Investigation Upon Factors Contributing to Housing Price Variations-Specifically Sydney for Reference
DOI:
https://doi.org/10.62051/4r609702Keywords:
Housing prices; Urban area housing; Multiple linear regression model; Influencing factors.Abstract
The purpose of the article is to determine the small aspects that can affect the overall housing prices around the globe. It will be established by looking into one specific area and finding the relationship between them. This research will be using a multiple linear regression model to find the connection. The study used 300 samples and 9 variables each, all are possible variables that can influence overall house price. The data are from Sydney Urban areas collected during the 2020-2021 house market. For the outcome to be valuable, this research will be comparing the significance and VIF value for those variables to find out the biggest connection. Through this process, the null data was deleted as much as possible. As a result, this article discovered that Safety and Median house rent are the biggest influencing factors to the house price. The two factors are the most considered during a house purchase. And the price of Sydney urban houses can be determined by those two factors.
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References
[1] K. Tsatsaronis, H. Zhu, What Drives Housing Price Dynamics: Cross-Country Evidence, Journal of Monetary Economics. (2004).
[2] K. Knoll, S. Moritz, S. Thomas, No Price Like Home: Global House Prices, 1870-2012, American Economic Review. 107(2) (2017) 331–353. DOI: https://doi.org/10.1257/aer.20150501
[3] J.Y. Campbell, J.F. Cocco, how do house prices affect consumption? Evidence from micro data, Journal of Monetary Economics. 54(3) (2007) 591–621. DOI: https://doi.org/10.1016/j.jmoneco.2005.10.016
[4] J. Pan, Y.Y. Luan, X.Y. Hong, M. Li, Analysis and prediction of second-hand housing prices in Qingdao based on integrated algorithms, Advances in Applied Mathematics. 12(4) (2023) 1671-1682. DOI: https://doi.org/10.12677/AAM.2023.124173
[5] X.J. Wang, Research on the impact of second-hand housing prices in Chongqing. Journal of Langfang Normal University (Natural Science Edition), 19(3) (2019).
[6] F.N. Chu, I.C. Tsai, do higher house prices indicate higher safety? Price volatility risk in major cities in Taiwan, International Journal of Strategic Property Management. 24(3) (2020) 165-181. DOI: https://doi.org/10.3846/ijspm.2020.12159
[7] C.G. Yang, H.B. Li, Population Migration, Changes in Residential Supply and Demand, and Regional Economic Development: An Economic Analysis of the Current "Man Snatching War" in Domestic Cities, Theoretical Investigation. 3 (2019) 93-98.
[8] V.N. Stijn, P.O. Weill, Why Has House Price Dispersion Gone Up, Review of Economic Studies. 77(4) (2010) 1567–1606. DOI: https://doi.org/10.1111/j.1467-937X.2010.00611.x
[9] A. Gustafsson, S. Wogenius, Modelling Apartment Prices with the Multiple Linear Regression Model, DIVA. (2014).
[10] Z. Peng, Q. Huang, Y.C. Han, Model Research on Forecast of Second-Hand House Price in Chengdu Based on XGboost Algorithm. 2019 IEEE 11th International Conference on Advanced Infocom Technology (ICAIT). IEEE, (2019). DOI: https://doi.org/10.1109/ICAIT.2019.8935894
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