Enhancing Real Estate Management - The Transformative Role of Machine Learning in Predictive Gains and Risk Model Performance - Literature Review

Aug 3, 2026

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Mortgage scoring models are central to assessing risk in mortgage lending, historically built using logistic regression. With advances in machine learning, classification trees, neural networks, and other algorithms train on mortgage samples labeled by default occurrence, then evaluate performance using separate training, validation, and test splits. Hyperparameters are tuned to maximize predictive results while reducing overfitting, and calibrated models are applied to test data to forecast default events. Despite added complexity, predictive performance is often comparable to logistic regression, while ensemble methods may further improve risk prediction.

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Abstract

Introduction

Literature review

University for Business and Technology in Kosovo UBT Knowledge Center | UBT International Conference | 2023 UBT International Conference | | --- | --- | | Oct 28th, 8:00 AM-Oct 29th, 6:00 PM
Enhancing Real Estate Management: The Transformative Role of Machine Learning in Predictive Gains and Risk Model Performance
Visar Hoxha
University for Business and Technology-UBT, [visar.hoxha@ubt-uni.net](visar.hoxha@ubt-uni.net)
Blerta Demjaha
[blerta.demjaha@eukos.org](blerta.demjaha@eukos.org)
Veli Lecaj
University for Business and Technology-UBT, [veli.lecaj@ubt-uni.net](veli.lecaj@ubt-uni.net)
Hazer Dana
University for Business and Technology, [hazer.dana@ubt-uni.net](hazer.dana@ubt-uni.net)
Fuat Pallaska
University for Business and Technology-UBT, [fuat.pallaska@ubt-uni.net](fuat.pallaska@ubt-uni.net)
Follow this and additional works at: [https://knowledgecenter.ubt-uni.net/conference](https://knowledgecenter.ubt-uni.net/conference)
Part of the Law Commons | | Recommended Citation Hoxha, Visar; Demjaha, Blerta; Lecaj, Veli; Dana, Hazer; and Pallaska, Fuat, "Enhancing Real Estate Management: The Transformative Role of Machine Learning in Predictive Gains and Risk Model Performance" (2023) . UBT International Conference. 2. [https://knowledgecenter.ubt-uni.net/conference/IC/LAW/2](https://knowledgecenter.ubt-uni.net/conference/IC/LAW/2) This Event is brought to you for free and open access by the Publication and Journals at UBT Knowledge Center. It has been accepted for inclusion in UBT International Conference by an authorized administrator of UBT Knowledge Center. For more information, please contact [knowledge.center@ubt-uni.net](knowledge.center@ubt-uni.net). Enhancing Real Estate Management: The Transformative Role of Machine Learning in Predictive Gains and Risk Model Performance Visar Hoxha1, Blerta Demjaha2 Veli Lecaj1 Hazer Dana1 Fuat Pallaska1 1 Faculty of Real Estate, UBT [Visar.hoxha@ubt-uni.net](Visar.hoxha@ubt-uni.net) 2 Real Estate Department, College ESLG [blerta.demjaha@eukos.org](blerta.demjaha@eukos.org) 1 Faculty of Real Estate, UBT [Veli.lecaj@ubt-uni.net](Veli.lecaj@ubt-uni.net) 1 Faculty of Real Estate, UBT [hazer.dana@ubt-uni.net](hazer.dana@ubt-uni.net) 1 Faculty of Real Estate, UBT [fuat.pallaska@ubt-uni.net](fuat.pallaska@ubt-uni.net) Abstract: Mortgage scoring models are pivotal in evaluating the risk associated with mortgages. Traditionally, these models were constructed using logistic regression. However, with the rise of machine learning, algorithms such as classification trees and neural networks have been employed. These algorithms are trained on a sample of mortgages, with the occurrence or non-occurrence of default observed. The data is then split into training and test samples, with machine learning algorithms further dividing the training sample for validation. This approach aims to determine hyperparameters that maximize performance while minimizing overfitting. Once calibrated, the model is applied to the test sample to predict default events. Despite the sophistication of machine learning algorithms, their predictive performance in mortgage scoring is comparable to logistic regression. Ensemble methods, which combine multiple models, have shown potential in enhancing predictive performance. This literature review explores the application of machine learning in mortgage scoring, comparing it with traditional methods, and discussing its implications. Keywords: scoring model, mortgage default, machine learning algorithms, logistic regression, receiver operating characteristic curve, neural networks 1. Introduction The rapid evolution of machine learning and its application in various sectors has garnered significant attention in the academic and industrial world. One such application is in the realm of real estate mortgage scoring. Traditional scoring models, primarily based on logistic regression, have been the c

FAQs

What is the purpose of mortgage scoring models in real estate lending?

Mortgage scoring models evaluate the risk associated with mortgages by predicting whether default will occur. They translate borrower and contract information into risk-related outputs.

How do machine learning mortgage scoring approaches differ from traditional logistic regression?

Machine learning methods such as classification trees and neural networks are trained on labeled mortgage samples and use training/validation/test splits to tune hyperparameters. Logistic regression relies on a traditional statistical modeling framework rather than algorithmic hyperparameter tuning across multiple model types.

What role do hyperparameters and validation play in preventing overfitting?

Hyperparameters are selected by maximizing performance on a validation sample that is different from the learning sample. This reduces overfitting risk caused by choosing “optimal” settings only on the training data.

Aug 3, 2026

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