# RECORD 29ce2045a8b41ca2314a2b80fea6fdc3b22ab75f # TITLE: Research on the Factors Influencing Housing Prices # YEAR: 2025.0 VENUE: Theoretical and Natural Science DOI: 10.54254/2753-8818/2025.gl24068 # AUTHORS: Suqi Chen # PAGES: 6 CANDIDATE TABLE PAGES (1-based): [5] # 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. ===== PAGE 1 (score 14, numeric-paren 0, table-words 0, var-words 14) ===== Proceedings of CONF-MPCS 2025 Symposium: Leveraging EVs and Machine Learning for Sustainable Energy Demand Management DOI: 10.54254/2753-8818/2025.GL24068 Β© 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/). 155 Research on the Factors Influencing Housing Prices Suqi Chen Superintendent Xuecheng Careers International Curriculum Center (Huang Guang Campus), Shanghai, China suki071019@gmail.com 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. Keywords: Housing prices, influence factors, multiple linear regression. 1. Introduction In recent years, housing prices have fluctuated significantly, especially in major cities, attracting widespread attention. High housing prices not only affect residents’ quality of life but also pose challenges to economic policies and urban development. Intensified population mobility has led to a continuous rise in housing prices, which remain persistently high [1]. In Beijing, housing price growth has far outpaced wage increases, resulting in widening price disparities [2]. After 2016, housing prices have risen to approximately 3.35 times those in 2000, though the difficulty of purchasing has slightly decreased [3]. However, people’s pressure to buy homes has increased, yet they cannot clearly and specifically identify the important factors influencing housing prices. Understanding the factors that affect housing prices is crucial. Therefore, this article aims to help people make rational decisions when buying a home based on potential factors that may lead to changes in housing prices. Housing prices are a critical indicator of the real estate market, influenced by a multitude of factors. Predictions of housing prices are also a popular argument among academics. Research on housing prices can be approached from multiple perspectives, such as regulatory policies, location, and analysis of specific city characteristics [4]. Chen pointed out that combining statistical analysis methods like the Panel Vector Autoregression (PVAR) model and impulse response allows for a more intuitive examination of the dynamic effects and mechanisms between variables, providing an in- depth analysis of how policy, supply, and demand influence housing prices. This method effectively maintains the robustness of the research results [5]. Zhang employed econometric methods- ===== PAGE 5 (score 20, numeric-paren 0, table-words 3, var-words 10) ===== Proceedings of CONF-MPCS 2025 Symposium: Leveraging EVs and Machine Learning for Sustainable Energy Demand Management DOI: 10.54254/2753-8818/2025.GL24068 159 3.2. Correlation analysis The analysis in this paper shows that there are many factors Influencing Housing Prices. As the graph shows: Figure 4: Relevance analysis between dependent and independent variables As shown in Figure 4, the Pearson correlation coefficients reveal the relationships between these factors and housing prices. The study found that square footage and lot size had the strongest positive correlations with price, indicating current buyers prioritize spacious layouts. While the number of bathrooms also showed a positive correlation, its impact was less significant. Besides, factors like the number of bedrooms, neighborhood quality, garage size, and building age exhibited negative correlations. This demonstrates the multidimensional nature of housing preferences, suggesting modern buyers make rational trade-offs across various attributes. 3.3. Model results Following this correlation analysis, a multiple linear regression was conducted. The general mathematical model is expressed as: 𝐸(π‘Œ) = 𝛽1𝑋1 + 𝛽2𝑋2 + β‹―+ 𝛽𝑝𝑋𝑝+ πœ– (1) In the above formula: 𝛽𝑝 is the parameter coefficients, and πœ– is a residual term. Table 2: Regression coefficient table B S.E. Beta T significance VIF Constant 735.360 1622.667 - 0.453 0.651 - X1 199.756 0.249 0.989 803.789 0.000 1.013 X2 10170.955 219.157 0.057 46.409 0.000 1.018 X3 8244.535 378.698 0.027 21.771 0.000 1.004 X4 80.617 107.618 0.001 0.749 0.454 1.004 X5 -991.468 15.065 -0.081 -65.812 0.000 1.005 X6 5158.064 383.350 0.017 13.455 0.000 1.016 X7 14919.323 240.583 0.076 62.013 0.000 1.014 ===== PAGE 6 (score 19, numeric-paren 0, table-words 2, var-words 15) ===== Proceedings of CONF-MPCS 2025 Symposium: Leveraging EVs and Machine Learning for Sustainable Energy Demand Management DOI: 10.54254/2753-8818/2025.GL24068 160 Table 2 presents the regression coefficients of the multiple linear regression model. All six independent variables, X1, X2, X3, X5, X6 and X7 show p-values of the T-test below 0.003, indicating statistically significant effects on the dependent variable (house price). Based on these results, the predictive regression equation can be formulated as: π‘Œ= 735.36 + 199.756𝑋1 + β‹―+ 14919.323𝑋7 (2) 4. Conclusion This study selected 1000 data samples for analysis, covering seven variables. The method is clear and comprehensive, using Pearson correlation coefficients to measure each variable’s relationship with housing prices. During the analysis phase, a multiple linear regression model was applied to identify potential connections between these variables and housing prices. To better understand the impact of individual factors, the study analyzed their effects after deriving the linear regression equation. Various graphs were used to present the results in a clear and accessible way. The findings show that housing prices are influenced by floor area, number of bedrooms, number of bathrooms, house age, garage size, and lot size-with floor area having the strongest effect. This research helps buyers make more informed budget decisions when purchasing property. However, there are some limitations: the dataset used was simulated, whereas real-world conditions involve more uncertainties, which may introduce bias. To improve accuracy, future studies could incorporate real-world surveys for more detailed and precise conclusions. References [1] Fan, J.N. (2021) Research on the influencing factors of housing prices in Population outflow cities. Dissertation defense paper of the School of Management and Economics of Tianjin University. [2] Xu, Y. (2021) Analysis of Factors Influencing Commercial Housing Prices in Beijing. Economic Management Digest, 14. [3] Gao, Q.D. (2021) Analysis of the characteristics of China's real estate market and factors affecting house prices. An industry-focused journal for Habitat, 25. [4] Tu, J., Jiang, W.C. and Leng, Z.X. (2021) Differentiated Study of Factors Affecting Urban House Price in China (Analysis of Big Data in the Chengdu Primary Housing Market). Journal of Price and Theory Practice, 10. [5] Chen, M.D. (2022) Study on influencing factors and regional differences of housing prices in China. Degree thesis at Liaoning University, 4. [6] Zhang, Y. (2022) Analysis of the causes of the impact of housing prices in the largest cities in China. University of Foreign Economics and Trade Master's Degree Essay, 5. [7] Zhu, Y.R., Qian, Y.H. and Guo, H.P. (2022) Statistical inference of the determinants of house price. Department of Statistics and Management, School of Mathematics and Statistics, Hunan Normal University, 5. [8] Yu, Y.R. (2019) A study of basic factors influencing housing rental prices based on multiple linear regression (using Shanghai housing rentals market as an example). Shanghai University of Finance and Economics, 29. [9] Lv, C.Y., Liu, Y.X. and Wang, L.D. (2022) Analysis and Forecast of Influencing Factors on House Prices Based on Machine Learning. Proceedings of 3rd International Symposium on Information Science and Engineering Technology, 117-121. [10] Zheng, Y.F. (2007) Research on the spatial difference of housing prices in different urban areas of Hangzhou. Economic Forum, 20, 32-34.