1# RECORD 79ef2b5c97af212a2651b0df761a0e38797c72542# TITLE: Investigation Upon Factors Contributing to Housing Price Variations-Specifically Sydney for Reference3# YEAR: 2024.0 VENUE: Transactions on Economics, Business and Management Research DOI: 10.62051/4r6097024# AUTHORS: H. Pang5# PAGES: 5 CANDIDATE TABLE PAGES (1-based): [4]6# 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.789===== PAGE 1 (score 7, numeric-paren 0, table-words 0, var-words 7) =====1011 12Transactions on Economics, Business and Management Research 13ISSN: 2960-1789, eISSN: 2960-2254 | Volume 10 14MEEA 2024 15 16Content from this work may be used under the terms of CC BY-NC 4.0 licence (https://creativecommons.org/licenses/by-nc/4.0/). 17Published by Warwick Evans Publishing. 18WEP19Warwick20Evans21Publishing22Investigation Upon Factors Contributing to Housing Price 23Variations-Specifically Sydney for Reference 24Haowen Pang * 25High School affiliated to China Renmin University, Beijing, 100086, China 26* Corresponding Author Email: cnhpang2@babson.edu 27Abstract. The purpose of the article is to determine the small aspects that can affect the overall 28housing prices around the globe. It will be established by looking into one specific area and finding 29the relationship between them. This research will be using a multiple linear regression model to find 30the connection. The study used 300 samples and 9 variables each, all are possible variables that 31can influence overall house price. The data are from Sydney Urban areas collected during the 2020-322021 house market. For the outcome to be valuable, this research will be comparing the significance 33and VIF value for those variables to find out the biggest connection. Through this process, the null 34data was deleted as much as possible. As a result, this article discovered that Safety and Median 35house rent are the biggest influencing factors to the house price. The two factors are the most 36considered during a house purchase. And the price of Sydney urban houses can be determined by 37those two factors. 38Keywords: Housing prices; Urban area housing; Multiple linear regression model; Influencing 39factors. 401. Introduction 41Housing is a long-passed tradition and way of living from the beginning of the human race. It is 42crucial for living in society, but in the last two decades house price increased dramatically causing 43many people unable to purchase their own. Housing prices around the globe have increased along 44with the overall inevitable inflation, and even more [1]. In fact, house prices from the 19th century to 45the 20th century were quite stable compared to now. It started to bloom during the latter half of the 4620th century. All this house price rise can be attributed to one reason-rising land prices [2]. More than 47that, population growth can be seen through the reach of 8 billion people. With more people in total, 48buying a house became even more expensive [3]. Though such huge changes have happened around 49the globe, many people still have no idea what factor might have caused the increase in price in their 50surroundings. To understand how small factors influence overall house prices, those factors would 51need to be separated and evaluated. Therefore, this passage will provide data and evaluation focusing 52on which factors make the greatest impact on rental prices, and to help people see them for their 53selves. This paper is here to evaluate the factors that influence house price increases specifically in 54Sydney's urban areas as an example. 55Houses in recent culture has become the symbol of one's wealth. The overpriced real estate market 56makes people work extremely hard to get their own house. Many people pay their house dept for their 57entire life. Every month they have to take out large part of their income to pay the dept, leaving them 58little amount to live and enjoy life with family [4]. However, the cause of overprice is still under 59investigation and research. Some Chinese scholars such as Pan Jia and Wang Xiaojuan claimed year 60of building construction is a factor that can make influence to the overall price of houses, so in this 61research that will be included in this research data to validate it [5, 6]. Safety is also a key aspect 62people care greatly, so having it considered in the factors is crucial for house price. The analysis of 63housing market data from 1991 to 2017 for four cities reveals that the main financial hub has the 64highest house prices but the lowest risk, providing a possibility of a great influence factor [7]. Overall, 65this research uses 9 variables (Median House Rent(Per Week), Avg. Year Held, Traffic, Public 66Transport, Nature Environment, Noise, Family Friendliness, Pet Friendliness, and Safety) and one 676869===== PAGE 4 (score 19, numeric-paren 0, table-words 5, var-words 4) =====7071 72177 73Table 2. Regression coefficient table 74 75B 76S.E. 77Beta 78T 79significance 80VIF 81Constant -936039.200 377413.855 82 83-2.480 840.000 85 86X1 872827.938 88147.397 890.901 9019.186 910.000 921.280 93X2 9414090.483 9516372.360 960.038 970.861 980.392 991.142 100X3 10111733.213 10227402.432 1030.022 1040.428 1050.670 1061.587 107X4 10840492.296 10919812.065 1100.094 1112.044 1120.044 1131.223 114X5 11547097.655 11630603.334 1170.093 1181.539 1190.128 1202.101 121X6 122-45282.789 12325636.386 124-0.108 -1.766 1250.081 1262.180 127X7 12830444.195 12938947.510 1300.044 1310.782 1320.437 1331.865 134X8 135-71507.776 13638696.169 137-0.103 -1.848 1380.068 1391.808 140X9 14161962.171 14227131.132 1430.130 1442.284 1450.025 1461.882 147 1484. Conclusion 149This experiment included 300 sample houses from all over Sydney 2020-2021 urban areas and 150managed to use 9 different variables for analysis. Multiple linear regression analysis was chosen for 151its accuracy and efficiency. It provided enough data analysis to determine the variable that has the 152most impact on overall house price. Which is supported by the Pearson correlation coefficients. 153Moreover, analyzing the regression coefficient for multiple linear equation models has the ability to 154point out relationships between variables and housing prices that are difficult to seek. As for the 155overall conclusion, safety and house rental price became the most important factors that can influence 156the house price. And shockingly to know, traffic barely relates to the dependent variable which is the 157overall house price. This means people don't consider it as the key factor when buying a house. 158By investigating this topic, the research provides different aspects and different perspectives for 159people to understand how house prices can vary. Therefore, giving them a chance to take reference 160when they are purchasing their own home in the future. Surely this would help people by allowing 161them to know how house prices are influenced and avoid negative factors. With all the positives, 162however, there are still some inadequate parts that this research can still be improved. For example, 163the relatively small sample number, covered areas, and there are still some null information after the 164elimination. To improve, more data and information will have to be collected in the future in order to 165find more possible relationships between house prices and different factors. With more information 166it will be able to fill in the gaps in this research. 167References 168[1] K. Tsatsaronis, H. Zhu, What Drives Housing Price Dynamics: Cross-Country Evidence, Journal of Monetary 169Economics. (2004). 170[2] K. Knoll, S. Moritz, S. Thomas, No Price Like Home: Global House Prices, 1870-2012, American Economic Review. 171107(2) (2017) 331–353. 172[3] J.Y. Campbell, J.F. Cocco, how do house prices affect consumption? 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