Research on Relationship Between Population Structure and Real Estate Investment with Big Data

Full text

Science Journal of Business and Management
2023; 11(1): 12-17
http://www.sciencepublishinggroup.com/j/sjbm
doi: 10.11648/j.sjbm.20231101.13
ISSN: 2331-0626 (Print); ISSN: 2331-0634 (Online)
Research on Relationship Between Population Structure
and Real Estate Investment with Big Data
Wenjing Xiang
*
, Gang Xia
*
School of Business Administration, Guizhou University of Finance and Economics, Guiyang, China
Email address:
*
Corresponding author
To cite this article:
Wenjing Xiang, Gang Xia. Research on Relationship Between Population Structure and Real Estate Investment with Big Data. Science Journal
of Business and Management. Vol. 11, No. 1, 2023, pp. 12-17. doi: 10.11648/j.sjbm.20231101.13
Received: January 13, 2023; Accepted: February 2, 2023; Published: February 14, 2023
Abstract:
With the rapid development of "Internet +", the current supply and demand of real estate have changed. The
introduction of big data technology to analyze the demographic structure and real estate market-related information improves the
scientific nature of real estate investment decisions. Scientifically defining the guiding role of population structure on real estate
development investment is helpful to promote the coordinated development of real estate industry. Based on this, we build a
theoretical model of the real estate development investment data analysis support system by selecting the panel data of each
province from 2002 to 2018 and using the econometric model. The impact of population structure is tested on real estate
investment from the national level and sub-regional level. The specific effect of population structure is also analyzed on
investment under the urbanization rate. The research result shows that the total dependency ratio of the population and the youth
dependency ratio is negatively correlated with real estate development investment at the national level. The elderly dependency
ratio is positively correlated with real estate development investment. At the regional level, the elderly dependency ratio is only
in the central region. As a positive impact, the juvenile dependency ratio affects real estate development investment, but the
degree of influence is different. From the perspective of the urbanization process, the higher the urbanization rate, the less
binding the juvenile dependency ratio on real estate development investment. In view of this, from the perspective of population
structure, suggestions are put forward on the supply-side structural reform of the real estate industry.
Keywords:
Population Structure, Real Estate Development Investment, Big Data
1. Introduction
In the context of the big data, researches on population
structure and real estate development investment is largely
shown in three respects below. To begin with, many scholars
explore the role of big data inside the real estate sales as well as
the operation. Scholars such as Ma Muyuan applied big data
technology to the housing management system to manage the
quantity, files, and configuration of real estate [1]. Luo Yuxia
analyzed the shortcomings of traditional real estate marketing
models and proposed the relationship between real estate
enterprises and big data [2]. The second is the research on the
big data concerning the real estate supervision and
decision-making. Scholars such as Shen Zhigang proposed to
build one real estate marketplace supervision mechanism
through data mining, for the sake of realizing early warning and
forecast of the marketplace concerning the real estate, medium,
macro and micro dissection, and to visualize the results [3].
Scholars such as Lou Liming put forward the detection idea of
the whole process of the marketplace concerning the real estate,
focusing on improving the data sharing mechanism, quantifying
indicators, and establishing mathematical models [4]. Third,
few scholars explore the correlation between population
structure and real estate investment. Jin Jianfeng studied the
demand for real estate investment [5], and Zhang Hong
conducted an empirical study on the factors affecting the
changes in the spatial agglomeration of housing advancement
investing within China. Consequences manifest that population
urbanization exerts an essential impact on the changes in spatial
agglomeration level of the real estate investing [6]. Yang
Yingying and other scholars studied the relationship between
urbanization, population structure and housing supply structure
transformation based on statistical data, and put forward

