# RECORD 79ef2b5c97af212a2651b0df761a0e38797c7254 # TITLE: Investigation Upon Factors Contributing to Housing Price Variations-Specifically Sydney for Reference # YEAR: 2024.0 VENUE: Transactions on Economics, Business and Management Research DOI: 10.62051/4r609702 # AUTHORS: H. Pang # PAGES: 5 CANDIDATE TABLE PAGES (1-based): [4] # 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. ===== PAGE 1 (score 7, numeric-paren 0, table-words 0, var-words 7) ===== Transactions on Economics, Business and Management Research ISSN: 2960-1789, eISSN: 2960-2254 | Volume 10 MEEA 2024 Content from this work may be used under the terms of CC BY-NC 4.0 licence (https://creativecommons.org/licenses/by-nc/4.0/). Published by Warwick Evans Publishing. WEP Warwick Evans Publishing Investigation Upon Factors Contributing to Housing Price Variations-Specifically Sydney for Reference Haowen Pang * High School affiliated to China Renmin University, Beijing, 100086, China * Corresponding Author Email: cnhpang2@babson.edu 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. Keywords: Housing prices; Urban area housing; Multiple linear regression model; Influencing factors. 1. Introduction Housing is a long-passed tradition and way of living from the beginning of the human race. It is crucial for living in society, but in the last two decades house price increased dramatically causing many people unable to purchase their own. Housing prices around the globe have increased along with the overall inevitable inflation, and even more [1]. In fact, house prices from the 19th century to the 20th century were quite stable compared to now. It started to bloom during the latter half of the 20th century. All this house price rise can be attributed to one reason-rising land prices [2]. More than that, population growth can be seen through the reach of 8 billion people. With more people in total, buying a house became even more expensive [3]. Though such huge changes have happened around the globe, many people still have no idea what factor might have caused the increase in price in their surroundings. To understand how small factors influence overall house prices, those factors would need to be separated and evaluated. Therefore, this passage will provide data and evaluation focusing on which factors make the greatest impact on rental prices, and to help people see them for their selves. This paper is here to evaluate the factors that influence house price increases specifically in Sydney's urban areas as an example. Houses in recent culture has become the symbol of one's wealth. The overpriced real estate market makes people work extremely hard to get their own house. Many people pay their house dept for their entire life. Every month they have to take out large part of their income to pay the dept, leaving them little amount to live and enjoy life with family [4]. However, the cause of overprice is still under investigation and research. Some Chinese scholars such as Pan Jia and Wang Xiaojuan claimed year of building construction is a factor that can make influence to the overall price of houses, so in this research that will be included in this research data to validate it [5, 6]. Safety is also a key aspect people care greatly, so having it considered in the factors is crucial for house price. The analysis of housing market data from 1991 to 2017 for four cities reveals that the main financial hub has the highest house prices but the lowest risk, providing a possibility of a great influence factor [7]. Overall, this research uses 9 variables (Median House Rent(Per Week), Avg. Year Held, Traffic, Public Transport, Nature Environment, Noise, Family Friendliness, Pet Friendliness, and Safety) and one ===== PAGE 4 (score 19, numeric-paren 0, table-words 5, var-words 4) ===== 177 Table 2. Regression coefficient table B S.E. Beta T significance VIF Constant -936039.200 377413.855 -2.480 0.000 X1 2827.938 147.397 0.901 19.186 0.000 1.280 X2 14090.483 16372.360 0.038 0.861 0.392 1.142 X3 11733.213 27402.432 0.022 0.428 0.670 1.587 X4 40492.296 19812.065 0.094 2.044 0.044 1.223 X5 47097.655 30603.334 0.093 1.539 0.128 2.101 X6 -45282.789 25636.386 -0.108 -1.766 0.081 2.180 X7 30444.195 38947.510 0.044 0.782 0.437 1.865 X8 -71507.776 38696.169 -0.103 -1.848 0.068 1.808 X9 61962.171 27131.132 0.130 2.284 0.025 1.882 4. Conclusion This experiment included 300 sample houses from all over Sydney 2020-2021 urban areas and managed to use 9 different variables for analysis. Multiple linear regression analysis was chosen for its accuracy and efficiency. It provided enough data analysis to determine the variable that has the most impact on overall house price. Which is supported by the Pearson correlation coefficients. Moreover, analyzing the regression coefficient for multiple linear equation models has the ability to point out relationships between variables and housing prices that are difficult to seek. As for the overall conclusion, safety and house rental price became the most important factors that can influence the house price. And shockingly to know, traffic barely relates to the dependent variable which is the overall house price. This means people don't consider it as the key factor when buying a house. By investigating this topic, the research provides different aspects and different perspectives for people to understand how house prices can vary. Therefore, giving them a chance to take reference when they are purchasing their own home in the future. Surely this would help people by allowing them to know how house prices are influenced and avoid negative factors. With all the positives, however, there are still some inadequate parts that this research can still be improved. For example, the relatively small sample number, covered areas, and there are still some null information after the elimination. To improve, more data and information will have to be collected in the future in order to find more possible relationships between house prices and different factors. With more information it will be able to fill in the gaps in this research. 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. [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. [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. [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. ===== PAGE 5 (score 1, numeric-paren 0, table-words 0, var-words 1) ===== 178 [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. [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).