1# RECORD 1ad007ac2e8575311162da739431607dfdaeeb1b2# TITLE: The Research on Factors Influencing Housing Prices-Take Chicago as an Example3# YEAR: 2025.0 VENUE: Theoretical and Natural Science DOI: 10.54254/2753-8818/2025.237564# AUTHORS: Zhaoyi Chen; Siqian Ji; Zi-Yu Li5# PAGES: 7 CANDIDATE TABLE PAGES (1-based): [4, 5, 6]6# ABSTRACT: This paper purposes to research what factor is influenced the house price in Chicago. The Multiple Linear Regression will be used as the main method in the article. The 158 samples are chosen to analyze the factors. According to this data, there are 8 variables will be related with housing price. There are the number of bedrooms, spaces, rooms, lots, taxes, bathrooms, garages, conditions respectively. Also, this paper considers the area, stories, guestroom, basement, hotwaterheating, air-conditioning, parking, prefarea, furnishing status, and use Pearson correlation to show the strength of them. To prove the accuracy of the set of data, the model will use the value of F to check. The fact is verifying that area, bedrooms, bathroom have significant relation with the house price in Chicago and the other factors are not have direct relationship with price. Overall, the house price in Chicago can be influenced by the condition of the environment around the house.789===== PAGE 1 (score 21, numeric-paren 0, table-words 0, var-words 21) =====1011Proceedings of the 3rd International Conference on Mathematical Physics and Computational Simulation12DOI: 10.54254/2753-8818/2025.2375613© 2025 The Authors. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.014(https://creativecommons.org/licenses/by/4.0/).1518816The Research on Factors Influencing Housing Prices-Take 17Chicago as an Example 18Zhaoyi Chen1*, Siqian Ji2, Ziyu Li3 191Capital Normal University High School, Beijing, China 202School of Adocte, Shanghai, China 213The Second High School attached to Normal University, Beijing, China 22*Corresponding Author. Email: ccczy080325@gmail.com 23Abstract: This paper purposes to research what factor is influenced the house price in 24Chicago. The Multiple Linear Regression will be used as the main method in the article. The 25158 samples are chosen to analyze the factors. According to this data, there are 8 variables 26will be related with housing price. There are the number of bedrooms, spaces, rooms, lots, 27taxes, bathrooms, garages, conditions respectively. Also, this paper considers the area, stories, 28guestroom, basement, hotwaterheating, air-conditioning, parking, prefarea, furnishing status, 29and use Pearson correlation to show the strength of them. To prove the accuracy of the set of 30data, the model will use the value of F to check. The fact is verifying that area, bedrooms, 31bathroom have significant relation with the house price in Chicago and the other factors are 32not have direct relationship with price. Overall, the house price in Chicago can be influenced 33by the condition of the environment around the house. 34Keywords: Housing prices, Chicago, multiple linear regression, influencing factors, 35correlation analysis. 361. 37Introduction 38This study uses XGBoost and SHAP value methods to analyze panel data of 70 Chinese cities from 392009 - 2019, aiming to identify key factors behind such price differentials. Findings show that 40expectation factors drive housing price divergence. Monetary, demand and supply factors aren't 41primary causes, yet can influence prices by shaping expectations. Thus, the essence of "city - specific 42policies" is stabilizing public price expectations. First - tier cities should control land costs and boost 43supply to counter upward price expectations. Second - and third - tier cities need to improve 44infrastructure and living standards to meet housing demand and prevent downward price expectations 45due to population outflow [1]. 46By reviewing the research findings of scholars on factors influencing housing prices, this study 47summarizes seven main factors potentially affecting Beijing's commercial housing prices from supply, 48demand, and macro - economic perspectives. These factors include the sales area of commercial 49housing, permanent population, the proportion of the population aged 15-64, per capita GDP, 50consumer price index, the land area purchased by real estate development enterprises, and inflation 51rate. Using Stata, a multiple linear regression model is established to analyze these related variables. 