Estimation of house property value with hybrid machine learning

S. Suguna Mallika; Ch. Sarada; G. Swetha

doi:10.1117/12.3030415

26 June 2024 Estimation of house property value with hybrid machine learning

S. Suguna Mallika, Ch. Sarada, G. Swetha

Proceedings Volume 13188, International Conference on Medical Imaging, Electronic Imaging, Information Technologies, and Sensors (MIEITS 2024); 131881A (2024) https://doi.org/10.1117/12.3030415
Event: International Conference on Medical Imaging, Electronic Imaging, Information Technologies, and Sensors (MIEITS 2024), 2024, Kuala Lumpur, Malaysia

Abstract

Numerous variables contribute to wide swings in the price of homes. Size, proximity to services, and other conveniences all play a role. However, in certain instances, these costs are not based on accurate value and are, instead, inflated. Many of us have probably come into situations in which the price of a home seemed excessively high for reasons that ultimately didn't matter to us. The goal of this work is to establish home values by considering a wide range of tangible characteristics. We need a model that takes into account all relevant factors to arrive at a fair pricing that works for both the buyer and the seller. For this reason, rather of depending on just one Machine Learning algorithm to make home price predictions, a combined approach is proposed. We can make sense of the various algorithmic outputs and formulate a rule that makes use of all these numbers. The cost of a home will be settled according to this guideline. The proposed approach would reduce errors and house price prediction precision also increases.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

S. Suguna Mallika, Ch. Sarada, and G. Swetha "Estimation of house property value with hybrid machine learning", Proc. SPIE 13188, International Conference on Medical Imaging, Electronic Imaging, Information Technologies, and Sensors (MIEITS 2024), 131881A (26 June 2024); https://doi.org/10.1117/12.3030415

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