# RECORD 2565afc9ee2e15c0daf31480b06229279a91d815 # TITLE: Study of the Factors Influencing House Prices in California, USA # YEAR: 2025.0 VENUE: Advances in Economics, Management and Political Sciences DOI: 10.54254/2754-1169/2024.19450 # AUTHORS: Shanchuan Zeng # PAGES: 5 CANDIDATE TABLE PAGES (1-based): [3, 4] # ABSTRACT: The world's population is growing at a rapid pace, which has raised concerns about the real estate market. This essay seeks to identify the factors that influence the cost of homes in California, USA. The findings can help individuals have a better grasp of home pricing and the variables that greatly affect a home's final cost. This article uses eight independent factors that have a significant impact on housing prices. The dataset is analyzed using the linear regression model. The location of the home, the number of bedrooms, and the median income are among the independent variables that were employed in this study and are critical to the model's outcomes. It is also explained how to make the model more multi-linear. The paper includes a correlation analysis that is utilized to enhance the prior model. The purpose of the comparison of the two models is to ascertain the benefits and drawbacks of each. The outcome suggests that the key elements pertaining to the median house value can be explained by four fundamental variables. ===== PAGE 1 (score 8, numeric-paren 0, table-words 0, var-words 8) ===== Study of the Factors Influencing House Prices in California, USA Shanchuan Zeng1,a,* 1Langley Secondary School, Langley, BC V2Y 2N1, Canada a. qinjiaomeng@ldy.edu.rs *corresponding author Abstract: The world's population is growing at a rapid pace, which has raised concerns about the real estate market. This essay seeks to identify the factors that influence the cost of homes in California, USA. The findings can help individuals have a better grasp of home pricing and the variables that greatly affect a home's final cost. This article uses eight independent factors that have a significant impact on housing prices. The dataset is analyzed using the linear regression model. The location of the home, the number of bedrooms, and the median income are among the independent variables that were employed in this study and are critical to the model's outcomes. It is also explained how to make the model more multi-linear. The paper includes a correlation analysis that is utilized to enhance the prior model. The purpose of the comparison of the two models is to ascertain the benefits and drawbacks of each. The outcome suggests that the key elements pertaining to the median house value can be explained by four fundamental variables. Keywords: Analysis of determinants, linear regression model, house prices. 1. Introduction This paper seeks to identify a suitable model that elucidates the elements driving California real estate prices. Because home prices are a major predictor of economic growth and stability, the housing market is vital to the state of the world economy as a whole. Examining the elements that affect a real estate property's value is important for both buyers and sellers. Graphs of linear functions can be used to display the home price in California, according to earlier studies on the factors influencing home prices in North America. Farhan, for instance, drew attention to the rising trend in residential property values nationwide as well as the government's receipt of property tax income per province and territory between 2017 and 2021. He also conducted study on Ontario's real estate bubble [1]. Regarding the factors that influence home values, Cohen and Karpaviciute made clear that factors like emigration, interest rates, and inflation are not only random variables as most people think [2]. In order to investigate the determinants, Zhang et al. used a non- linear modeling technique called Nonlinear Auto Regressive Moving Average with Exogenous inputs (NARMAX) [3]. The study of California's home prices was inspired by the research, despite the fact that it focused on the factors influencing China's real estate prices. According to Nistor and Reianu, Canada has a unique scenario because immigration has a significant impact on the country's economy. The survey also revealed that low mortgage interest rates are a contributing factor to Ontario's high pricing. The size of the household and income were also taken into consideration [4]. Harrison et al. Proceedings of the 3rd International Conference on Financial Technology and Business Analysis DOI: 10.54254/2754-1169/152/2024.19450 Β© 2024 The Authors. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (https://creativecommons.org/licenses/by/4.0/). 147 ===== PAGE 3 (score 23, numeric-paren 3, table-words 6, var-words 5) ===== Population X6 Count of the total number of population in the block Households X7 Count of the total number of households in the block Median_income X8 Median of the total household income of all the houses in the block 2.2. Method Introduction In this study, the statistical relationship between the independent and dependent variables can be modeled using linear regression. Thus, the following equation is applied: π‘Œ= 𝛼+ 𝛽1𝑋1 + β‹―+ 𝛽𝑝𝑋𝑝 (1) Where 𝛽𝑖 represents the regression coefficient. 3. Results and Discussion 3.1. Correlation Analysis Five of the variables' correlation analyses were processed, and the results showed that most of the variables had little to no effect on the median house value. There are still a few other factors that have a big impact on the price of a home. X4 and X8 have correlation coefficients of 0.254 and 0.616, respectively, as Table 2 illustrates. Given their strong link, X4 and X6 can both significantly affect the outcomes and have a positive correlation with the Y value. Table 2: Correlation results. Y X3 0.019 X4 0.254* X5 0.122 X6 0.129 X7 0.153 X8 0.616** * p<0.05 ** p<0.01 The bulk of these variables are not strictly related to one another, according to the results of the multi-collinearity test conducted on the eight variables. Furthermore, there are still a few factors that are connected to one another. For instance, X6 and X7 have a multi-collinearity of 0.945, as Table 3 illustrates. Because X6 and X7 are so similar, they may have an effect on the outcomes. When