1# RECORD 98438191d74bdd40ba8f1ee8325b8fb8b74203482# TITLE: Influence of Objective Factors on House Prices3# YEAR: 2023.0 VENUE: Advances in Economics, Management and Political Sciences DOI: 10.54254/2754-1169/4/202210594# AUTHORS: L. Lyu; Xin-Yu Song5# PAGES: 6 CANDIDATE TABLE PAGES (1-based): [2, 3, 5]6# ABSTRACT: With the development of economy and the improvement of people's living standards, many people will try to buy houses. But when it comes to buying a house, people will also pay attention to some objective factors such as the size of the house and supporting facilities. This paper uses the statistical analysis software SPSS linear regression analysis, factor analysis, comparative analysis and other methods to analyze the impact of objective factors on house prices, through data analysis, the fitting degree also reached about 0.88. In the experimental results, it is found that taxi distance, market distance, hospital distance, carpet area, built up area, parking type, city type, rainfall have a significant impact on house prices. Because in real life, These objective factors will be considered when people choose to buy a house.789===== PAGE 1 (score 9, numeric-paren 0, table-words 0, var-words 9) =====1011Influence of Objective Factors on House Prices 12Lyuyangguang Lyu1,a,*, Xinyu Song2,b 131International College, Wenzhou Business School, Wenzhou, China 142College of Accounting, Shandong Technology and Business University, Yantai, China 15a. 1477608853@qq.com, b. sxydyx5757@163.com 16*corresponding author 17Abstract: With the development of economy and the improvement of people's living 18standards, many people will try to buy houses. But when it comes to buying a house, people 19will also pay attention to some objective factors such as the size of the house and supporting 20facilities. This paper uses the statistical analysis software SPSS linear regression analysis, 21factor analysis, comparative analysis and other methods to analyze the impact of objective 22factors on house prices, through data analysis, the fitting degree also reached about 0.88. In 23the experimental results, it is found that taxi distance, market distance, hospital distance, 24carpet area, built up area, parking type, city type, rainfall have a significant impact on house 25prices. Because in real life, These objective factors will be considered when people choose to 26buy a house. 27Keywords: data processing, house prices, infrastructure. 281. 29Introduction 30House prices have been widely concerned by people, but this problem has not been well solved. 31House price prediction has also become a hot topic of concern for governments, markets and people 32all over the world. In a complete market environment, housing prices are determined by both demand 33and supply. However, in the current society, housing has both residential and investment effects, 34which makes the research on housing prices very complex. Predicting long-term housing prices has 35become an impossible task [1]. Before the housing crisis in 2007-2008, most people believed that 36house prices had been rising for many years, and those who invested in real estate would certainly be 37rewarded. But this is not the case, because banks are basically approving loans to people who are 38unable to pay for their houses, and even financial institutions approve loans to ordinary individuals 39at variable interest rates (that is, the interest rates will change according to the current market interest 40rates). When the crisis occurs, many ordinary individuals are unable to repay their mortgages. Of 41course, there are other reasons leading to the financial crisis, such as the introduction of complex 42financial instruments (derivatives are still not widely understood), hedging financial instruments 43(credit default swaps) [2] and the deregulation of the entire financial industry. Although we can 44discuss the factors that led to the financial crisis, the main purpose of this article is to determine which 45possible characteristics will have a real impact on the overall value of housing. 46In academic circles, many scholars have also analyzed and predicted the impact of objective factors 47on house prices. Supporting facilities are an important factor for house prices. From the perspective 48of urban resource allocation [3,4], such as medical care, education and market, constitute the basic 49The 6th International Conference on Economic Management and Green Development (ICEMGD 2022) 50DOI: 10.54254/2754-1169/4/2022105951© 2023 The Authors. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.052(https://creativecommons.org/licenses/by/4.0/).53220545556===== PAGE 2 (score 23, numeric-paren 6, table-words 3, var-words 8) =====5758elements of housing price differentiation [5]. Feng et al. believe that the premium effect of public 59service facilities on the housing market is the key determinant of housing prices, which leads to 60competition among different social groups in the housing market and intensifies spatial inequality [6]. 61Chen et al. mainly studied the impact of the geographical distance between Shanghai and the city 62center (whether the supporting facilities are complete) on the housing sales price, and discussed how 63the price gradient in different directions in Shanghai changes [7]. Lou et al. believe that house prices 64are not only affected by the quality of houses, but also closely related to geographical location. After 65the research and analysis, it is found that schools, subway stations and green spaces will have a 66positive impact on the house price in the study area, while business centers and hospitals will have a 67negative impact [8]. Moreover, Heyman et al. believed that location is the main determinant for 68people to estimate the value of houses [9]. 69The research content is arranged as follows: Chapter 2 is the comparative analysis of the original 70data, normality test, independent sample t-test, factor analysis. Chapter 3 is the model establishment 71of linear regression. Chapter 4 summarizes the full text and draws a conclusion. 722. 73Exploratory data analysis 74Variable meaning: Taxi_dist (Taxi distance): Market_dist(Market distance)；Hospital_dist(Hospital 75distance)；Carpet_area(Indoor area/Carpet area); Builtup_area(Construction area), Parking_type 76(Parking permit), City_type(City type) 772.1. Normality test 78All variables were tested for normality, and the results are shown in the following table 1: 79Table 1: This caption has one line so it is centered. 80variable 81sample size 82average value 83Kolmogorov-Smirnov test 84 85Statistics D value 86p 87 88Taxi_dist 89919 908229.728 910.012 920.991 93 94Market_dist 95919 9611018.753 970.037 980.004** 99 100Hospital_dist 101931 10213072.092 1030.022 1040.318 105 106Carpet_area 107924 1081511.863 1090.262 1100.000** 111 112Builtup_area 113917 1141794.925 1150.108 1160.000** 117 118Parking_type 119932 1202.840 1210.241 1220.000** 123 124City_type 125932 1261.902 1270.231 1280.000** 129 130Rainfall 131932 132785.579 1330.021 1340.389 135 136Price_house 137932 1386084695.279 1390.248 1400.000** 141 142The normality test is carried out for Taxi_dist, Market_dist, Hospital_dist, Carpet_area, Builtup_area, 143Parking_type, City_type, Rainfall, Price_house. It can be seen from the above table that the sample 144size of the research data is all greater than 50, so the K-S test is used. Specifically, a total of 6 items 145of Market_dist, Carpet_area, Builtup_area, Parking_type, City_type, Price_house showed significant 146(p<0.05), meaning that Market_dist, Carpet_area, Builtup_area, Parking_type, City_type, 147Price_house did not have normality characteristics. In addition, the three items of Taxi_dist, 148Hospital_dist, Rainfall did not show significant (p>0.05), which means that Taxi_dist, Hospital_dist, 149Rainfall have normal characteristics. 150In conclusion, Market_dist, Carpet_area, Builtup_area, Parking_type, City_type, Price_house do 151not have normality characteristics. Also, Taxi_dist, Hospital_dist, Rainfall have normality traits. 152The 6th International Conference on Economic Management and Green Development (ICEMGD 2022) 153DOI: 10.54254/2754-1169/4/20221059154221155156157===== PAGE 3 (score 20, numeric-paren 4, table-words 4, var-words 5) =====1581592.2. Non-parametric Test 160From the normality test, it can be seen that Market_dist, Carpet_area, Builtup_area, Parking_type, 161City_type showed significant (p<0.05), so these variables were tested nonparametrically. The values 162in Price_house that are less than the mean are marked as 0, and the values greater than the mean are 163marked as 1. 164Table 2: Non-parametric test. 165 166Price_house median M(P25，P75) 167p 1680.0(n=508) 1691.0(n=424) 170Market_dist 10763.000(8950.0,12407.0)11544.500(9684.8,13156.8)0.000**171Carpet_area 1463.000(1305.0,1626.0) 1496.000(1346.0,1682.0) 0.007**172Builtup_area 1758.000(1570.0,1948.0) 1794.000(1613.5,2015.5) 0.013* 173Parking_type 1743.000(2.0,4.0) 1753.000(2.0,4.0) 1760.593 177City_type 1782.000(2.0,3.0) 1791.000(1.0,2.0) 1800.000**181As can be seen from the above table 2, the non-parametric test is used to study the difference of 182Price_house to Market_dist, Carpet_area, Builtup_area, Parking_type, City_type a total of 5 items , 183the summary analysis shows that: using the MannWhitney test statistic for analysis, the analysis 184shows that: different Price_house samples will not show significant difference for a total of 1 185Parking_type (p>0.05), and the Price_house sample has a total of 4 for Market_dist, Carpet_area, 186Builtup_area, City_type Items showed significant differences (p<0.05). 