# RECORD 309364b6d8361dba9a9e751b55ade4c452cd784b
# TITLE: The research of influence factors that possibly lead to the change in house prices
# YEAR: 2023.0  VENUE: Theoretical and Natural Science  DOI: 10.54254/2753-8818/14/20240926
# AUTHORS: Yi-Xiao Huang
# PAGES: 7  CANDIDATE TABLE PAGES (1-based): [3, 6]
# ABSTRACT: With the significant increase in housing prices, more research on the factors that influence housing prices is urgently needed. Most of the previous research focuses on the number data, such as the average disposable income of the population, property management fees, and the total number of the population in specific areas. In this research, the multiple linear regression model is used to judge the relationship between 10 independent variables (Carpet area, Floor, Total floor, Transaction, Furnishing, Facing, Bathrooms, Balconies, Car parking, and Ownership) and the amount of housing prices based on 910 cases. It is concluded that the ten independent variables all have strong linear relationship with the Amount. With the factors that were studied before, like the ages of the house and the policy for buying homes, these studies indeed give some ideas for the government to come up with the approach to control the increase in housing prices.


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The research of influence factors that possibly lead to the 
change in house prices 
Yixiao Huang  
HuaZhong University of Science and Technology, 430074, China 
 
u202316281@hust.edu.cn 
Abstract. With the significant increase in housing prices, more research on the factors that 
influence housing prices is urgently needed. Most of the previous research focuses on the number 
data, such as the average disposable income of the population, property management fees, and 
the total number of the population in specific areas. In this research, the multiple linear regression 
model is used to judge the relationship between 10 independent variables (Carpet area, Floor, 
Total floor, Transaction, Furnishing, Facing, Bathrooms, Balconies, Car parking, and Ownership) 
and the amount of housing prices based on 910 cases. It is concluded that the ten independent 
variables all have strong linear relationship with the Amount. With the factors that were studied 
before, like the ages of the house and the policy for buying homes, these studies indeed give 
some ideas for the government to come up with the approach to control the increase in housing 
prices. 
Keywords: Housing Prices, Multiple Linear Regression Model, India, Influence Factors. 
1.  Introduction 
The 2020 Chinese government report emphasized safeguarding the public’s access to housing. Real 
estate is one of the motivations for developing economics due to its status as a pillar industry. Based on 
the real estate revolution background, housing prices have experienced rapid growth [1]. Between 2003 
and 2013, there was a significant rise of around 13% in housing prices in China’s first-tier cities, while 
second and third-tier cities saw growth rates of 10.5% and 7.9%. These surpassed those in the US and 
Japanese real estate bubbles [2]. Meanwhile, some foreign researchers found that there was also a 
considerable increase in the housing prices in their country [3]. Simultaneously, there are many problems 
with the increasing housing prices. For instance, most youth cannot afford to buy a house, even an 
apartment. The issue of high housing prices and low income has become a challenge in urbanization 
worldwide and has attracted close attention from society, government, and academia [4]. Therefore, this 
essay aims to gain insights into the housing market dynamics and try to aid buyers, sellers, and real 
estate professionals in making informed decisions using the dataset found in Kaggle. 
In addressing the issue of rapidly rising housing prices, it is significant to understand the real estate 
market scientifically and implement relevant policies to ensure stable development of housing prices. 
Understanding the factors that influence house prices and their relationships is essential. The reasons 
behind the rise in housing prices are intricate. When studying the factors that affect the price of 
residential properties, researchers typically consider a range of variables. These may include location 
