Real Estate Cost Estimation Through Data Mining Techniques

Abstract Real estate is one of the most fast-paced and emerging industries today. Nowadays everyone wants to be the owner of their house rather than live on rent. Therefore, people are very cautious in searching for the most suitable house. Different people have a different budgets and so varies the...

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Bibliographic Details
Published inIOP conference series. Materials Science and Engineering Vol. 1099; no. 1; p. 12053
Main Authors Khare, Sandali, Gourisaria, Mahendra Kumar, Harshvardhan, GM, Joardar, Subhankar, Singh, Vijander
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.03.2021
Subjects
Data mining
Decision trees
Machine learning
Polynomials
Real estate
Regression
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Summary:Abstract Real estate is one of the most fast-paced and emerging industries today. Nowadays everyone wants to be the owner of their house rather than live on rent. Therefore, people are very cautious in searching for the most suitable house. Different people have a different budgets and so varies their desire. This paper draws attention to the house rate predictions based on different objectives like financial status and expectations of non-house holders. It consists of two prediction sets, one with all the available features required for buying the house and the other with a few selected features. It involves varying machine learning regression techniques like linear regression, polynomial regression, decision tree, and random forest. Here, all the above techniques are compared, and it is found that polynomial regression with all the features gives the best results.
ISSN:1757-8981
1757-899X
DOI:10.1088/1757-899X/1099/1/012053