1# RECORD 2565afc9ee2e15c0daf31480b06229279a91d8152# TITLE: Study of the Factors Influencing House Prices in California, USA3# YEAR: 2025.0 VENUE: Advances in Economics, Management and Political Sciences DOI: 10.54254/2754-1169/2024.194504# AUTHORS: Shanchuan Zeng5# PAGES: 5 CANDIDATE TABLE PAGES (1-based): [3, 4]6# 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.789===== PAGE 1 (score 8, numeric-paren 0, table-words 0, var-words 8) =====1011 12Study of the Factors Influencing House Prices in California, 13USA 14Shanchuan Zeng1,a,* 151Langley Secondary School, Langley, BC V2Y 2N1, Canada 16a. qinjiaomeng@ldy.edu.rs 17*corresponding author 18Abstract: The world's population is growing at a rapid pace, which has raised concerns about 19the real estate market. This essay seeks to identify the factors that influence the cost of homes 20in California, USA. The findings can help individuals have a better grasp of home pricing 21and the variables that greatly affect a home's final cost. This article uses eight independent 22factors that have a significant impact on housing prices. The dataset is analyzed using the 23linear regression model. The location of the home, the number of bedrooms, and the median 24income are among the independent variables that were employed in this study and are critical 25to the model's outcomes. It is also explained how to make the model more multi-linear. The 26paper includes a correlation analysis that is utilized to enhance the prior model. The purpose 27of the comparison of the two models is to ascertain the benefits and drawbacks of each. The 28outcome suggests that the key elements pertaining to the median house value can be explained 29by four fundamental variables. 30Keywords: Analysis of determinants, linear regression model, house prices. 311. 32Introduction 33This paper seeks to identify a suitable model that elucidates the elements driving California real estate 34prices. Because home prices are a major predictor of economic growth and stability, the housing 35market is vital to the state of the world economy as a whole. Examining the elements that affect a real 36estate property's value is important for both buyers and sellers. 37Graphs of linear functions can be used to display the home price in California, according to earlier 38studies on the factors influencing home prices in North America. Farhan, for instance, drew attention 39to the rising trend in residential property values nationwide as well as the government's receipt of 40property tax income per province and territory between 2017 and 2021. He also conducted study on 41Ontario's real estate bubble [1]. Regarding the factors that influence home values, Cohen and 42Karpaviciute made clear that factors like emigration, interest rates, and inflation are not only random 43variables as most people think [2]. In order to investigate the determinants, Zhang et al. used a non-44linear modeling technique called Nonlinear Auto Regressive Moving Average with Exogenous inputs 45(NARMAX) [3]. The study of California's home prices was inspired by the research, despite the fact 46that it focused on the factors influencing China's real estate prices. According to Nistor and Reianu, 47Canada has a unique scenario because immigration has a significant impact on the country's economy. 48The survey also revealed that low mortgage interest rates are a contributing factor to Ontario's high 49pricing. The size of the household and income were also taken into consideration [4]. Harrison et al. 50Proceedings of the 3rd International Conference on Financial Technology and Business Analysis 51DOI: 10.54254/2754-1169/152/2024.19450 52© 2024 The Authors. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 53(https://creativecommons.org/licenses/by/4.0/). 54147 555657===== PAGE 3 (score 23, numeric-paren 3, table-words 6, var-words 5) =====5859 60Population 61X6 62Count of the total number of population in the block 63Households 64X7 65Count of the total number of households in the block 66Median_income 67X8 68Median of the total household income of all the houses in 69the block 702.2. Method Introduction 71In this study, the statistical relationship between the independent and dependent variables can be 72modeled using linear regression. Thus, the following equation is applied: 73 74𝑌= 𝛼+ 𝛽1𝑋1 + ⋯+ 𝛽𝑝𝑋𝑝 75(1) 76Where 𝛽𝑖 represents the regression coefficient. 773. 78Results and Discussion 793.1. Correlation Analysis 80Five of the variables' correlation analyses were processed, and the results showed that most of the 81variables had little to no effect on the median house value. There are still a few other factors that have 82a big impact on the price of a home. X4 and X8 have correlation coefficients of 0.254 and 0.616, 83respectively, as Table 2 illustrates. Given their strong link, X4 and X6 can both significantly affect 84the outcomes and have a positive correlation with the Y value. 85Table 2: Correlation results. 86 87Y 88X3 890.019 90X4 910.254* 92X5 930.122 94X6 950.129 96X7 970.153 98X8 990.616** 100* p<0.05 ** p<0.01 101 102The bulk of these variables are not strictly related to one another, according to the results of the 103multi-collinearity test conducted on the eight variables. Furthermore, there are still a few factors that 104are connected to one another. For instance, X6 and X7 have a multi-collinearity of 0.945, as Table 3 105illustrates. Because X6 and X7 are so similar, they may have an effect on the outcomes. When 106examining the model, these two could be removed to prevent that. 107Table 3: Multi-Collinearity. 108 109median_house_value 110housing_median_age 111total_rooms 112 113total_bedrooms 114 115population 116(X6) 117median_income 118households 119 120median_house_value 1211 122 123 124 125 126 127 128housing_median_age 1290.019 1301 131 132 133 134 135 136total_rooms 1370.254 138-0.033 1391 140 141 142 143 144total_bedrooms 1450.122 146-0.135 1470.869 1481 149 150 151 152population 1530.129 154-0.062 1550.920 1560.945 1571 158 159 160median_income 1610.616 1620.081 1630.314 1640.082 1650.093 1661 167 168Households 1690.153 170-0.140 1710.880 1720.955 1730.945 1740.110 1751 176Table 1: (continued). 177Proceedings of the 3rd International Conference on Financial Technology and Business Analysis 178DOI: 10.54254/2754-1169/152/2024.19450 179149 180181182===== PAGE 4 (score 31, numeric-paren 2, table-words 7, var-words 6) =====183184 1853.2. Descriptive Analysis 186All of the variables' data, including the number of samples, minimum, maximum, average value, 187standard deviation, and median value, are shown in the following table 4. For example, the 188independent variable X1, which denotes longitude, has a standard deviation of 0.015, which is so 189little that it falls within a range that indicates little influence on the price. Using the same technique, 190it is possible to ascertain that X1, X2, X3, and X8 all have comparatively low Standard deviation 191values, suggesting that they have no discernible effect on the price. In contrast, X4, X5, X6, and X7 192all play significant roles in deciding the price because of their high Standard deviation values. 193Table 4: This caption has one line so it is centered. 194 195Minimum 196Maximum 197Average 198SD 199Median 200Y 20160000.000 202500001.000 203151752.010 20479735.891 205131650.000 206X1 207-122.300 208-122.220 209-122.272 2100.015 211-122.70 212X2 21337.790 21437.880 21537.826 2160.018 21737.820 218X3 2192.000 22052.000 22144.020 22212.061 22350.000 224X4 22512.000 2267099.000 2271529.700 2281132.602 2291226.000 230X5 2314.000 2322477.000 233380.040 234325.292 235317.000 236X6 23718.000 2383469.000 239800.080 240532.292 241693.500 242X7 2437.000 2442323.000 245349.290 246308.546 247297.000 248X8 2490.500 2508.325 2512.178 2521.430 2531.850 2543.3. Model Results 255The significance of each variable is shown in the table 5 below. A variable is considered more 256significant the smaller its p-value. The most significant factors are X1, X2, X5, and X8, as the table 257shows. X1 and X2 demonstrate that the property's location has a substantial impact on its price, while 258X5 indicates that buyers give careful consideration to the number of bedrooms when making a house 259purchase. Because it affects the buyer's capacity to invest in or acquire real estate, X8 illustrates how 260the median income has a substantial impact on the price of the property. 261Table 5: Model results. 