characteristics, building attributes, and the overall residential environment. Researchers may also 
Proceedings of the 3rd International Conference on Computing Innovation and Applied Physics
DOI: 10.54254/2753-8818/14/20240926
© 2023 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/).
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2.2.  Variable selection 
The data used in this paper count a total of 910 cases, including the house prices from 780 thousand 
rupees to 18200 thousand rupees. The data contains 11 variables: Amount (in rupees), Carpet area, Floor, 
Total floors, Transaction, Furnishing, Facing, Bathrooms, Balconies, Car parking, and Ownership. 
Table 1 shows the interpretation of each variable. Because some variables are presented in text form, 
and it is difficult for us to study, this essay changes the text form into numbers, and the corresponding 
numbers are shown in Table 1. 
Table 1. Logogram and interpretation of the 11 factors. 
Elements 
Logogram 
Interpretation 
Carpet area 
x1 
The area that can be used. Unit: square feet 
Floor 
x2 
The floor the home is in 
Total floors 
x3 
The total floor of the building that the home is in 
Transaction 
x4 
Whether the home is “Resale” or “New property”. “Resale” takes 0, “New 
property” takes 1 
Furnishing 
x5 
The condition of furnishing. “Unfurnished” take 0; “Semi-furnished” takes 
1; “Furnished” takes 2 
Facing 
x6 
“East” takes 0; “West” takes 1; “North” takes 2; 
“South” takes 3; “South - West” takes 4; “North - East” takes 5; “North - 
West” takes 6; “South - East” takes 7 
Bathrooms 
x7 
The number of bathrooms 
Balconies 
x8 
The number of balconies 
Car parking 
x9 
“1 Open” takes 0; “1 Covered” takes 1; 
“2 Open” takes 2; “2 Covered” takes 3 
Ownership 
x10 
“Freehold” takes 0; “Co-operative Society” takes 1; 
“Power Of Attorney” takes 2; “Leasehold” takes 3 
Amount 
y 
The total price of the home 
Table 2 shows some statistics about the variables, including the mean, the variance, the maximum, 
and the minimum.  
Table 2. Summary statistics of each variable. 
Elements 
Logogram 
mean 
variance 
maximum 
minimum 
Carpet area 
x1 
1098.18 
1284995.59 
1820 
78 
Floor 
x2 
8.09 
32.51 
18 
1 
Total floors 
x3 
14.69 
67.68 
38 
1 
Transaction 
x4 
0.25 
0.19 
- 
- 
Furnishing 
x5 
0.61 
0.59 
- 
- 
Facing 
x6 
0.65 
2.27 
- 
- 
Bathrooms 
x7 
2.81 
1.07 
4 
1 
Balconies 
x8 
1.44 
0.55 
3 
1 
Car parking 
x9 
1.44 
1.18 
- 
- 
Ownership 
x10 
0.32 
0.69 
- 
- 
Amount 
y 
1493.58 
1202749.26 
4250 
219 
Note: The maximum (minimum) doesn’t have meaning when it comes to transactions, furnishing, 
facing, car parking, and ownership. 
Proceedings of the 3rd International Conference on Computing Innovation and Applied Physics
DOI: 10.54254/2753-8818/14/20240926
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2.3.  Research protocol 
This paper uses the Linear Regression model and Single Factor Correlation Analysis, the total amount 
of the home is the dependent variable(Y), and the 10 factors are the independent variables(X). Next, this 
paper uses SPSSAU to analyze the relationship between the effect of X on Y based on the results of 
scatter plots, box plots and multiple linear regression. 
2.4.  Model principle 
Linear regression is a standard statistical learning method to establish a linear relationship between the 
independent and dependent variables. Here is the typical formula for linear regression: 
y = β0 + β1 x1 + β2 x2 + ⋯+ βp xp + ε
(1) 
Where β is the coefficient, ε is the residuals and the expectation for ε is 0. 
This paper uses scatter plots to directly see the relationship between two distinct variables. 
3.  Results and Discussion 
Figure 1 shows that it has an increasing trend, leading us to use a linear regression model. The result is 
y =  955.730 +  0.490 ∗x1, and the R square is 0.256. Because the R square is very close to 0, the 
dataset should delete the outliers to keep the R square close to 1.  
 
Figure 1. The scatter plot of carpet area and the amount. 
As shown in figure 2, after deleting all the outliers, the data seems more linear. However, the results 
show that the regression model becomes y = −301.308 +  1.705 ∗x1, and R square becomes 0.476, 
still not very close to 1. 
 
