Utilize the Machine Learning Models to Forecast Home Values.:Seattle U.S.

Yunqi Zhang

doi:10.62051/7gzntt12

Authors

Yunqi Zhang

DOI:

https://doi.org/10.62051/7gzntt12

Keywords:

Machine learning, ensemble learning, housing price forecast, neural network

Abstract

Understanding housing price predictions assists the government in better adjusting and formulating relevant policies to promote economic stability and sustainable social development. In this study, researchers collected a large amount of data on the Seattle real estate market, including housing characteristics (such as area, number of bedrooms, number of bathrooms), geographical location (such as neighborhood, nearby facilities), and historical price data. This paper used this data to train and test linear regression, and KNN prediction models to forecast future housing price trends. The linear regression model, on the other hand, models the linear relationship between a single independent variable and the dependent variable, predicting housing prices by fitting an optimal line. The KNN prediction model, based on the nearest neighbor algorithm, predicts by searching for the K nearest neighbor samples closest to the target sample. Researchers will compare the accuracy and effectiveness of these three methods in predicting Seattle housing prices to determine which method is most suitable for housing price forecasting. Through this analysis, they aim to provide a reliable housing price prediction model for local residents to help them make wiser real estate decisions.

Downloads

Download data is not yet available.

References

M. Monson, "Valuation using hedonic pricing models." Journal of Property Research, 26(1), 75-88 (2009).

C. Zou, "The House Price Prediction Using Machine Learning Algorithm: The Case of Jinan, China." Journal of Real Estate Data Science, 8(2), 123-136 (2023).

L. Li, L., K.H. Chu, "Prediction of real estate price variation based on economic parameters." 2017 International Conference on Applied System Innovation (ICASI). 87-90 (2017). DOI: https://doi.org/10.1109/ICASI.2017.7988353

O.I. Abiodun, A. Jantan, A., Omolara, "State-of-the-art in artificial neural network applications: A survey." Heliyon 4.11, e00938 (2018). DOI: https://doi.org/10.1016/j.heliyon.2018.e00938

E. Ahmed, M. Moustafa, "House price estimation from visual and textual features." arXiv preprint arXiv: 1609.08399, (2016). DOI: https://doi.org/10.5220/0006040700620068

Q. Truong, M. Nguyen, H. Dang, H., B. Mei, "Housing price prediction via improved machine learning techniques." Procedia Computer Science 174, 433-442 (2020). DOI: https://doi.org/10.1016/j.procs.2020.06.111

S Levantesi, G. Piscopo, "The importance of economic variables on London real estate market: A random forest approach." Risks 8.4, 112 (2020). DOI: https://doi.org/10.3390/risks8040112

T.D. Phan, "Housing price prediction using machine learning algorithms: The case of Melbourne city, Australia." 2018 International Conference on Machine Learning and Data Engineering (iCMLDE). 35-42, (2018). DOI: https://doi.org/10.1109/iCMLDE.2018.00017

"Machine Learning Housing Price Prediction in Petaling Jaya, Selangor, Malaysia." Journal of Real Estate Analytics (2020).

S. Putatunda, "PropTech for Proactive Pricing of Houses in Classified Advertisements in the Indian Real Estate Market." International Journal of Real Estate Technology and Innovation (2021).