Effect of Housing Factors on Price in Seattle During the COVID-19 Pandemic
DOI:
https://doi.org/10.54097/ezaycc33Keywords:
Housing Price, Linear regression, Multicollinearity.Abstract
Due to the influence of the COVID-19 pandemic, global economic trends have faced an immense change. The major difficulty experienced by workers, laborers, and households are the tremendous changes in housing prices in every part of the world which are both hard to bear and almost impossible to forecast from current factors. With a particular dataset based on the housing price in Seattle, Oregon, USA. This research will use a multiple linear regression model to predict the housing price through various housing factors and to compare the accuracy of the prediction and the relationship between variables by contrasting the train and test set whilst also constructing a residual graph. The result ends up with a moderate accuracy due to the influence of multicollinearity between the factors, this can be seen through the variance Inflation Factor. The process requires the standardization of the data in order to make the test legitimate.
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Abigail Bola Adetunji, Oluwatobi Noah Akande, Funmilola Alaba Ajala, Ololade Oyewo, Yetunde Faith Akande, Gbenle Oluwadara, House Price Prediction using Random Forest Machine Learning Technique, Procedia Computer Science, Volume 199, 2022, Pages 806-813, ISSN 1877 - 0509.
Ghosalkar, Nehal N., and Sudhir N. Dhage. "Real estate value prediction using linear regression." 2018 fourth international conference on computing communication control and automation (ICCUBEA). IEEE, 2018.
Piao, Yong, Ansheng Chen, and Zhendong Shang. "Housing price prediction based on CNN." 2019 9th international conference on information science and technology (ICIST). IEEE, 2019.
G. Naga Satish, Ch. V. Raghavendran, M. D. Sugnana Rao, Ch. Srinivasulu, House Price Prediction Using Machine Learning, International Journal of Innovative Technology and Exploring Engineering (IJITEE), Volume-8 Issue-9, July 2019, ISSN: 2278 - 3075.
Manasa, J., Radha Gupta, and N. S. Narahari. "Machine learning based predicting house prices using regression techniques." 2020 2nd International conference on innovative mechanisms for industry applications (ICIMIA). IEEE, 2020.
Byeonghwa Park, Jae Kwon Bae, Using machine learning algorithms for housing price prediction: The case of Fairfax County, Virginia housing data, Expert Systems with Applications, Volume 42, Issue 6, 2015, Pages 2928 - 2934, ISSN 0957 - 4174.
Samuel Cortinhas, HOUSE PRICE PREDICTION - SEATTLE, 2022, https://www.kaggle.com/datasets/samuelcortinhas/house-price-prediction-seattle?select=test.csv,https://www.kaggle.com/datasets/samuelcortinhas/house-price-prediction-seattle?select=train.csv.
Shrestha, Noora. "Detecting multicollinearity in regression analysis." American Journal of Applied Mathematics and Statistics 8.2 (2020): 39 - 42.
D. Banerjee and S. Dutta, "Predicting the housing price direction using machine learning techniques," 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), Chennai, India, 2017, pp. 2998 - 3000.
Tay, Richard. "Correlation, variance inflation and multicollinearity in regression model." Journal of the Eastern Asia Society for Transportation Studies 12 (2017): 2006 - 2015.
Montgomery, Douglas C., Elizabeth A. Peck, and G. Geoffrey Vining. Introduction to linear regression analysis. John Wiley & Sons, 2021.
Diskin, M. H. "Definition and uses of the linear regression model." Water Resources Research 6.6 (1970): 1668 - 1673.
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