Advances in Economics, Management and Political Sciences
- The Open Access Proceedings Series for Conferences
Proceedings of the 2022 International Conference on Financial Technology and Business Analysis (ICFTBA 2022), Part 2
Series Vol. 6 , 27 April 2023
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Access | Article
House Price Prediction with Big Data
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Abstract
Many of the current models for house price prediction focus on the house price index itself or include too few factors. In this research, however, different aspects of housing properties (bedrooms, bathrooms, grade, view, etc.) will be incorporated to generate a more accurate prediction model. Doing so not only remedies for a lack of variables in current models but also benefits the consumers by providing an accurate estimation of housing values. This paper examines the estimation methodology of a multiple linear regression model. To get the optimal prediction power and keep the model as simple as possible, different variable combinations will be tested. Through comparing different regression models and analyzing the regression results, the predictive model introduced in this paper has a high house price prediction power. This paper provides a potential solution for the prediction of the house price in the King County, Washington.
Keywords
House Price Prediction, Predictive Model, Data Analysis, Linear Regression, Pricing
References
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 2022 International Conference on Financial Technology and Business Analysis (ICFTBA 2022), Part 2
- ISBN (Print)
- 978-1-915371-23-2
- ISBN (Online)
- 978-1-915371-24-9
- Published Date
- 27 April 2023
- Series
- Advances in Economics, Management and Political Sciences
- ISSN (Print)
- 2754-1169
- ISSN (Online)
- 2754-1177
- DOI
- 10.54254/2754-1169/6/20220155
- Copyright
- 27 April 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited