# RECORD 4e466a17fb2cf5a4adc290cec91b0f1aaedbabd1 # TITLE: Research on the Influencing Factors of Rental House Prices # YEAR: 2024.0 VENUE: Transactions on Economics, Business and Management Research DOI: 10.62051/cs0pd728 # AUTHORS: Xiaoyan Mao # PAGES: 6 CANDIDATE TABLE PAGES (1-based): [3, 4] # ABSTRACT: Since the 1990s, China's real estate market has gradually recovered, and has developed into a large and mature trading market today, as the housing price increase, the demand of housing rental also increases a lot. Based on this this article aims to identify the factors that have an impact on rental price. The method of multi-linear regression is used to analyze the factors with over 10,000 samples in Guangzhou from May 2020 to June 2020 from the website of LianJia, one of the biggest real estate agencies in China. The dataset contains 10 variables in total, this research uses VIF, Pearson correlation coefficient to narrow the variables down to District, Rent Type, Area, Elevator Flag and Buld Floor these five variables and considered that they have a significant linear relationship with rental price. By focusing on these variables this study provides a view of the relationship of demand and supply of Chinaโ€™s renting market. ===== PAGE 1 (score 18, numeric-paren 0, table-words 2, var-words 14) ===== Transactions on Economics, Business and Management Research ISSN: 2960-1789, eISSN: 2960-2254 | Volume 10 MEEA 2024 Content from this work may be used under the terms of CC BY-NC 4.0 licence (https://creativecommons.org/licenses/by-nc/4.0/). Published by Warwick Evans Publishing. WEP Warwick Evans Publishing Research on the Influencing Factors of Rental House Prices Xiaoyang Mao Chemistry Department, Denison University, Granville, 43023, The US mao_x1@denison.edu Abstract. Since the 1990s, China's real estate market has gradually recovered, and has developed into a large and mature trading market today, as the housing price increase, the demand of housing rental also increases a lot. Based on this this article aims to identify the factors that have an impact on rental price. The method of multi-linear regression is used to analyze the factors with over 10,000 samples in Guangzhou from May 2020 to June 2020 from the website of LianJia, one of the biggest real estate agencies in China. The dataset contains 10 variables in total, this research uses VIF, Pearson correlation coefficient to narrow the variables down to District, Rent Type, Area, Elevator Flag and Buld Floor these five variables and considered that they have a significant linear relationship with rental price. By focusing on these variables this study provides a view of the relationship of demand and supply of Chinaโ€™s renting market. Keywords: Rental price; Linear regression; Pearson correlation coefficient. 1. Introduction Since the housing reform in the 1990s, China's housing rental market has also begun to recover under the strong market, demographic fundamentals and strong support from the government [1]. In recent years, under the influence of many factors, the rental population in the country shows an increasing trend year by year [2]. With the expansion of the rental market and the continuous growth of the number of tenants, the study of the house rental price has become increasingly important. This will not only help strengthen the supervision of the rental market, but also provide tenants with better pricing references. In recent years, with the promotion of national policies and the continuous improvement of the market, the housing rental market in Guangzhou has gradually matured, but it also faces many challenges [3]. On the one hand, the fluctuation of housing rental prices is affected by many factors, such as geographical location, housing quality, supporting facilities, etc. [4]. On the other hand, the demand for housing is increasingly diversified, and there are differences in the expectations and affordability of housing rental prices. Therefore, this paper aims to provide a reference for the government to formulate relevant policies, for developers to optimize product positioning, and for residents to rationally choose housing by systematically analyzing the influencing factors of housing rent prices in Guangzhou. In related research fields, many scholars have conducted in-depth discussions on the influencing factors of housing rental prices. From the perspective of building characteristics, factors such as house size, orientation, floor, house age, elevator, and property management will all have an impact on rental price [5]. Another key factors that significantly impact rental prices in Guangzhou is transportation convenience. The city's robust public transportation system, including its extensive subway network and bus routes, has a profound effect on the accessibility and attractiveness of different neighborhoods. Properties located near major transportation hubs tend to command higher rents due to their superior connectivity and convenience. This is particularly true for commuters and young professionals