Paper Title
House construction cost prediction
Article Identifiers
Authors
Subhaan Jirait , Shreyas Rede
Keywords
House price , Prediction , Machine learning , Construction
Abstract
House construction cost estimation plays a pivotal role in the dynamics of the real estate market and shapes the decisions of various stakeholders involved in the construction process. Understanding and accurately predicting the cost of building a house is essential for developers, contractors, architects, lenders, and prospective homeowners alike. It serves as the foundation for budgeting, pricing, financing, and project planning activities, influencing the feasibility and success of construction ventures. In response to these challenges, the application of machine learning (ML) and data analytics presents a promising solution for improving the accuracy and efficiency of construction cost estimation. Machine learning models can analyze vast amounts of historical data, identify patterns and correlations, and predict costs with higher precision. By continuously learning from new data, these models can adapt to changing market conditions and provide real-time insights.
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How To Cite (APA)
Subhaan Jirait & Shreyas Rede (May-2024). House construction cost prediction. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(5), h311-h342. https://ijnrd.org/papers/IJNRD2405751.pdf
Issue
Volume 9 Issue 5, May-2024
Pages : h311-h342
Other Publication Details
Paper Reg. ID: IJNRD_222572
Published Paper Id: IJNRD2405751
Downloads: 000121985
Research Area: Engineering
Country: Phaltan, Maharashtra, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2405751.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2405751
About Publisher
Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)
ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016
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This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition
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Current Issue: Volume 10 | Issue 10 | October 2025
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