1# RECORD 29ce2045a8b41ca2314a2b80fea6fdc3b22ab75f2# TITLE: Research on the Factors Influencing Housing Prices3# YEAR: 2025.0 VENUE: Theoretical and Natural Science DOI: 10.54254/2753-8818/2025.gl240684# AUTHORS: Suqi Chen5# PAGES: 6 CANDIDATE TABLE PAGES (1-based): [5]6# ABSTRACT: This paper aims to investigate and demonstrate the factors affecting housing prices. The method used to analyze 1,000 samples for determining these factors is multiple linear regression. After preliminary analysis, some less relevant factors were excluded, such as built year, and finally, seven key influencing factors were selected for the study. The research employs various intuitive charts and clear tables to present the results. A linear relationship between housing prices and the following factors was established: square footage, number of bedrooms, neighborhood quality, number of bathrooms, parking space size, house age, and lot size. This allows for a clear understanding of how much each factor contributes to price changes, as well as the individual impact of each variable. Behind the influence of each of these factors, this paper further analyzes other possible practical factors. In conclusion, this study provides useful reference value for real estate investment and home-buying budget planning.789===== PAGE 1 (score 14, numeric-paren 0, table-words 0, var-words 14) =====1011Proceedings of CONF-MPCS 2025 Symposium: Leveraging EVs and Machine Learning for Sustainable Energy Demand Management 12DOI: 10.54254/2753-8818/2025.GL2406813© 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/).1515516 17 18Research on the Factors Influencing Housing Prices 19Suqi Chen 20Superintendent Xuecheng Careers International Curriculum Center (Huang Guang Campus), 21Shanghai, China 22suki071019@gmail.com 23Abstract: This paper aims to investigate and demonstrate the factors affecting housing prices. 24The method used to analyze 1,000 samples for determining these factors is multiple linear 25regression. After preliminary analysis, some less relevant factors were excluded, such as built 26year, and finally, seven key influencing factors were selected for the study. The research 27employs various intuitive charts and clear tables to present the results. A linear relationship 28between housing prices and the following factors was established: square footage, number of 29bedrooms, neighborhood quality, number of bathrooms, parking space size, house age, and 30lot size. This allows for a clear understanding of how much each factor contributes to price 31changes, as well as the individual impact of each variable. Behind the influence of each of 32these factors, this paper further analyzes other possible practical factors. In conclusion, this 33study provides useful reference value for real estate investment and home-buying budget 34planning. 35Keywords: Housing prices, influence factors, multiple linear regression. 361. 37Introduction 38In recent years, housing prices have fluctuated significantly, especially in major cities, attracting 39widespread attention. High housing prices not only affect residents’ quality of life but also pose 40challenges to economic policies and urban development. Intensified population mobility has led to a 41continuous rise in housing prices, which remain persistently high [1]. In Beijing, housing price growth 42has far outpaced wage increases, resulting in widening price disparities [2]. After 2016, housing prices 43have risen to approximately 3.35 times those in 2000, though the difficulty of purchasing has slightly 44decreased [3]. However, people’s pressure to buy homes has increased, yet they cannot clearly and 45specifically identify the important factors influencing housing prices. Understanding the factors that 46affect housing prices is crucial. Therefore, this article aims to help people make rational decisions 47when buying a home based on potential factors that may lead to changes in housing prices. 48Housing prices are a critical indicator of the real estate market, influenced by a multitude of factors. 