who value efficiency and time savings [6]. In addition, environmental factors also play a crucial role in determining rental prices. The accessibility of parks and green spaces, as well as the level of industrial pollution, can significantly influence the desirability and pricing of housing units. Residents are increasingly aware of the importance of living in a healthy and sustainable environment, and this is reflected in the rental market. Properties situated in areas with ===== PAGE 3 (score 35, numeric-paren 3, table-words 11, var-words 3) ===== 148 2.3. Method Introduction This paper uses Pearson correlation coefficient to test the correlation between the price and other variables. This test is mainly aim for the correlation between variables and select the variables can be used in the further multi-linear regression model. The multi-linear regression model is also used in this paper. It is a linear regression model with more than one variable. It is used in explain the linear relationship between one variable and multiple related variables to make the model closer to the reality. ๐ธ(๐‘Œ) = ๐›ฝ0 + ๐›ฝ1๐‘ฅ1 + ๐›ฝ2๐‘ฅ2 + โ‹ฏ+ ๐›ฝ๐‘๐‘ฅ๐‘+ ๐‘’ (1) In the above formula: ๐›ฝ0 is a constant term, and ๐‘’ is a residual term. 3. Results and Discussion 3.1. Multi-linear Regression This analysis shows that there are many factors can impact on rental price. As Table 2 shows: Table 2. Multi-linear Regression analysis on six variables Nonnormalized Coefficient Normalized Coefficient t p Collinearity B S.E. Beta VIF Tolerance Constant -360.505 377.670 - -0.955 0.340 - - District (x1) 84.865 10.472 0.057 8.104 0.000**1.016 0.984 RentType (x2) -70.768 184.130 -0.003 -0.384 0.701 1.025 0.975 Area (x3) 41.938 0.445 0.664 94.1420.000**1.030 0.970 BuldFloor (x4) 35.954 3.518 0.089 10.2210.000**1.559 0.642 ElevatorFlag (x5) 129.228 81.439 0.014 1.587 0.113 1.521 0.658 CommunityID (x6) 0.009 0.027 0.003 0.348 0.728 1.099 0.910 Based on the analysis above the relevant multi-linear regression equation is available: ๐‘ƒ๐‘Ÿ๐‘–๐‘๐‘’= โˆ’360505 + 84.865๐‘ฅ1 โˆ’70.768๐‘ฅ2 + 41.938๐‘ฅ3 + 129.228๐‘ฅ4 + 35.954๐‘ฅ5 + 0.009๐‘ฅ6 (2) It is showed in the regression analysis that the coefficient value of District is 84.865, and t is equal to 8.104, p is 0.000, smaller than 0.01, which indicate that District has a significant positive impact on Price. The coefficient value of Rent Type is -70.768, and t is -0.384, p is 0.701, larger than 0.05, it shows that Rent Type has no significant effect on Price. The coefficient value of Area is 41.938, with t equals 94.142 and p equals 0.000, which is samller than0.01, indicating that Area has a significant positive effect on Price. The coefficient value of Buld Floor is 35.954, t is 10.221, and p is 0.000, smaller than 0.01, which means that Buld Floor has a significant positive effect on Price. The coefficient value of Elevator Flag is 129.228 it has a t equal 1.587 and p equals 0.113, which is larger than 0.05, which indicates that Elevator Flag has no significant effect on Price. The coefficient value of Community ID is 0.009 with a t value of 0.348 and p value of 0.728, larger than 0.05, this shows that Community ID doesnโ€™t have a significant influence on Price. To sum up, District, Area and Buld Floor have a significant positive impact on Price, while Rent Type, Elevator Flag and Community ID have no significant impact on Price ===== PAGE 4 (score 40, numeric-paren 8, table-words 12, var-words 2) ===== 149 3.2. Data Relevance The regression analysis shows that only district, area and buld floor have a significant positive impact on the rent price. Pearson Correlation Coefficient is introduced to double check the correlation between rent price and the independent variables. From Table 3, it demonstrates the Pearson correlation coefficient of the independent variables with respect to price. Given the complexity of geometry and house floor in the dataset, they are not incorporated in the analysis. Furthermore, the unit price equates to price per area and is solely reliant on price and area, excluding it from the test. The table above allows for determining the correlation between price and the independent variables. The correlation between price and region is 0.070, significant at the 0.01 level, indicating a significant positive correlation between the two. The correlation between price and lease type is 0.086, also significant at the 0.01 level, suggesting a significant positive correlation. The correlation between price and elevator sign is 0.107, significant at the 0.01 level, signifying a significant positive correlation. The correlation between price and area is 0.671, significant at the 0.01 level, indicating a significant positive correlation. The correlation between price and building floor is 0.147, significant at the 0.01 level, suggesting a significant positive correlation. The correlation between price and community ID is -0.001, near 0, with a P-value of 0.877, greater than 0.05, suggesting no correlation between price and community ID. In conclusion, district, rent