Figure 2. Processed figure 1. 
Proceedings of the 3rd International Conference on Computing Innovation and Applied Physics
DOI: 10.54254/2753-8818/14/20240926
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that all the factors have relatively strong affection on the amount. Moreover, the p-values of Carpet area, 
Floor, Total floor, Transaction, Furnishing, Bathrooms, Balconies, Car parking, and Ownership are 
smaller than 0.01, which illustrates these variables have significantly strong relationship with the 
Amount. When it comes to the collinearity diagnosis, the values of tolerance of Carpet area, Floor, Total 
floor, Transaction, Furnishing, Facing, Bathrooms, Balconies, Car parking, and Ownership all are 
greater than 0.02. The outcomes show no autocorrelation in the model and no correlation between the 
sample data, indicating that the model is of good quality. Moreover, the D-W value is around 2, which 
also illustrates that the model doesn’t have autocorrelation or correlation between the sample data. The 
second column shows the coefficients of 10 variables. The coefficients of Carpet area, Floor, Total floor, 
Furnishing, Facing, Bathrooms, Balconies, Car parking, and Ownership are all bigger than 0, with the 
result of p-values, which means these variables have significant favourable influence on the amount. 
The coefficient of Transaction is less than 0, with the consequence of p-value, so this variable has a 
significant negative impact on the amount, which means that the prices of “Resale” are universally 
greater than “New property”. 
In comparison to prior research, they tend to use specific analysis of a single factor, especially the 
factors shown in number, which is limited to known possible factors for influencing the housing prices, 
such as Facing, Transaction, Ownership, Furnishing, and Car parking, the data initially shown in the text 
form. In contrast, this study covers a wide range of factors, making it more comprehensive and effective 
in avoiding errors caused by not controlling for a single variable. It also expands ideas for future research 
on housing prices and helps sellers identify the preferences of potential buyers. 
Table 3. A slightly more complex table with a narrow caption. 
 
B 
t 
p 
collinearity diagnosis 
VIF 
tolerance 
constant 
-1347.29 
-14.413 
0.000** 
- 
- 
x1 
0.085 
3.645 
0.000** 
2.099 
0.476 
x2 
17.03 
3.483 
0.001** 
2.273 
0.44 
x3 
29.62 
8.702 
0.000** 
2.304 
0.434 
x4 
-314.184 
-6.454 
0.000** 
1.342 
0.745 
x5 
353.179 
12.373 
0.000** 
1.42 
0.704 
x6 
28.751 
2.18 
0.029* 
1.169 
0.855 
x7 
431.747 
11.769 
0.000** 
4.236 
0.236 
x8 
134.352 
4.934 
0.000** 
1.226 
0.816 
x9 
403.357 
12.619 
0.000** 
3.57 
0.28 
x10 
73.178 
3.032 
0.002** 
1.207 
0.829 
R2 
0.74 
adjustment R2 
0.737 
F 
F (10,889)=253.086,p=0.000 
D-W value 
1.822 
Dependent: y 
* p<0.05 ** p<0.01 
4.  Conclusion 
In this study, various data were analysed to determine the factors that affect housing prices. The results 
suggest that changes in housing prices are linked to factors such as carpet area, floor, total number of 
floors, transaction, furnishing, facing direction, number of bathrooms and balconies, availability of 
Proceedings of the 3rd International Conference on Computing Innovation and Applied Physics
DOI: 10.54254/2753-8818/14/20240926
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parking space, and ownership. Most of the factors studied in this paper have not received much attention 
in previous studies.  
It is true that the model may have errors due to the limited amount of data. Additionally, the sample 
did not cover all conditions from different countries or discuss policy and economic backgrounds, which 
could cause possible variations and affect the accuracy of the results. The study still has some advantages. 
Firstly, the data was from India, and was just released last month, which ensures the uniqueness of our 
data. Secondly, this essay chooses the data in text form and changes them into number form, which is 
really innovative. Thirdly, this essay uses more than one method to get the outcomes. It ensures the 
feasibility of the linear regression model and eliminates errors. At last, we draw a conclusion that the 10 
variables all have relationship with the amount of the housing prices, but it is still a preliminary 
conclusion. Further investigation is needed to determine if these factors are linked to changes in housing 
prices, and these findings may guide future research. Once other factors that influence housing prices 
are found, the actions that will be taken to control the prices may come out more quickly and more 
efficiently. 
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