52The results show that all these factors have a significant impact on Beijing's housing prices. Based on 53the regression results, three policy recommendations are put forward for the government's macro - 545556===== PAGE 4 (score 29, numeric-paren 6, table-words 4, var-words 13) =====5758Proceedings of the 3rd International Conference on Mathematical Physics and Computational Simulation59DOI: 10.54254/2753-8818/2025.237566019161where y stands for the dependent variable (housing price), β0 the intercept, β1, β2 … β𝓃 are the 62coefficients for the independent variables, 𝓍1, 𝓍2 … 𝓍𝓃 are the independent variables (factors 63influencing housing prices), and ϵ is the error term. 643. 65Results and discussion 663.1. Correlation analysis 67The relevance analysis evaluates the relationships between housing prices and 11 potential 68independent variables as shown in the table 2. 69Table 2: Correlation analysis between dependent and independent variables 70 71price 72area 730.242* 74bedrooms 750.304** 76bathrooms 770.319** 78stories 790.017 80guestroom 810.031 82basement 830.093 84hotwaterheating 85-0.025 86furnishingstatus 87-0.069 88prefarea 890.107 90parking 910.411** 92airconditioning 930.098 94 95* p<0.05 ** p<0.01 96Based on the table 2, there are some key findings. First, bathrooms, bedrooms, and parking 97displayed moderate-to-strong positive correlations with price. Second, area showed a weak but 98statistically significant positive correlation. Third, variables such as stories, basement, and air-99conditioning (r = 0.098) exhibited approximately zero correlations with price (p > 0.05). 100As for the analysis of inter-variable (figure 1 shown), the correlations among potential influencing 101factors were uniformly weak (|r| approximately equal to zero), with no statistically significant 102relationships. The computed correlation coefficients (0.129, 0.073, -0.172, -0.140, 0.090, -0.139, -1030.122, 0.181, 0.095, -0.093 respectively) all demonstrated negligible magnitudes approaching zero. 104Furthermore, all corresponding p-values exceeded the 0.05 significance threshold. These statistical 105outcomes collectively indicate the absence of meaningful associations between the area measurement 106and the other investigated housing characteristics in this study. 107108109===== PAGE 5 (score 27, numeric-paren 1, table-words 2, var-words 16) =====110111Proceedings of the 3rd International Conference on Mathematical Physics and Computational Simulation112DOI: 10.54254/2753-8818/2025.23756113192114 115Figure 1: Relevance analysis among independent variables 1163.2. Multiple linear regression analysis 117As the result shown in the table 3, the multiple linear regression model for this dataset is: 118 119𝒴= 732673.319 + 124.558𝑥1 + 650566.462𝑥2 + ⋯+ 78390.114𝑥11 120(2) 121The regression model (F = 4.652, p < 0.001) explained 36.8% of the variance in housing prices 122(R² = 0.368), indicating that the influencing factors collectively account for a moderate portion of 123price fluctuations. Two variables demonstrated statistically significant effects: bathrooms and parking, 124reflecting buyers' prioritization of functional living spaces and vehicular accessibility. The positive 125coefficients suggest that each additional bathroom increases prices by approximately 650566 units, 126while an extra parking space adds 660661 units. 127Moreover, variables initially showing bivariate correlations with price, including area and 128bedrooms, became non-significant in the model. Non-significant results were also observed for stories, 129air-conditioning, and furnishing status. Variance Inflation Factor (VIF) values for all influential 130factors ranged between 1 and 5, confirming no severe multicollinearity. 131Table 3: Results of multiple linear regression analysis 132 133Non-standardized 134coefficients 135Standardized 136coefficients 137t 138p 139Collinearity 140Dianosis 141B 142Standard 143Error 144Beta 145VIF 146Tolerance 147Constant 148732673.319 1491664938.869 150- 1510.440 1520.661 153- 154- 155area 156124.558 15765.678 1580.177 1591.897 1600.061 1611.219 1620.820 163bathrooms 164650566.462 165230693.362 1660.265 1672.820 1680.006** 1691.227 1700.815 171stories 17287504.025 173144525.352 1740.065 1750.605 1760.546 1771.606 1780.623 179bedrooms 180372422.350 181220797.720 1820.155 1831.687 1840.095 1851.182 1860.846 187guestroom 18881401.929 189304786.009 1900.025 1910.267 1920.790 1931.191 1940.839 195basement 196403708.567 197333873.702 1980.134 1991.209 2000.230 2011.702 2020.588 203hotwaterheating 