type, elevator flag, area, and building floor all exhibit a significant correlation with the rent price. However, the community ID doesnโ€™t show any significant correlation to the rent price, so it can be ignored in further analysis. Table 3. Pearson correlation coefficient analysis on variables Price District (x1) 0.070** RentType (x2) 0.086** Area (x3) 0.671** BuldFloor (x4) 0.147** ElevatorFlag (x5) 0.107** CommunityID (x6) -0.001 3.3. Re-build Multi-linear Regression Model According to the Pearson correlation coefficient a new multi-linear regression model was built. District, Rent Type, Area, Elevator Flag and Buld Floor are the remaining independent variables (Table 4). Table 4. Multi-linear Regression Analysis five remaining variables Nonnormalized Coefficient Normalized Coefficient t p Collinearity B S.E. Beta VIF Tolerance Constant -346.032 375.352 - -0.922 0.357 - - District (x1) 84.809 10.471 0.057 8.100 0.000** 1.016 0.984 RentType (x2) -68.823 184.037 -0.003 -0.374 0.708 1.024 0.976 Area (x3) 41.947 0.445 0.664 94.328 0.000** 1.027 0.974 ElevatorFlag (x4) 125.881 80.864 0.013 1.557 0.120 1.500 0.667 BuldFloor (x5) 35.757 3.471 0.088 10.300 0.000** 1.518 0.659 Based on the new regression model, new equation can be obtained: ๐‘ƒ๐‘Ÿ๐‘–๐‘๐‘’= โˆ’346.032 + 84.809๐‘ฅ1 โˆ’68.823๐‘ฅ2 + 41.947๐‘ฅ3 + 125.881๐‘ฅ4 + 35.757๐‘ฅ5 (3) ===== PAGE 5 (score 17, numeric-paren 0, table-words 6, var-words 4) ===== 150 In the table 4, no variable has a VIF greater than 5, which indicate that there is no covariance problems occur. Table 5. Multi-linear Regression Model Summary R R2 Adjusted R2 RMSE DW AIC BIC 0.681 0.464 0.463 3252.976 1.570 211469.338 211513.238 According to the data in the above table 5, the square value of model R is 0.464, which indicates that factors such as District, Rent Type, Area, Elevator Flag and Buld Floor can explain 46.4% of the change in Price. Moreover, during the F-test of the model, the result shows that the model passes the F-test (F=1920.929, p=0.000<0.05), which indicates that the construction of the model has practical significance. 4. Conclusion The study selected 11128 samples from LianJia from May to June 2020, which contains 10 variables. This paper uses multi-linear regression and Pearson correlation coefficient combine with logical reasoning to reduce the variables to 5 and conclude that District, Rent Type, Area, Elevator Flag and Buld Floor have a significant impact on the rent price. It cannot be denied that due to the change of the policy, supply and demand relationship and customerโ€™s expectation this model cannot perfectly fit in the real market situation. Also, the data for the district is not evenly distributed, which may also make the model has a district preference. However, this study still has a lot of value in some aspects. First of all, the method used in this study is innovative, the use of multi-linear regression is to find out the relationship between variables and the rental price. Compare to the single factor analysis method it makes the analysis more comprehensive and closer to the reality. It provides an insight into the proportionate weight of location and the building itself in rental pricing. On one hand, the analysis of this model reflects customerโ€™s demand for rent from the side, which provides the direction for the housing agent when choosing the high-quality housing. This can improve the relationship between supply and demand of rent market, making the housing agent choose more targeted housing and lower down the cost of choosing low demand housing. On the other hand, this study shows the relationship between variables and rental price, which also gives the customer a measuring standard when renting house. This can help the customer balance the housing demand and cost, lower down the waste of money on unwanted requirements. In general, this study gives the supplier and customer in the housing rent market a guidance on the demand and supply, which can improve the market efficiency and reduce the cost of unwanted demand. References [1] W. Y. Zhang, People Living in rental residential housing in China 2017-2023, Statista, (2022). [2] Y. Wang, et al. Spatial Differentiation and Influencing Factors Dataset of Housing Rents in Guangdong-Hongkong- Macao Greater Bay Area, Journal of Global Change Data & Discovery, 6 (1) (2022) 37-44. [3] Y. Wang, et al. The core influencing factors of housing rent difference in Guangzhouโ€™s urban district, Acta Geographica Sinica, 76(8) (2021). [4] L. Q. Gu, On the perfection of Chinaโ€™s Long-term Rental System, Frontiers in Business, Economics and Management, (2022). [5] T. Hu, The rise of Chinaโ€™s rental housing market, Jones Lang LaSalle, (2018). [6] D. G. Wang, S. M. Li, Socio-economic differentials and stated housing preferences in Guangzhou, China, Habitat International, (2006). [7] Y. X. Xu, S. J. He, J. X. Qian, An investigation on the emerging housing rental market in a studentified village in Guangzhou: a new institutional economics perspective. University at Albany, (2020).