# RECORD 1ad007ac2e8575311162da739431607dfdaeeb1b # TITLE: The Research on Factors Influencing Housing Prices-Take Chicago as an Example # YEAR: 2025.0 VENUE: Theoretical and Natural Science DOI: 10.54254/2753-8818/2025.23756 # AUTHORS: Zhaoyi Chen; Siqian Ji; Zi-Yu Li # PAGES: 7 CANDIDATE TABLE PAGES (1-based): [4, 5, 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. ===== PAGE 1 (score 21, numeric-paren 0, table-words 0, var-words 21) ===== Proceedings of the 3rd International Conference on Mathematical Physics and Computational Simulation DOI: 10.54254/2753-8818/2025.23756 © 2025 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/). 188 The Research on Factors Influencing Housing Prices-Take Chicago as an Example Zhaoyi Chen1*, Siqian Ji2, Ziyu Li3 1Capital Normal University High School, Beijing, China 2School of Adocte, Shanghai, China 3The Second High School attached to Normal University, Beijing, China *Corresponding Author. Email: ccczy080325@gmail.com 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. Keywords: Housing prices, Chicago, multiple linear regression, influencing factors, correlation analysis. 1. Introduction This study uses XGBoost and SHAP value methods to analyze panel data of 70 Chinese cities from 2009 - 2019, aiming to identify key factors behind such price differentials. Findings show that expectation factors drive housing price divergence. Monetary, demand and supply factors aren't primary causes, yet can influence prices by shaping expectations. Thus, the essence of "city - specific policies" is stabilizing public price expectations. First - tier cities should control land costs and boost supply to counter upward price expectations. Second - and third - tier cities need to improve infrastructure and living standards to meet housing demand and prevent downward price expectations due to population outflow [1]. By reviewing the research findings of scholars on factors influencing housing prices, this study summarizes seven main factors potentially affecting Beijing's commercial housing prices from supply, demand, and macro - economic perspectives. These factors include the sales area of commercial housing, permanent population, the proportion of the population aged 15-64, per capita GDP, consumer price index, the land area purchased by real estate development enterprises, and inflation rate. Using Stata, a multiple linear regression model is established to analyze these related variables. The results show that all these factors have a significant impact on Beijing's housing prices. Based on the regression results, three policy recommendations are put forward for the government's macro - ===== PAGE 4 (score 29, numeric-paren 6, table-words 4, var-words 13) ===== Proceedings of the 3rd International Conference on Mathematical Physics and Computational Simulation DOI: 10.54254/2753-8818/2025.23756 191 where y stands for the dependent variable (housing price), β0 the intercept, β1, β2 … β𝓃 are the coefficients for the independent variables, 𝓍1, 𝓍2 … 𝓍𝓃 are the independent variables (factors influencing housing prices), and ϵ is the error term. 3. Results and discussion 3.1. Correlation analysis The relevance analysis evaluates the relationships between housing prices and 11 potential independent variables as shown in the table 2. Table 2: Correlation analysis between dependent and independent variables price area 0.242* bedrooms 0.304** bathrooms 0.319** stories 0.017 guestroom 0.031 basement 0.093 hotwaterheating -0.025 furnishingstatus -0.069 prefarea 0.107 parking 0.411** airconditioning 0.098 * p<0.05 ** p<0.01 Based on the table 2, there are some key findings. First, bathrooms, bedrooms, and parking displayed moderate-to-strong positive correlations with price. Second, area showed a weak but statistically significant positive correlation. Third, variables such as stories, basement, and air- conditioning (r = 0.098) exhibited approximately zero correlations with price (p > 0.05). As for the analysis of inter-variable (figure 1 shown), the correlations among potential influencing factors were uniformly weak (|r| approximately equal to zero), with no statistically significant relationships. The computed correlation coefficients (0.129, 0.073, -0.172, -0.140, 0.090, -0.139, - 0.122, 0.181, 0.095, -0.093 respectively) all demonstrated negligible magnitudes approaching zero. Furthermore, all corresponding p-values exceeded the 0.05 significance threshold. These statistical outcomes collectively indicate the absence of meaningful associations between the area measurement and the other investigated housing characteristics in this study. ===== PAGE 5 (score 27, numeric-paren 1, table-words 2, var-words 16) ===== Proceedings of the 3rd International Conference on Mathematical Physics and Computational Simulation DOI: 10.54254/2753-8818/2025.23756 192 Figure 1: Relevance analysis among independent variables 3.2. Multiple linear regression analysis As the result shown in the table 3, the multiple linear regression model for this dataset is: 𝒴= 732673.319 + 124.558𝑥1 + 650566.462𝑥2 + ⋯+ 78390.114𝑥11 (2) The regression model (F = 4.652, p < 0.001) explained 36.8% of the variance in housing prices (R² = 0.368), indicating that the influencing factors collectively account for a moderate portion of price fluctuations. Two variables demonstrated statistically significant effects: bathrooms and parking, reflecting buyers' prioritization of functional living spaces and vehicular accessibility. The positive coefficients suggest that each additional bathroom increases prices by approximately 650566 units, while an extra parking space adds 660661 units. Moreover, variables initially showing bivariate correlations with price, including area and bedrooms, became