204486872.335 205510848.994 2060.099 2070.953 2080.343 2091.494 2100.669 211212213===== PAGE 6 (score 25, numeric-paren 1, table-words 3, var-words 15) =====214215Proceedings of the 3rd International Conference on Mathematical Physics and Computational Simulation216DOI: 10.54254/2753-8818/2025.23756217193218airconditioning 219530970.100 220339127.011 2210.166 2221.566 2230.121 2241.565 2250.639 226parking 227660661.380 228159352.363 2290.370 2304.146 2310.000** 2321.108 2330.903 234prefarea 235434783.946 236276309.617 2370.145 2381.574 2390.119 2401.183 2410.845 242furnishingstatus 24378390.114 244172544.131 2450.041 2460.454 2470.651 2481.113 2490.899 250R2 2510.368 252Adjusted R2 2530.289 254F 255F (11,88) =4.652, p=0.000 256D-W value 2570.797 258 2594. 260Conclusion 261This study utilized the Multiple Linear Regression model to explore the key factors influencing 262housing prices in Chicago. Through relevance analysis, it was found that bathrooms, bedrooms, and 263parking had moderate - to - strong positive correlations with housing prices. This indicates that the 264number of functional spaces like bathrooms and bedrooms, as well as the availability of parking, are 265important considerations for homebuyers in Chicago, and these factors can significantly affect 266housing prices. The area also showed a weak but significant positive correlation, suggesting that 267larger houses generally tend to have higher prices. However, variables such as stories, basements, 268and air - conditioning had nearly zero correlations with housing prices, meaning they may not be 269primary determinants of housing prices in this context. 270The multiple linear regression analysis showed that the model could explain 36.8% of the variance 271in housing prices. Bathrooms and parking had significant effects, with each additional bathroom 272increasing prices by approximately 650,566 units and an extra parking space adding 660,661 units. 273Nevertheless, some variables that initially showed correlations with housing prices, like area and 274bedrooms, became insignificant in the model. This might be due to the complex interactions among 275various factors or the limitations of the model. 276Overall, this research identified some important factors affecting housing prices in Chicago. 277However, it should be noted that the model has its limitations. The relatively small sample size of 278158 samples may not fully represent the entire Chicago housing market, which could lead to potential 279biases in the results. Also, there may be other unconsidered factors, such as local economic 280development trends, changes in interest rates, and new housing policies, that could impact housing 281prices. Future studies could expand the sample size, incorporate more comprehensive data, and 282consider additional influencing factors to improve the model's accuracy and explanatory power. This 283would provide a more in - depth understanding of the housing price mechanisms in Chicago and offer 284more reliable references for relevant stakeholders. 285Authors contribution 286All the authors contributed equally and their names were listed in alphabetical order. 287References 288[1] Chen, X.L., Chen, K., Wang, Z.R. and Xiao, Z.Y. (2024) Research on the influencing factors of housing price 289differentiation among cities. Economic Theory and Business Management, 2, 49-64. 290[2] Meng, K.Y. (2023) Analysis of the influencing factors of housing prices and policy recommendations-Taking Beijing 291as an example. Economic Research Guide, 22, 32-34. 292[3] Zha, H. (2023) Analysis of the influencing factors of housing prices in China based on panel data. Master's Thesis, 293Nanjing Audit University, Master. 294Table 3: (continued). 295296297===== PAGE 7 (score 3, numeric-paren 0, table-words 0, var-words 3) =====298299Proceedings of the 3rd International Conference on Mathematical Physics and Computational Simulation300DOI: 10.54254/2753-8818/2025.23756301194302[4] Yan, L.Y. (2023) Prediction of second - hand housing prices in Beijing and analysis of influencing factors based on 303optimization algorithms. Master's Thesis, Dongbei University of Finance and Economics, Master. 304[5] Norada Real Estate Investments. (2025) Chicago Housing Market: Trends and Forecast 2025-2026. Norada Real 305Estate Investments. Retrieved from https://www.noradarealestate.com/blog/chicago-real-estate-market/ 306[6] Federal Reserve Bank of Chicago. 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