1872.3. Independent Sample T-Test 188From the normality test, it can be seen that Taxi_dist, Hospital_dist, Rainfall a total of 3 items did 189not show significant (p>0.05), so the independent sample T-test was performed. The values in 190Price_house that are less than the mean are marked as 0, and the values greater than the mean are 191marked as 1. 192Table 3: Independent sample t-test. 193 194F 195salience 196t 197degrees of 198freedom 199Significance 200(two-tailed) 201Taxi_dist 202Assume equal variances 11.634 .001 -3.454 203917 204.001 205do not assume equal 206variances 207 208 209-3.401 816.263 210.001 211Hospital_dist 212Assume equal variances 8.810 213.003 -3.944 214929 215.000 216do not assume equal 217variances 218 219 220-3.894 842.155 221.000 222Rainfall 223Assume equal variances .061 224.805 225.187 226930 227.852 228do not assume equal 229variances 230 231 232.187 233901.654 234.852 235As can be seen from the above table 3, the independent sample ta test was used to study the differences 236of Price_house for Taxi_dist, Hospital_dist and Rainfall. The summary analysis showed that different 237Price_house samples did not show significant difference for Rainfall (p>0.05). , and the Price_house 238sample showed a significant difference (p<0.05) for a total of 2 items of Taxi_dist and Hospital_dist. 239The 6th International Conference on Economic Management and Green Development (ICEMGD 2022) 240DOI: 10.54254/2754-1169/4/20221059241222242243244===== PAGE 4 (score 30, numeric-paren 1, table-words 11, var-words 6) =====2452462.4. D. Factor Analysis 247Through the result graph of factor analysis, it can be seen that the significance is 0.000. It is 248considered that there is a significant difference between the correlation coefficient matrix and the 249identity matrix.The extraction of variables in the common factor variance table is basically above 0.7 250and most of them are above 0.85, indicating that the common factor is well expressed. Five factors 251extracted from the total variance interpretation table jointly explained 89.383 of the total variance, so 252the original data lost less information, and the result of factor analysis was ideal. 253Table 4: Rotated component matrix. 254 2551 2562 2573 2584 2595 260Carpet_area 261.991 262 263 264 265 266Builtup_area 267.941 268 269 270 271 272Price_house 273.911 274 275 276 277 278Hospital_dist 279 280.936 281 282 283 284Taxi_dist 285 286.872 287 288 289 290Market_dist 291 292.780 293 294 295 296City_type 297 298 299.993 300 301 302Rainfall 303 304 305 306.997 307 308Parking_type 309 310 311 312 313.997 314As can be seen from the above table 4, the rotated component matrix can be seen that Carpet_ area, 315Builtup_ area, Price_house are located on the first factor and interpreted as housing information; 316Hospital_dist, Taxi_dist, Market_dist are located on the second factor and interpreted as the distance 317of infrastructure; City_type is located on the third factor On the factor and is the interpretation of the 318city type; Rainfall is on the fourth factor and is the interpretation of the rainfall; Parking_type is on 319the fifth factor and is interpreted as the parking space information. It can be seen that the meaning of 320the factors is very clear. 3213. 322Model Building 323Assume that Taxi_dist, Market_dist, Hospital_dist, Carpet_area, Builtup_area, Parking_type, 324City_type, Rainfall variables can predict house prices. 325Table 5: Model summary table. 326 327R 328R-square 329Adjusted 330R-square 331Error in 332standard 333estimation 334Debin-335Watson 3361 337.905a 338.818 339.818 3402176566.578 341 3422 343.942b 344.887 345.887 3461720028.281 347 3483 349.943c 350.889 351.888 3521706073.726 3531.938 354As can be seen from the above table 5, the model summary table shows the fitting degree of the 355model.From the results of the model summary table in the linear regression analysis, it can be seen 356that there is only a single variable Carpet_ area, the R-square (0.818) is greater than 0.5 and the data 357fitting is good; When adding a variable Builtup_ area, the explanatory enhancement of R-square 358becomes 0.887 and the fitting degree also increases; When adding another variable Hospital_ dist, 359the interpretability and fitting degree of R-square are improved to 0.889. From the table, we can get 360The 6th International Conference on Economic Management and Green Development (ICEMGD 2022) 361DOI: 10.54254/2754-1169/4/20221059362223363364365===== PAGE 5 (score 20, numeric-paren 0, table-words 5, var-words 8) =====366367the conclusion that the fitting degree of the whole model is the best when there are three variables 368Carpet_ area, Builtup_ area and Hospital_ dist in the model at the same time. 369Table 6: ANOVA. 