non-significant in the model. Non-significant results were also observed for stories, air-conditioning, and furnishing status. Variance Inflation Factor (VIF) values for all influential factors ranged between 1 and 5, confirming no severe multicollinearity. Table 3: Results of multiple linear regression analysis Non-standardized coefficients Standardized coefficients t p Collinearity Dianosis B Standard Error Beta VIF Tolerance Constant 732673.319 1664938.869 - 0.440 0.661 - - area 124.558 65.678 0.177 1.897 0.061 1.219 0.820 bathrooms 650566.462 230693.362 0.265 2.820 0.006** 1.227 0.815 stories 87504.025 144525.352 0.065 0.605 0.546 1.606 0.623 bedrooms 372422.350 220797.720 0.155 1.687 0.095 1.182 0.846 guestroom 81401.929 304786.009 0.025 0.267 0.790 1.191 0.839 basement 403708.567 333873.702 0.134 1.209 0.230 1.702 0.588 hotwaterheating 486872.335 510848.994 0.099 0.953 0.343 1.494 0.669 ===== PAGE 6 (score 25, numeric-paren 1, table-words 3, var-words 15) ===== Proceedings of the 3rd International Conference on Mathematical Physics and Computational Simulation DOI: 10.54254/2753-8818/2025.23756 193 airconditioning 530970.100 339127.011 0.166 1.566 0.121 1.565 0.639 parking 660661.380 159352.363 0.370 4.146 0.000** 1.108 0.903 prefarea 434783.946 276309.617 0.145 1.574 0.119 1.183 0.845 furnishingstatus 78390.114 172544.131 0.041 0.454 0.651 1.113 0.899 R2 0.368 Adjusted R2 0.289 F F (11,88) =4.652, p=0.000 D-W value 0.797 4. Conclusion This study utilized the Multiple Linear Regression model to explore the key factors influencing housing prices in Chicago. Through relevance analysis, it was found that bathrooms, bedrooms, and parking had moderate - to - strong positive correlations with housing prices. This indicates that the number of functional spaces like bathrooms and bedrooms, as well as the availability of parking, are important considerations for homebuyers in Chicago, and these factors can significantly affect housing prices. The area also showed a weak but significant positive correlation, suggesting that larger houses generally tend to have higher prices. However, variables such as stories, basements, and air - conditioning had nearly zero correlations with housing prices, meaning they may not be primary determinants of housing prices in this context. The multiple linear regression analysis showed that the model could explain 36.8% of the variance in housing prices. Bathrooms and parking had significant effects, with each additional bathroom increasing prices by approximately 650,566 units and an extra parking space adding 660,661 units. Nevertheless, some variables that initially showed correlations with housing prices, like area and bedrooms, became insignificant in the model. This might be due to the complex interactions among various factors or the limitations of the model. Overall, this research identified some important factors affecting housing prices in Chicago. However, it should be noted that the model has its limitations. The relatively small sample size of 158 samples may not fully represent the entire Chicago housing market, which could lead to potential biases in the results. Also, there may be other unconsidered factors, such as local economic development trends, changes in interest rates, and new housing policies, that could impact housing prices. Future studies could expand the sample size, incorporate more comprehensive data, and consider additional influencing factors to improve the model's accuracy and explanatory power. This would provide a more in - depth understanding of the housing price mechanisms in Chicago and offer more reliable references for relevant stakeholders. Authors contribution All the authors contributed equally and their names were listed in alphabetical order. References [1] Chen, X.L., Chen, K., Wang, Z.R. and Xiao, Z.Y. (2024) Research on the influencing factors of housing price differentiation among cities. Economic Theory and Business Management, 2, 49-64. [2] Meng, K.Y. (2023) Analysis of the influencing factors of housing prices and policy recommendations-Taking Beijing as an example. Economic Research Guide, 22, 32-34. [3] Zha, H. (2023) Analysis of the influencing factors of housing prices in China based on panel data. Master's Thesis, Nanjing Audit University, Master. Table 3: (continued). ===== PAGE 7 (score 3, numeric-paren 0, table-words 0, var-words 3) ===== Proceedings of the 3rd International Conference on Mathematical Physics and Computational Simulation DOI: 10.54254/2753-8818/2025.23756 194 [4] Yan, L.Y. (2023) Prediction of second - hand housing prices in Beijing and analysis of influencing factors based on optimization algorithms. Master's Thesis, Dongbei University of Finance and Economics, Master. [5] Norada Real Estate Investments. (2025) Chicago Housing Market: Trends and Forecast 2025-2026. Norada Real Estate Investments. Retrieved from https://www.noradarealestate.com/blog/chicago-real-estate-market/ [6] Federal Reserve Bank of Chicago. (2018) Determinants of Housing Values and Variations in Home Prices Across [7] Neighborhoods in Cook County. Federal Reserve Bank of Chicago. Retrieved from https://www.chicagofed.org/publications/profitwise-news-and-views/2018/determinants-of-housing-values-and- variations-in-home-prices-across-neighborhoods-in-cook-county [8] Lu, Y. Shi, V. and Pettit, C.J. (2023) The Impacts of Public Schools on Housing Prices of Residential Properties: A Case Study of Greater Sydney, Australia. ISPRS Int. J. Geo-Inf., 12, 298. [9] Kou, J.Y. (2022) Analysing Housing Price in Australia with Data Science Methods. PhD thesis, Victoria University. [10] Rahman, M.M. (2010) The Australian housing market – understanding the causes and effects of rising prices. Policy Studies, 31(5), 577-590.