ABSTRACT
Buying a house of their own is a goal of most people, regardless of where they come from and their purchasing power. In order to help people realize this goal within their means, regression techniques such as Multiple linear, Ridge and Lasso regressions and other methods may be applied to determine a fair price. To that end, this paper is based on analysis done by applying the three regression models on a dataset obtained from the King County, WA, United States, to understand which model is most effective to achieve the above goal.
- Y. Wang, S. Wang, and G. Li. 2017. Identifying the determinants of housing prices in China using spatial regression and the geographical detector technique. Applied Geography 79, 26-36. https://doi.org/10.1016/j.apgeog.2016.12.003Google ScholarCross Ref
- K. Cao, M. Diao, and B. Wu. 2018. A Big Data–Based Geographically Weighted Regression Model for Public Housing Prices: A Case Study in Singapore. Annals of the American Association of Geographers 109, 1, 173-186.https://doi.org/10.1080/24694452.2018.1470925Google ScholarCross Ref
- S. Lu, Z. Li, Z. Qin, X. Yang, and R. Goh. 2017. A hybrid regression technique for house prices prediction. 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). https://doi.org/10.1109/IEEM.2017.8289904Google ScholarCross Ref
- N. Hoda, S. Jafri, N. Ahmad, and S. Hussain. 2020. An Empirical Testing of a House Pricing Model in the Indian Market. The Journal of Asian Finance, Economics and Business 7, 8, 33-40. https://doi.org/10.13106/jafeb.2020.vol7.no8.033Google ScholarCross Ref
- H. Selim. 2009. Determinants of house prices in Turkey: Hedonic regression versus artificial neural network. Expert Systems with Applications 36, 2, 2843-2852. https://doi.org/10.1016/j.eswa.2008.01.044Google ScholarDigital Library
- E. Ahmed, and M.N. Moustafa. 2016. House Price Estimation from Visual and Textual Features. IJCCI. https://arxiv.org/abs/1609.08399Google Scholar
- C. Madhuri, G. Anuradha, and M. Pujitha. 2019. House Price Prediction Using Regression Techniques: A Comparative Study. 2019 International Conference on Smart Structures and Systems (ICSSS). https://doi.org/10.1109/ICSSS.2019.8882834Google ScholarCross Ref
- T. Hülagü, E. Kızılkaya, Ali Gencay and P. Tunar. 2016. "A Hedonic House Price Index for Turkey," Chapters from NBP Conference Publications, in: Hanna Augustyniak & Jacek Łaszek & Krzysztof Olszewski & Joanna Waszczuk (ed.), Papers presented during the Narodowy Bank Polski Workshop: Recent trends in the real estate market and its analysis - 2015 edition, chapter 15, pages 179-202, Narodowy Bank Polski, Economic Research Department.Google Scholar
- H. Wu, H. Jiao, and Y. Yu. 2018. Influence Factors and Regression Model of Urban Housing Prices Based on Internet Open Access Data. Sustainability 10, 5, 1676. https://doi.org/10.3390/su10051676Google ScholarCross Ref
- G. Uzut, and S. Buyrukoglu. (2020). Prediction of real estate prices with data mining algorithms. Euroasia Journal of Mathematics Engineering Natural and Medical Sciences. 7. 77-84.Google Scholar
- H. Yu, and J. Wu. 2016. Real estate price prediction with regression and classification. CS229 (Machine Learning) Final Project Reports. http://cs229.stanford.edu/proj2016/report/WuYu_HousingPrice_report.pdfGoogle Scholar
- Brian K. Reid. 1980. A high-level approach to computer document formatting. In Proceedings of the 7th Annual Symposium on Principles of Programming Languages. ACM, New York, 24–31. https://doi.org/10.1145/567446.567449Google ScholarDigital Library
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