370Model 371 372Sum of squares Freedom 373Mean 374F 375Sig. 3761 377Regression 3781.914E+16 3791 3801.914E+16 4040.085 381.000b 382Residual 3834.245E+15 384896 3854.737E+12 386 387 388Total 3892.338E+16 390897 391 392 393 3942 395Regression 3962.074E+16 3972 3981.037E+16 3504.576 399.000c 400Residual 4012.648E+15 402895 4032.958E+12 404 405 406Total 4072.338E+16 408897 409 410 411 4123 413Regression 4142.078E+16 4153 4166.927E+15 2379.994 417.000d 418Residual 4192.602E+15 420894 4212.911E+12 422 423 424Total 4252.338E+16 426897 427 428 429 430As can be seen from the above table 6, the ANOVA shows whether the data model is significant. The 431three groups of models in the ANOVA analysis table passed the F-test and the significance was 0.000 432less than 0.05, then the model construction is meaningful. It can be concluded that house prices are 433significantly affected by three variables Carpet_ area, Builtup_ area and Hospital_ dist. 434In the beta values of the regression coefficient table, the values of Carpet_ area and Hospital_ dist 435are both positive, and only the value of Builtup_ area is negative, indicating that the impact of Carpet_ 436area and Hospital_ dist on house prices is positively correlated, while Builtup_ area is negatively 437correlated, it also means that Taxi_dist,Market_dist, Parking_type,City_type，Rainfall variables will 438not have an impact on house prices. Then, the significance of T-test is 0.000, indicating that the result 439is very significant. Finally, the VIF values of the three groups of models are between 0-10 and the 440tolerance is not less than 0.1, which shows that there is no multilinearity. Then the experimental 441results reject the original hypothesis. 442 443𝑌= 5764.491𝑋𝐶−7804.659𝑋𝐵+ 87.981𝑋𝐻−3617504.798444(1) 445 4464. 447Conclusion 448This paper studies the impact of objective factors on house prices and establishes a model through 449data, with a fitting degree of about 88%. This also really shows that housing prices are affected by 450some social and natural factors such as surrounding infrastructure, housing information, etc. this 451complex influence relationship can be used as a reference for housing builders and buyers and sellers. 452The study also found that the level of housing prices will be restricted by the type of city. For example, 453in cities with high-speed economic development, house prices will rise on the basis of a certain 454minimum price, and generally will not be too low; However, in economically underdeveloped cities, 455housing prices near the city center may be slightly higher, and relatively speaking, housing prices in 456other regions will not be particularly high. 457References 458[1] Zhou Y. Influencing factors of real estate prices in China, Hangzhou: Zhejiang University, 2017 459[2] Adam Hayes, Credit Default Swap (CDS), Updated March 02, 2022 460[3] Thomas Francis Galligan, An Urban Comprehensive Planning Model for Resource Allocation, May 05, 2020 461The 6th International Conference on Economic Management and Green Development (ICEMGD 2022) 462DOI: 10.54254/2754-1169/4/20221059463224464465466===== PAGE 6 (score 3, numeric-paren 0, table-words 0, var-words 3) =====467468[4] Wiley,Markets and Efficient Resource Allocation in Urban Areas. 469[5] Shasha Lu, Xingliang Guan, Min Zhou, Yang Wang, Land resources allocation strategies in an urban area involving 470uncertainty: a case study of Suzhou, in the Yangtze River Delta of China,2014 February 14 471[6] Feng L, Qi W, Tao Z, Huili D, Spatial Effects of Public Service Facilities Accessibility on Housing Prices: A Case 472Study of Xi’an, China,29 November,2018 473[7] Jie Chen, Qianjin Hao, The impacts of distance to CBD on housing prices in Shanghai: a hedonic analysis,11 474August,2008 475[8] Hanning Lou, Wu Xiao, Debin Lu, Understanding the Impacts of Public Facilities on Residential House Prices: 476Spatial Data-Driven Approach Applied in Hangzhou, China, February 24, 2022 477[9] Axel Viktor Heymana, Dag Einar Sommervoll, House prices and relative location, December 2019, 102373Axel 478Viktor Heymana, Dag Einar Sommervoll, House prices and relative location, December 2019, 102373 479The 6th International Conference on Economic Management and Green Development (ICEMGD 2022) 480DOI: 10.54254/2754-1169/4/20221059481225482