49Predictions of housing prices are also a popular argument among academics. Research on housing 50prices can be approached from multiple perspectives, such as regulatory policies, location, and 51analysis of specific city characteristics [4]. Chen pointed out that combining statistical analysis 52methods like the Panel Vector Autoregression (PVAR) model and impulse response allows for a more 53intuitive examination of the dynamic effects and mechanisms between variables, providing an in-54depth analysis of how policy, supply, and demand influence housing prices. This method effectively 55maintains the robustness of the research results [5]. Zhang employed econometric methods-565758===== PAGE 5 (score 20, numeric-paren 0, table-words 3, var-words 10) =====5960Proceedings of CONF-MPCS 2025 Symposium: Leveraging EVs and Machine Learning for Sustainable Energy Demand Management 61DOI: 10.54254/2753-8818/2025.GL240686215963 64 653.2. Correlation analysis 66The analysis in this paper shows that there are many factors Influencing Housing Prices. As the graph 67shows: 68 69Figure 4: Relevance analysis between dependent and independent variables 70As shown in Figure 4, the Pearson correlation coefficients reveal the relationships between these 71factors and housing prices. The study found that square footage and lot size had the strongest positive 72correlations with price, indicating current buyers prioritize spacious layouts. While the number of 73bathrooms also showed a positive correlation, its impact was less significant. Besides, factors like the 74number of bedrooms, neighborhood quality, garage size, and building age exhibited negative 75correlations. This demonstrates the multidimensional nature of housing preferences, suggesting 76modern buyers make rational trade-offs across various attributes. 773.3. Model results 78Following this correlation analysis, a multiple linear regression was conducted. The general 79mathematical model is expressed as: 80 81𝐸(𝑌) = 𝛽1𝑋1 + 𝛽2𝑋2 + ⋯+ 𝛽𝑝𝑋𝑝+ 𝜖 82(1) 83In the above formula: 𝛽𝑝 is the parameter coefficients, and 𝜖 is a residual term. 84Table 2: Regression coefficient table 85 86B 87S.E. 88Beta 89T 90significance 91VIF 92Constant 93735.360 941622.667 95- 960.453 970.651 98- 99X1 100199.756 1010.249 1020.989 103803.789 1040.000 1051.013 106X2 10710170.955 108219.157 1090.057 11046.409 1110.000 1121.018 113X3 1148244.535 115378.698 1160.027 11721.771 1180.000 1191.004 120X4 12180.617 122107.618 1230.001 1240.749 1250.454 1261.004 127X5 128-991.468 12915.065 130-0.081 131-65.812 1320.000 1331.005 134X6 1355158.064 136383.350 1370.017 13813.455 1390.000 1401.016 141X7 14214919.323 143240.583 1440.076 14562.013 1460.000 1471.014 148 149150151===== PAGE 6 (score 19, numeric-paren 0, table-words 2, var-words 15) =====152153Proceedings of CONF-MPCS 2025 Symposium: Leveraging EVs and Machine Learning for Sustainable Energy Demand Management 154DOI: 10.54254/2753-8818/2025.GL24068155160156 157 158Table 2 presents the regression coefficients of the multiple linear regression model. All six 159independent variables, X1, X2, X3, X5, X6 and X7 show p-values of the T-test below 0.003, 160indicating statistically significant effects on the dependent variable (house price). Based on these 161results, the predictive regression equation can be formulated as: 162 163𝑌= 735.36 + 199.756𝑋1 + ⋯+ 14919.323𝑋7 164(2) 1654. 166Conclusion 167This study selected 1000 data samples for analysis, covering seven variables. The method is clear and 168comprehensive, using Pearson correlation coefficients to measure each variable’s relationship with 169housing prices. During the analysis phase, a multiple linear regression model was applied to identify 170potential connections between these variables and housing prices. 171To better understand the impact of individual factors, the study analyzed their effects after deriving 172the linear regression equation. Various graphs were used to present the results in a clear and accessible 173way. The findings show that housing prices are influenced by floor area, number of bedrooms, number 174of bathrooms, house age, garage size, and lot size-with floor area having the strongest effect. 175This research helps buyers make more informed budget decisions when purchasing property. 176However, there are some limitations: the dataset used was simulated, whereas real-world conditions 177involve more uncertainties, which may introduce bias. To improve accuracy, future studies could 178incorporate real-world surveys for more detailed and precise conclusions. 179References 180[1] Fan, J.N. (2021) Research on the influencing factors of housing prices in Population outflow cities. Dissertation 181defense paper of the School of Management and Economics of Tianjin University. 182[2] Xu, Y. (2021) Analysis of Factors Influencing Commercial Housing Prices in Beijing. Economic Management Digest, 18314. 184[3] Gao, Q.D. 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