262 263B 264Standard error 265Beta 266t 267p 268Constant 269501003339.025 27071639699.738 271- 2726.993 2730.000** 274X1 2753844993.974 276519609.991 2770.705 2787.400 2790.000** 280X2 281-812912.108 282442693.389 283-0.181 284-1.836 2850.070 286X3 287125.158 288480.062 2890.019 2900.261 2910.795 292X4 2938.669 29414.846 295-0.123 296-0.584 2970.561 298X5 299-276.904 300179.261 301-1.130 302-1.545 3030.126 304X6 30531.488 30640.220 3070.210 3080.783 3090.436 310X7 311233.863 312190.778 3130.905 3141.226 3150.223 316X8 31717089.170 3185082.675 3190.306 3203.362 3210.001 322 323X3, X4, and X6 are three inconsequential elements; therefore, the model can be improved by 324eliminating those variables. The value of R^2 is 0.612 according to the results of the second analysis, 325compared to 0.603 in the first. The comparison demonstrates that the model can explain higher 326percentages of the house value after removing the inconsequential variables, as indicated by the value 327of R^2, which represents the proportion of variables that influence the price of the house. 328Proceedings of the 3rd International Conference on Financial Technology and Business Analysis 329DOI: 10.54254/2754-1169/152/2024.19450 330150 331332333===== PAGE 5 (score 7, numeric-paren 0, table-words 0, var-words 7) =====334335 3364. 337Conclusion 338When buying a residential home, a buyer's decision is influenced by a number of things. There were 339both quantitative and qualitative components in the original dataset. Eight indicators remain after a 340few that have no discernible impact are removed: Median House Value, Housing Median Age, Total 341Rooms, Total Bedrooms, Population, Households, and Median Income. These eight variables 342significantly influence how much a home costs. 343The Linear Regression model revealed that certain characteristics do not have a significant impact 344on the house value when assessing the variables' relative relevance. The sale price is significantly 345impacted by the eight variables included in the linear regression model. It is possible to clearly see 346the factors that determine the median home value. 347A comparison of the pre- and post-elimination findings for the inconsequential factors 348demonstrates that the model can account for house prices with a greater percentage once the 349unimportant variables have been reduced. 350References 351[1] Bayan, Y.F. (2024) Canada's leadership and housing affordability: Evidence from the Canadian real estate market. 352Journal of Urban Management, 13 (1), 52-61. 353[2] Viktorija, C. and Karpaviciute, L. (2017) The analysis of the determinants of housing prices. Independent journal 354of management & production, 8 (1), 049-063. 355[3] Zhang, Y. Hua, X.Y. and Zhao, L. (2012) Exploring determinants of housing prices: A case study of Chinese 356experience in 1999–2010. Economic modelling, 29 (6), 2349-2361. 357[4] Nistor, A. and Reianu, D. (2018) Determinants of housing prices: evidence from Ontario cities, 2001-2011. 358International Journal of Housing Markets and Analysis, 11 (3), 541-556. 359[5] Harrison, D, Greg, T.S. and Schwartz, A. (2001) Environmental determinants of housing prices: The impact of flood 360zone status. Journal of Real Estate Research, 21(1), 3-20. 361[6] Tripathi, S. (2019) Macroeconomic Determinants of Housing Prices: A Cross Country Level Analysis. MPRA Paper. 362[7] Hossain, B. and Latif, E. (2009) Determinants of housing price volatility in Canada: a dynamic analysis. Applied 363Economics, 41(27), 3521-3531. 364[8] Tanimowo, A. (2024) Determinants of House Prices in Nova Scotia During the 2010-2020 Period, Working paper. 365[9] Okkola, S. and Brunelle, C. (2018) The changing determinants of housing affordability in oil-booming 366agglomerations: A quantile regression investigation from Canada, 1991–2011. Housing studies, 33(6), 902-937. 367[10] Allen, J., Amano, R., Byrne, D.P. and Allan, W.G. (2009) Canadian city housing prices and urban market 368segmentation. Canadian Journal of Economics/Revue canadienne d'économique, 42(3), 1132-1149. 369 370Proceedings of the 3rd International Conference on Financial Technology and Business Analysis 371DOI: 10.54254/2754-1169/152/2024.19450 372151 373