sapienza” University of Rome, 00196 Rome, ItalyDepartment of Architecture and Design, “Sapienza” University of Rome, 00196 Rome, ItalyThe performance of machine learning models depends on several factors, including data normalization, which can significantly improve its accuracy. There are many standardization techniques, and none is universally suitable; the choice depends on the characteristics of the problem, the predictive task, and the needs of the model used. This study analyzes how normalization techniques influence the outcomes of real estate price regression models using machine learning to uncover complex relationships between urban and economic factors. Six normalization techniques are employed to assess how they affect the estimation of relationships between property value and factors like social degradation, resident population, per capita income, green spaces, building conditions, and degraded neighborhood presence. The study’s findings underscore the pivotal role of normalization in shaping the perception of variables, accentuating critical thresholds, or distorting anticipated functional relationships. The work is the first application of a methodological approach to define the best technique on the basis of two criteria: statistical reliability and empirical evidence of the functional relationships obtainable with each standardization technique. Notably, the study underscores the potential of machine-learning-based regression to circumvent the limitations of conventional models, thereby yielding more robust and interpretable results.https://www.mdpi.com/2078-2489/16/6/486machine learningreal estate valuesnormalizationregression model |
| spellingShingle | Debora Anelli Pierluigi Morano Francesco Tajani Maria Rosaria Guarini The Interpretative Effects of Normalization Techniques on Complex Regression Modeling: An Application to Real Estate Values Using Machine Learning Information machine learning real estate values normalization regression model |
| title | The Interpretative Effects of Normalization Techniques on Complex Regression Modeling: An Application to Real Estate Values Using Machine Learning |
| title_full | The Interpretative Effects of Normalization Techniques on Complex Regression Modeling: An Application to Real Estate Values Using Machine Learning |
| title_fullStr | The Interpretative Effects of Normalization Techniques on Complex Regression Modeling: An Application to Real Estate Values Using Machine Learning |
| title_full_unstemmed | The Interpretative Effects of Normalization Techniques on Complex Regression Modeling: An Application to Real Estate Values Using Machine Learning |
| title_short | The Interpretative Effects of Normalization Techniques on Complex Regression Modeling: An Application to Real Estate Values Using Machine Learning |
| title_sort | interpretative effects of normalization techniques on complex regression modeling an application to real estate values using machine learning |
| topic | machine learning real estate values normalization regression model |
| url | https://www.mdpi.com/2078-2489/16/6/486 |
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