1# RECORD 8506f16b0710a57ed835f9583338b207fd2042582# TITLE: Analysis of Tianjin real estate market: multiple linear regression prediction method3# YEAR: 2024.0 VENUE: Transactions on Economics, Business and Management Research DOI: 10.62051/a4tma7824# AUTHORS: J. Yi5# PAGES: 5 CANDIDATE TABLE PAGES (1-based): [4]6# ABSTRACT: In this paper, the price prediction of Tianjin real estate market is studied, and the key factors affecting housing prices are systematically analyzed by using the method of multiple linear regression prediction. The research data comes from the real estate transaction data of Tianjin from 2018 to 2022, covering independent variable information such as housing area, housing age, geographical location and supporting facilities. By constructing multiple linear regression model, it is found that housing area and supporting facilities have a significant positive impact on housing prices, while housing age and distance from the city center have a significant negative impact on housing prices. Specifically, for every 1 square meter increase in housing area, house prices are expected to rise by 450 yuan; Every year the age of the house increases, the house price is expected to drop by 200 yuan; Every kilometer away from the city center, house prices are expected to drop in 800 yuan; Every time the score of supporting facilities is increased by 1 point, the house price is expected to increase by 1200 yuan. The goodness-of-fit test of the model shows that the R value is 0.85, which shows that the model has strong explanatory power. In addition, through the correlation analysis of independent variables, it is found that there is a positive correlation between housing area and supporting facilities, but the correlation with other variables is weak. This study not only enriches the theoretical research of the real estate market, but also provides valuable decision-making reference for the government, enterprises and consumers, and helps the healthy and stable development of the real estate market in Tianjin.789===== PAGE 1 (score 3, numeric-paren 0, table-words 0, var-words 3) =====1011 12Transactions on Economics, Business and Management Research 13ISSN: 2960-1789, eISSN: 2960-2254 | Volume 12 14EDI 2024 15 16Content from this work may be used under the terms of CC BY-NC 4.0 licence (https://creativecommons.org/licenses/by-nc/4.0/). 17Published by Warwick Evans Publishing. 18WEP19Warwick20Evans21Publishing22Analysis of Tianjin real estate market: multiple linear regression 23prediction method 24Juntai Yi 25Changjun High School International Department, Changsha, 410002, China 26Abstract. In this paper, the price prediction of Tianjin real estate market is studied, and the key 27factors affecting housing prices are systematically analyzed by using the method of multiple linear 28regression prediction. The research data comes from the real estate transaction data of Tianjin from 292018 to 2022, covering independent variable information such as housing area, housing age, 30geographical location and supporting facilities. By constructing multiple linear regression model, it is 31found that housing area and supporting facilities have a significant positive impact on housing prices, 32while housing age and distance from the city center have a significant negative impact on housing 33prices. Specifically, for every 1 square meter increase in housing area, house prices are expected 34to rise by 450 yuan; Every year the age of the house increases, the house price is expected to drop 35by 200 yuan; Every kilometer away from the city center, house prices are expected to drop in 800 36yuan; Every time the score of supporting facilities is increased by 1 point, the house price is expected 37to increase by 1200 yuan. The goodness-of-fit test of the model shows that the R value is 0.85, which 38shows that the model has strong explanatory power. In addition, through the correlation analysis of 39independent variables, it is found that there is a positive correlation between housing area and 40supporting facilities, but the correlation with other variables is weak. This study not only enriches the 41theoretical research of the real estate market, but also provides valuable decision-making reference 42for the government, enterprises and consumers, and helps the healthy and stable development of 43the real estate market in Tianjin. 44Key words: Tianjin; real estate market; multiple linear regression. 451. Introduction 46As an important municipality directly under the central government and economic center of China, 47the development of Tianjin's real estate market has an important impact on the regional and even the 48national economy. In recent years, with the acceleration of urbanization and the improvement of 49residents' living standards, the real estate market in Tianjin has shown a vigorous development trend, 50attracting a large number of investors and residents' attention. However, the volatility and uncertainty 51of the real estate market also bring a lot of risks to relevant stakeholders [1]. Therefore, in-depth 52analysis and accurate prediction of Tianjin real estate market will not only help the government and 53enterprises to make scientific and reasonable decisions, but also provide consumers with more 54transparent market information. 55The purpose of this study is to make a systematic analysis of Tianjin real estate market by using the 56method of multiple linear regression prediction. As a statistical method, multiple linear regression 57can explore the relationship between multiple independent variables and dependent variables, and 58help us understand the influencing factors of house prices more comprehensively. This study not only 59has theoretical significance, but also has practical application value. Through scientific forecasting 60methods, we can better grasp the market dynamics and contribute to the healthy and stable 61development of Tianjin real estate market. 626364===== PAGE 4 (score 23, numeric-paren 0, table-words 8, var-words 4) =====6566 6736 68Table 1 Prediction results of multivariate linear regression model 69variable 70B 71SE 72T value 73P value 74Intercept 7550000 7615000 773.33 78<0.01 79Housing area (X1) (per square meter) 80450 8135 8212.86 83<0.001 84Room age (X2) (per year) 85-200 8660 87-3.33 88<0.01 89Geographical location (X3) (per kilometer) 90-800 91120 92-6.67 93<0.001 94Supporting facilities (X4) (each score) 951200 96150 978.00 98<0.001 994.2. Independent variable correlation analysis 100The correlation between independent variables is also considered in the process of model construction. 