{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T03:36:37Z","timestamp":1779334597460,"version":"3.51.4"},"reference-count":43,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,6,11]],"date-time":"2025-06-11T00:00:00Z","timestamp":1749600000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Evaluation models for the preservation and the revitalization of the small towns","award":["RP124190A5E8AD3D"],"award-info":[{"award-number":["RP124190A5E8AD3D"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>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\u2019s 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.<\/jats:p>","DOI":"10.3390\/info16060486","type":"journal-article","created":{"date-parts":[[2025,6,11]],"date-time":"2025-06-11T11:18:40Z","timestamp":1749640720000},"page":"486","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["The Interpretative Effects of Normalization Techniques on Complex Regression Modeling: An Application to Real Estate Values Using Machine Learning"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6235-3440","authenticated-orcid":false,"given":"Debora","family":"Anelli","sequence":"first","affiliation":[{"name":"Department of Architecture and Design, \u201cSapienza\u201d University of Rome, 00196 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8049-1206","authenticated-orcid":false,"given":"Pierluigi","family":"Morano","sequence":"additional","affiliation":[{"name":"Department of Civil, Environmental, Land, Building Engineering and Chemistry (DICATECh), Polytechnic University of Bari, 70126 Bari, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2011-1950","authenticated-orcid":false,"given":"Francesco","family":"Tajani","sequence":"additional","affiliation":[{"name":"Department of Architecture and Design, \u201cSapienza\u201d University of Rome, 00196 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8675-7498","authenticated-orcid":false,"given":"Maria Rosaria","family":"Guarini","sequence":"additional","affiliation":[{"name":"Department of Architecture and Design, \u201cSapienza\u201d University of Rome, 00196 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Locurcio, M., Tajani, F., and Anelli, D. 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