Application of Artificial Intelligence for Predicting Real Estate Prices: The Case of Saudi Arabia

Alzain, Elham; Alshebami, Ali Saleh; Aldhyani, Theyazn H. H.; Alsubari, Saleh Nagi

doi:10.3390/electronics11213448

Cited by 15 publications

(13 citation statements)

References 41 publications

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“…In recent years, there have been many papers conducted on the analysis of time series data, for example, Alzain E, et al used artificial intelligence to predict housing prices [35], Shehadeh A, et al used machine learning models to predict the residual value of heavy construction equipment [34], Ecer F, et al trained a multilayer perceptron to predict the stock price index [36], Ahmed A A, et al integrated LSTM and random forest to estimate soil moisture [39], etc.…”

Section: Methods and Results Discussionmentioning

confidence: 99%

Study on Dynamic Evaluation of Sci-tech Journals Based on Time Series Model

Ma¹,

Han²,

Chen

et al. 2022

Applied Sciences

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As science and technology continue to advance, sci-tech journals are developing rapidly, and the quality of these journals affects the development and progress of particular subjects. Whether sci-tech journals can be evaluated and predicted comprehensively and dynamically from multiple angles based on the current qualitative and quantitative evaluations of sci-tech journals is related to a rational adjustment of journal resource allocation and development planning. In this study, we propose a time series analysis task for the comprehensive and dynamic evaluation of sci-tech journals, construct a multivariate short-time multi-series time series dataset that contains 18 journal evaluation metrics, and build models based on machine learning and deep learning methods commonly used in the field of time series analysis to carry out training and testing experiments on the dataset. We compare and analyze the experimental results to confirm the generalizability of these methods for the comprehensive dynamic evaluation of journals and find the LSTM model built on our dataset produced the best performance (MSE: 0.00037, MAE: 0.01238, accuracy based on 80% confidence: 72.442%), laying the foundation for subsequent research on this task. In addition, the dataset constructed in this study can support research on the co-analysis of multiple short time series in the field of time series analysis.

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Section: Methods and Results Discussionmentioning

confidence: 99%

Study on Dynamic Evaluation of Sci-tech Journals Based on Time Series Model

Ma¹,

Han²,

Chen

et al. 2022

Applied Sciences

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“…After location and rebuilding adjustments, maps are created and then linked to a GIS, where solar energy facility field operators can rapidly identify anomalies and detect solar panel failures [38]. It is expected that in the coming years, the evolution of artificial intelligence (AI) and machine learning (ML) technologies as part of the Industry 4.0 paradigm will contribute to the solar energy industry, for example, in global solar irradiation modeling/prediction and solar power production estimates, among other areas [40,41].…”

Section: New Technologies Applied To Solar Photovoltaic (Pv) Energy P...mentioning

confidence: 99%

Implementation of Renewable Energy from Solar Photovoltaic (PV) Facilities in Peru: A Promising Sustainable Future

Cacciuttolo,

Guardia,

Villicaña

2024

Sustainability

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In the last two decades, Peru has experienced a process of transformation in the sources of its energy matrix, increasing the participation of clean energy such as solar photovoltaic (PV), on-shore wind, biomass, and small hydro. However, hydropower and natural gas remain the main sources of electricity, whereas off-shore wind, biogas, waves, tidal, and geothermal sources are currently underdeveloped. This article presents the enormous potential of Peru for the generation of electrical energy from a solar source equivalent to 25 GW, as it has in one of the areas of the world with the highest solar radiation throughout the year. In addition, this article presents the main advantages, benefits, and considerations of the implementation of solar photovoltaic technology, with emphasis on (i) the potential of solar energy, showing the available potential and an installed capacity by the year 2024 equivalent to 398 MW, (ii) current solar energy sources, characterizing existing industrial solar photovoltaic (PV) energy plants, and (iii) future solar energy facilities projections, stating the portfolio of solar renewable energy plant projects to be implemented in the future considering an installed capacity of 7.2 GW by 2028. Additionally, lessons learned, challenges, and directions for the future development of solar energy in the country are presented. Finally, the article concludes that if Peru takes advantage of solar potential by considering a sustainable future perspective and implementing strategic land-use planning, the southern region will be transformed into a world-class territory for renewable energy development considering the hybridization of concentrated solar power (CSP) systems with solar photovoltaic (PV) systems and solar energy storage systems.

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“…Artificial neural networks (ANNs) have been widely explored for real estate price prediction due to their ability to model complex nonlinear relationships [5]. The study in [6] combined case-based reasoning (CBR) and ANNs, achieving high accuracy while acknowledging challenges in data availability and model refinement.…”

Section: Neural Network Approachmentioning

confidence: 99%

“…Comprehensive understanding of real estate price dynamics [5] Lack of research on interpretability of ML models in real estate pricing Challenges in understanding factors driving predictions [12] Need for investigating generalizability of findings across diverse real estate markets…”

Section: Research Gap/limitationmentioning

confidence: 99%

Advanced Machine Learning Techniques for Predictive Modeling of Property Prices

Mathotaarachchi,

Hasan,

Mahmood

2024

Information

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Real estate price prediction is crucial for informed decision making in the dynamic real estate sector. In recent years, machine learning (ML) techniques have emerged as powerful tools for enhancing prediction accuracy and data-driven decision making. However, the existing literature lacks a cohesive synthesis of methodologies, findings, and research gaps in ML-based real estate price prediction. This study addresses this gap through a comprehensive literature review, examining various ML approaches, including neural networks, ensemble methods, and advanced regression techniques. We identify key research gaps, such as the limited exploration of hybrid ML-econometric models and the interpretability of ML predictions. To validate the robustness of regression models, we conduct generalization testing on an independent dataset. Results demonstrate the applicability of regression models in predicting real estate prices across diverse markets. Our findings underscore the importance of addressing research gaps to advance the field and enhance the practical applicability of ML techniques in real estate price prediction. This study contributes to a deeper understanding of ML’s role in real estate forecasting and provides insights for future research and practical implementation in the real estate industry.

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Application of Artificial Intelligence for Predicting Real Estate Prices: The Case of Saudi Arabia

Cited by 15 publications

References 41 publications

Study on Dynamic Evaluation of Sci-tech Journals Based on Time Series Model

Study on Dynamic Evaluation of Sci-tech Journals Based on Time Series Model

Implementation of Renewable Energy from Solar Photovoltaic (PV) Facilities in Peru: A Promising Sustainable Future

Advanced Machine Learning Techniques for Predictive Modeling of Property Prices

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