101By calculating the correlation coefficient matrix, it is found that there is a certain degree of positive 102correlation between housing area and supporting facilities, which may be because larger houses are 103often equipped with more perfect facilities. The correlation between other independent variables is 104weak, which will not cause serious multicollinearity problems to the model. See Table 2. 105Table 2 Correlation coefficient matrix 106variable 107Housing area 108(X1) 109Room age 110(X2) 111location 112(X3) 113Supporting facilities 114(X4) 115Housing area (X1) 1161.00 117-0.10 118-0.20 1190.40 120Room age (X2) 121-0.10 1221.00 1230.05 124-0.05 125Geographical location 126(X3) 127-0.20 1280.05 1291.00 130-0.15 131Supporting facilities 132(X4) 1330.40 134-0.05 135-0.15 1361.00 137 138There is a certain degree of positive correlation between the housing area (X1) and the supporting 139facilities (X4), and the correlation coefficient is 0.40. This means that larger houses are often 140equipped with better facilities. This positive correlation is reasonable in the real estate market, 141because large houses usually have more space and resources to provide comprehensive supporting 142facilities, thus improving the convenience and comfort of living. 143There is a certain negative correlation between the housing area (X1) and the geographical location 144(X3), and the correlation coefficient is -0.20. This may indicate that the housing area in Tianjin, which 145is closer to the city center, may be relatively small due to limited land resources. On the contrary, far 146away from the city center, land resources are more abundant, so the housing area may be larger. 147However, this negative correlation is relatively weak, indicating that the relationship between area 148and geographical location is not absolute. 149Room age (X2), its correlation with other variables is very weak. Especially, the correlation 150coefficients with the building area (X1) and supporting facilities (X4) are only -0.10 and -0.05, which 151are almost negligible. This means that the house age may be a relatively independent variable, which 152is less affected by other factors. In fact, the age of the house mainly reflects the old and new degree 153of the house, which is not directly related to the size of the house and the perfection of supporting 154facilities. 155Finally, there is a certain degree of negative correlation between geographical location (X3) and 156supporting facilities (X4), and the correlation coefficient is -0.15. This may indicate that in Tianjin, 157in areas far from the city center, supporting facilities may be relatively few or not perfect. However, 158159160===== PAGE 5 (score 5, numeric-paren 1, table-words 0, var-words 4) =====161162 16337 164this negative correlation is relatively weak, indicating that the relationship between geographical 165location and supporting facilities is not static. 166The multiple linear regression model in this study is suitable for forecasting the housing price in 167Tianjin under similar market conditions. However, the model also has some limitations. First of all, 168the model assumes that there is a linear relationship between independent variables and dependent 169variables, which may not be fully established in practical application. Secondly, the model does not 170consider all the factors that may affect housing prices, such as market supply and demand, policy 171changes and so on. Therefore, it is necessary to combine other information to make a comprehensive 172judgment in practical application. 173To sum up, this study successfully predicted the housing price in Tianjin through multiple linear 174regression model, and analyzed the influence degree of their respective variables on housing prices 175and their correlation. Although the model has certain forecasting ability and application scope, it still 176needs to pay attention to its limitations and consider them in practical application. 1775. Conclusion 178In this study, the real estate market in Tianjin is systematically analyzed by using the method of 179multiple linear regression prediction. The results show that housing area, housing age, geographical 180location and supporting facilities are the key factors affecting housing prices in Tianjin. Among them, 181housing area and supporting facilities have a significant positive impact on housing prices, while 182housing age and distance from the city center have a significant negative impact on housing prices. 183In addition, the correlation analysis between independent variables shows that there is a certain degree 184of positive correlation between housing area and supporting facilities, while there is a certain degree 185of negative correlation between housing area and geographical location. These findings provide 186strong support for the healthy and stable development of Tianjin real estate market. Although the 187multiple linear regression model has certain forecasting ability and applicable scope, it still needs to 188pay attention to its limitations and consider them in practical application. First of all, the model 189assumes that there is a linear relationship between independent variables and dependent variables, 190which may not be fully established in practical application. Therefore, when using this model to 191predict, it is necessary to combine other information to make a comprehensive judgment. Secondly, 192the model does not consider all the factors that may affect housing prices, such as market supply and 193demand, policy changes and so on. Therefore, in practical application, it is necessary to further collect 194and sort out relevant data in order to predict house prices more accurately. Finally, in order to improve 195the accuracy of forecasting, other more advanced forecasting methods, such as machine learning and 196artificial intelligence, are considered, so as to provide more comprehensive and accurate decision-197making basis for the development of Tianjin real estate market. 198References 199[1] Su Zhi. (2016). 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