Representative Residential Property Model—Soft Computing Solution

Chmielewska, Aneta; Renigier‐Biłozor, Małgorzata; Janowski, Artur

doi:10.3390/ijerph192215114

Cited by 3 publications

(3 citation statements)

References 51 publications

(62 reference statements)

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“…One of the key problems in real estate market analyses is the identification of real estate characteristics (their occurrence, extent, and relevance) affecting the reasoning [13][14][15][16][17]. The accurate identification of problems and limitations in the area of real estate information analyses determines the direction of the search for solutions that better reflect market reality.…”

Section: The Problem Of Real Estate Feature Significance Assessmentmentioning

confidence: 99%

Real Estate Industry Sustainable Solution (Environmental, Social, and Governance) Significance Assessment—AI-Powered Algorithm Implementation

Walacik,

Chmielewska

2024

Sustainability

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As the global imperative for sustainable development intensifies, the real estate industry stands at the intersection of environmental responsibility and economic viability. This paper presents a comprehensive exploration of the significance of sustainable solutions within the real estate sector, employing advanced artificial intelligence (AI) algorithms to assess their impact. This study focuses on the integration of AI-powered tools in a decision-making process analysis. The research methodology involves the development and implementation of AI algorithms capable of analyzing vast datasets related to real estate attributes. By leveraging machine learning techniques, the algorithm assesses the significance of energy efficiency solutions along with other intrinsic and extrinsic attributes. This paper examines the effectiveness of these solutions in relation to the influence on property prices with a framework based on an AI-driven algorithm. The findings aim to inform real estate professionals and investors about the tangible advantages of integrating AI technologies into sustainable solutions, promoting a more informed and responsible approach to industry practices. This research contributes to the growing interest in the connection of the real estate sector, sustainability, and AI, offering insights that can guide strategic decision making. By implementing the random forest method in the real estate feature significance assessment original methodology, it has been shown that AI-powered algorithms can be a useful tool from the perspective of real estate price prediction. The methodology’s ability to handle non-linear relationships and provide insights into feature importance proved advantageous in comparison to the multiple regression analysis.

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Section: The Problem Of Real Estate Feature Significance Assessmentmentioning

confidence: 99%

Real Estate Industry Sustainable Solution (Environmental, Social, and Governance) Significance Assessment—AI-Powered Algorithm Implementation

Walacik,

Chmielewska

2024

Sustainability

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“…This suggests that an object (consisting of features) is not identical to the one being compared, but they are similar to some extent. This approach allows for the inclusion of the object within the analyzed decisional rule based on a certain threshold of similarity (Chmielewska et al, 2022).…”

Section: Realmentioning

confidence: 99%

Human-Machine Synergy in Real Estate Similarity Concept

Renigier-Biłozor,

Janowski

2023

Real Estate Management and Valuation

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The issue of similarity in the real estate market is a widely recognized aspect of analysis, yet it remains underexplored in scientific research. This study aims to address this gap by introducing the concept of a Property Cognitive Information System (PCIS), which offers an innovative approach to analyzing similarity in the real estate market. The PCIS introduces non-classical and alternative solutions, departing from the conventional data analysis practices commonly employed in the real estate market. Moreover, the study delves into the integration of artificial intelligence (AI) in the PCIS. The paper highlights the value added by the PCIS, specifically discussing the validity of using automatic ML-based solutions to objectify the results of synergistic data processing in the real estate market. Furthermore, the article establishes a set of essential assumptions and recommendations that contribute to a well-defined and interpretable notion of similarity in the context of human-machine analyses. By exploring the intricacies of similarity in the real estate market through the innovative PCIS and AI-based solutions, this research seeks to broaden the understanding and applicability of data analysis techniques in this domain.

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“…The fundamental concept behind machine learning involves empowering computers to acquire knowledge from data, recognize patterns, and arrive at informed choices while minimizing the need for extensive human involvement. By utilising algorithms and statistical models, machine learning systems can adapt and improve their performance over time, making them valuable tools for a multitude of tasks, ranging from natural language processing [12] and image recognition [13] to real estate value forecasting [14][15][16][17] and medical purposes [18]. The foundation of machine learning lies in its ability to extract knowledge from data, which is achieved by employing various learning paradigms.…”

Section: Introductionmentioning

confidence: 99%

Computational Complexity and Its Influence on Predictive Capabilities of Machine Learning Models for Concrete Mix Design

Ziolkowski

2023

Materials

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The design of concrete mixtures is crucial in concrete technology, aiming to produce concrete that meets specific quality and performance criteria. Modern standards require not only strength but also eco-friendliness and production efficiency. Based on the Three Equation Method, conventional mix design methods involve analytical and laboratory procedures but are insufficient for contemporary concrete technology, leading to overengineering and difficulty predicting concrete properties. Machine learning-based methods offer a solution, as they have proven effective in predicting concrete compressive strength for concrete mix design. This paper scrutinises the association between the computational complexity of machine learning models and their proficiency in predicting the compressive strength of concrete. This study evaluates five deep neural network models of varying computational complexity in three series. Each model is trained and tested in three series with a vast database of concrete mix recipes and associated destructive tests. The findings suggest a positive correlation between increased computational complexity and the model’s predictive ability. This correlation is evidenced by an increment in the coefficient of determination (R2) and a decrease in error metrics (mean squared error, Minkowski error, normalized squared error, root mean squared error, and sum squared error) as the complexity of the model increases. The research findings provide valuable insights for increasing the performance of concrete technical feature prediction models while acknowledging this study’s limitations and suggesting potential future research directions. This research paves the way for further refinement of AI-driven methods in concrete mix design, enhancing the efficiency and precision of the concrete mix design process.

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Representative Residential Property Model—Soft Computing Solution

Cited by 3 publications

References 51 publications

Real Estate Industry Sustainable Solution (Environmental, Social, and Governance) Significance Assessment—AI-Powered Algorithm Implementation

Real Estate Industry Sustainable Solution (Environmental, Social, and Governance) Significance Assessment—AI-Powered Algorithm Implementation

Human-Machine Synergy in Real Estate Similarity Concept

Computational Complexity and Its Influence on Predictive Capabilities of Machine Learning Models for Concrete Mix Design

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