[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119744-en":3,"doc-seo-119744-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119744,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Comparison of Various Machine Learning Models for Estimating Construction Projects Sales Valuation - Rapid Estimation","The capability of multiple machine learning techniques for predicting construction project sales valuation in residential buildings using economic variables and indices (EV&Is) together with physical and financial variables (P&F) remains uncertain. Although prior studies mainly identify factors affecting construction sales for short-term economic impact, accurate prediction supports project sustainability. This study compares different machine learning approaches, using EV&Is and P&F as input features and sales valuation as output, to determine the most effective model. Results show extremely randomized trees achieves the best predictive performance, while decision trees perform the least satisfactorily.","| \u003Cbr>Contents lists available at SCCE |  |\n| --- | --- |\n| \u003Cbr>Journal of Soft Computing in Civil Engineering |  |\n| \u003Cbr>[Journal homepage: www.jsoftcivil.com](Journal homepage: www.jsoftcivil.com) |  |\n| Comparison of Various Machine Learning Models for Estimating Construction Projects Sales Valuation Using Economic Variablesand Indices\u003Cbr>YazanAlzubi1*\u003Cbr>1. Associate Professor, Civil Engineering Department, Faculty of Engineering Technology Al-Balqa Applied University, 11134 Amman, Jordan\u003Cbr>[Corresponding author:](Corresponding author: yazan.alzubi@bau.edu.jo)[ yazan.alzubi@bau.edu.jo](Corresponding author: yazan.alzubi@bau.edu.jo)\u003Cbr> [https://doi.org/10.22115/SCCE.2023.364221.1536](https://doi.org/10.22115/SCCE.2023.364221.1536) |  |\n| ARTICLE INFO\u003Cbr>Article history:\u003Cbr>Received: 02 October 2022\u003Cbr>Revised: 25 April 2023\u003Cbr>Accepted: 19 May 2023\u003Cbr>Keywords:\u003Cbr>Real estate valuation; Construction project sales valuation;\u003Cbr>Economic variables and indices; Machine learning;\u003Cbr>Rapid estimation. | ABSTRACT\u003Cbr>The capability of various machine learning techniques in predicting construction project profit in residential buildings using a combination of economic variables and indices (EV&Is) and physical and financial variables (P&F) as input variables remain uncertain. Although recent studies have primarily focused on identifying the factors influencing the sales of construction projects due to their significant short-term impact on a country's economy, the prediction of these parameters is crucial for ensuring project sustainability. While techniques such as regression and artificial neural networks have been utilized to estimate construction project sales, limited research has been conducted in this area. The application of machine learning techniques presents several advantages over conventional methods, including reductions in cost, time, and effort. Therefore, this study aims to predict the sales valuation of construction projects using various machine learning approaches, incorporating different EV&Is and P&F as input features for these models and subsequently generating the sales valuation as the output. This research will undertake a comparative analysis to investigate the efficiency of the different machine learning models, identifying the most effective approach for estimating the sales valuation of construction projects. By leveraging machine learning techniques, it is anticipated that the accuracy of sales valuation predictions will be enhanced, ultimately resulting in more sustainable and successful construction projects. In general, the findings of this research reveal that the extremely randomized trees model delivers the best performance, while the decision tree model exhibits the least satisfactory performance in predicting the sales valuation of construction projects. |\n\n1. Introduction  \nDifferent investment decisions are associated with high returns, such as real estate, which is considered one of the most profitable and sustainable choices [1,2] . The evaluation of real estate in any region is based on the assessment of multiple factors, including the ongoing condition of the economy and the value of money [3] . In addition to that, the prevalence of the application of real estate is significantly governed by the expansion of the population and the prompt urbanization due to the necessity of investigating the obtainability, supply, and demand of housing in order to provide the requirements caused by the growth of urbanization and population [4,5] . Hence, the need for adequate and accurate housing price estimation is crucial for various aspects, including demand, development, investment, evaluations, and tax inspections of housing prices [6,7] . The existence of real estate valuation in many aspects caused the development of diverse methods for forecasting fluctuating housing prices [8–10] . To overcome the undesirability of this type of inaccurate prediction, Ibisola et al. [11] suggested the need for","cbCaicYAjYdIbEXf","https://ap.wps.com/l/cbCaicYAjYdIbEXf","pdf",1876485,1,32,"English","en",105,"# Introduction\n## Real estate and housing price estimation needs\n## Challenges in building general predictive models\n## Computational methods and machine learning approaches\n# (Content beyond introduction) Model comparison and results","[{\"question\":\"Why is predicting construction project sales valuation important?\",\"answer\":\"Accurate prediction helps ensure construction project sustainability and supports decision-making related to demand, investment, evaluation, and tax inspections of housing prices.\"},{\"question\":\"What inputs and output does the study use for the machine learning models?\",\"answer\":\"The models use economic variables and indices (EV\\u0026Is) and physical and financial variables (P\\u0026F) as input features, and they generate sales valuation as the output.\"},{\"question\":\"Which machine learning model performs best and which performs worst?\",\"answer\":\"Extremely randomized trees delivers the best performance, while the decision tree model shows the least satisfactory performance for predicting sales valuation.\"}]","Comparison of Various Machine Learning Models for Estimating Construction Projects Sales Valuation - 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