examining the model, these two could be removed to prevent that. Table 3: Multi-Collinearity. median_house_value housing_median_age total_rooms total_bedrooms population (X6) median_income households median_house_value 1 housing_median_age 0.019 1 total_rooms 0.254 -0.033 1 total_bedrooms 0.122 -0.135 0.869 1 population 0.129 -0.062 0.920 0.945 1 median_income 0.616 0.081 0.314 0.082 0.093 1 Households 0.153 -0.140 0.880 0.955 0.945 0.110 1 Table 1: (continued). Proceedings of the 3rd International Conference on Financial Technology and Business Analysis DOI: 10.54254/2754-1169/152/2024.19450 149 ===== PAGE 4 (score 31, numeric-paren 2, table-words 7, var-words 6) ===== 3.2. Descriptive Analysis All of the variables' data, including the number of samples, minimum, maximum, average value, standard deviation, and median value, are shown in the following table 4. For example, the independent variable X1, which denotes longitude, has a standard deviation of 0.015, which is so little that it falls within a range that indicates little influence on the price. Using the same technique, it is possible to ascertain that X1, X2, X3, and X8 all have comparatively low Standard deviation values, suggesting that they have no discernible effect on the price. In contrast, X4, X5, X6, and X7 all play significant roles in deciding the price because of their high Standard deviation values. Table 4: This caption has one line so it is centered. Minimum Maximum Average SD Median Y 60000.000 500001.000 151752.010 79735.891 131650.000 X1 -122.300 -122.220 -122.272 0.015 -122.70 X2 37.790 37.880 37.826 0.018 37.820 X3 2.000 52.000 44.020 12.061 50.000 X4 12.000 7099.000 1529.700 1132.602 1226.000 X5 4.000 2477.000 380.040 325.292 317.000 X6 18.000 3469.000 800.080 532.292 693.500 X7 7.000 2323.000 349.290 308.546 297.000 X8 0.500 8.325 2.178 1.430 1.850 3.3. Model Results The significance of each variable is shown in the table 5 below. A variable is considered more significant the smaller its p-value. The most significant factors are X1, X2, X5, and X8, as the table shows. X1 and X2 demonstrate that the property's location has a substantial impact on its price, while X5 indicates that buyers give careful consideration to the number of bedrooms when making a house purchase. Because it affects the buyer's capacity to invest in or acquire real estate, X8 illustrates how the median income has a substantial impact on the price of the property. Table 5: Model results. B Standard error Beta t p Constant 501003339.025 71639699.738 - 6.993 0.000** X1 3844993.974 519609.991 0.705 7.400 0.000** X2 -812912.108 442693.389 -0.181 -1.836 0.070 X3 125.158 480.062 0.019 0.261 0.795 X4 8.669 14.846 -0.123 -0.584 0.561 X5 -276.904 179.261 -1.130 -1.545 0.126 X6 31.488 40.220 0.210 0.783 0.436 X7 233.863 190.778 0.905 1.226 0.223 X8 17089.170 5082.675 0.306 3.362 0.001 X3, X4, and X6 are three inconsequential elements; therefore, the model can be improved by eliminating those variables. The value of R^2 is 0.612 according to the results of the second analysis, compared to 0.603 in the first. The comparison demonstrates that the model can explain higher percentages of the house value after removing the inconsequential variables, as indicated by the value of R^2, which represents the proportion of variables that influence the price of the house. Proceedings of the 3rd International Conference on Financial Technology and Business Analysis DOI: 10.54254/2754-1169/152/2024.19450 150 ===== PAGE 5 (score 7, numeric-paren 0, table-words 0, var-words 7) ===== 4. Conclusion When buying a residential home, a buyer's decision is influenced by a number of things. There were both quantitative and qualitative components in the original dataset. Eight indicators remain after a few that have no discernible impact are removed: Median House Value, Housing Median Age, Total Rooms, Total Bedrooms, Population, Households, and Median Income. These eight variables significantly influence how much a home costs. The Linear Regression model revealed that certain characteristics do not have a significant impact on the house value when assessing the variables' relative relevance. The sale price is significantly impacted by the eight variables included in the linear regression model. It is possible to clearly see the factors that determine the median home value. A comparison of the pre- and post-elimination findings for the inconsequential factors demonstrates that the model can account for house prices with a greater percentage once the unimportant variables have been reduced. References [1] Bayan, Y.F. (2024) Canada's leadership and housing affordability: Evidence from the Canadian real estate market. Journal of Urban Management, 13 (1), 52-61. [2] Viktorija, C. and Karpaviciute, L. (2017) The analysis of the determinants of housing prices. Independent journal of management & production, 8 (1), 049-063. [3] Zhang, Y. Hua, X.Y. and Zhao, L. (2012) Exploring determinants of housing prices: A case study of Chinese experience in 1999–2010. Economic modelling, 29 (6), 2349-2361. [4] Nistor, A. and Reianu, D. (2018) Determinants of housing prices: evidence from Ontario cities, 2001-2011. International Journal of Housing Markets and Analysis, 11 (3), 541-556. [5] Harrison, D, Greg, T.S. and Schwartz, A. (2001) Environmental determinants of housing prices: The impact of flood zone status. Journal of Real Estate Research, 21(1), 3-20. [6] Tripathi, S. (2019) Macroeconomic Determinants of Housing Prices: A Cross Country Level Analysis. MPRA Paper. [7] Hossain, B. and Latif, E. (2009) Determinants of housing price volatility in Canada: a dynamic analysis. Applied Economics, 41(27), 3521-3531. [8] Tanimowo, A. (2024) Determinants of House Prices in Nova Scotia During the 2010-2020 Period, Working paper. [9] Okkola, S. and Brunelle, C. (2018) The changing determinants of housing affordability in oil-booming agglomerations: A quantile regression investigation from Canada, 1991–2011. Housing studies, 33(6), 902-937. [10] Allen, J., Amano, R., Byrne, D.P. and Allan, W.G. (2009) Canadian city housing prices and urban market segmentation. Canadian Journal of Economics/Revue canadienne d'Γ©conomique, 42(3), 1132-1149. 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