Science Journal of Business and Management 2023; 11(1): 12-17 13
rationalization countermeasures and suggestions to make
balanced development between the three [7]. On the basis of
constructing the mechanism of the influence of aging on the
housing need, Zhu Linlin, along with other scholars used the
spatial econometric model and panel threshold regression
model to empirically research the influence of aging on the
housing need. Also, they concluded that the housing price,
family size and elderly dependency ratio were passively linked
to the housing sales as well as housing sales area, that is, the
higher the house price, the smaller the number of households,
and the deeper the degree of aging, all of which will reduce the
purchase of residential housing [8]. Long Teng et found that
population urbanization in large and medium-sized cities has
effectively driven housing demand in the past decade, and has
performed in terms of floating population, family size,
population urbanization rate, and education level, and
promoted housing price increases [9]. Li Dan et found that the
size of families is becoming smaller and smaller, and the type
of family is becoming more and more simplified; The number
of households is also gradually increasing, and the demand for
housing is strong in the next decade; The demand for rental
private housing and affordable housing is increasing;
Household demographics have a significant impact on the type
of house purchased, the size of the house purchased, and the
type of house [10]. Deng Hongqian's empirical research
consequences reflect that the ratio of the dependency
concerning children and the elderly population have a
significant negative correlation to the housing prices. Also, the
population turnover rate is significantly actively linked to the
housing prices [11]. Yin Luyang investigates the correlation
between the population structure and the housing need, the
larger the proportion of children and the percentage of the
elderly inside the population, the increase in average
household size reflects the increase of the child population, the
higher the education level, the higher the unmarried rate and
the urbanization rate, the greater the housing demand [12]. Gao
yuan research shows that the influence of the old-age
dependency ratio on the housing prices is positively correlated.
Also, the per capita disposable income and urbanization rate of
the residents have an essential influence upon housing prices
[13]. Jin wei uses the Hansen non-dynamic panel regression
model to explore the correlation between the population
structure and urban housing prices during the economic
transformation. These results show that the active effect of the
population age structure upon the housing prices shows an
"inverted U" trend [14]. Shi Xiaohao analyzed that changes in
population structure are important factors affecting housing
demand, and population urbanization, population aging, and
second-child birth policies will have a significant impact on
urban housing demand [15].
Most of the existing literature is about the research on big
data technology and real estate sales, supervision and
operation systems, but there are few studies on real estate
development investment. Little literature includes population
structure in the empirical research of real estate development
investment. Few scholars extend from the national level to
different regional levels to study the distribution effect of the
population structure upon the real estate advancement
investment. For the new normal of economic growth, there are
few studies on the correlation between population structure
and the real estate advancement investing, and few scholars
have considered the intermediary effect of big data technology.
Therefore, using 31 provincial-level data in my country from
2002 to 2018, we estimated the model from three aspects. First,
based on national-level data to test whether the population
structure (total dependency ratio, old dependency ratio, and
juvenile dependency ratio) has any impact on real estate
advancement investing. The second is just to further analyze
whether the population structure plays a certain part in
regional distribution of the real estate investing based on the
regional level. The third is to explore whether the allocation of
the real estate investing is different from the population
structure under the background of different urbanization rates.
2. Theoretical Model, Variable Selection,
Data Description and Model Setting
2.1. Theoretical Model
The population is important to determine the real estate
marketplace demand as well as the real estate developers'
investing. Big data technology reflects the intermediary
effect in real estate development companies' investment
decisions. The theoretical pattern of this article is depicted
in “Figure 1”.
Figure 1. Theoretical model.

14 Wenjing Xiang and Gang Xia: Research on Relationship Between Population Structure and
Real Estate Investment with Big Data
The real estate development investment data support
system is divided into data layer and application layer. The
data layerl includes two sections: population structure and real
estate market. The population structure section includes
spending power, currency storage, housing demand, and
consumer satisfaction. The real estate marketplace includes
the number of houses, house configuration, as well as
property files. The layer of the application is for the capability
dissection of decision support. Through the data capture and
processing of demographic structure and real estate market,
online analysis reports are generated to support real estate
investment decisions.
2.2. Variable Selection and Data Description
The research data are collected from the National Bureau of
Statistics, and all variables are processed in logarithm.
Variable selection is as follows.
Real estate development investment (hi) stands for the
explained variable and expressed by the factual real estate
advancement investing in every province. Total dependency
ratio (tdr) means the rate of non-working-age population to
working-age population in whole population, indicating how
many non-working-age populations are borne by every 100
working-age people. Old-age dependency ratio (odr) indicates
the rate of the elderly people to working-age people in the
entire population, indicating how many elderly people are
borne by every 100 working-age population. Juvenile
dependency ratio (ydr) refers to the rate of the quantity of the
children and children in the total deduction to the quantity of
the working-age people, indicating how many children and
children are borne by every 100 working-age population. We
selected control variables which might impact the behavior of
the real estate advancement and investing spatial distribution
distinctions as below.
Economic development level (gprgdp): The difference in
the level of economic development among provinces is
measured by the average annual increase ratio of real GDP
every capita.
Industrial structure: The industrial structure is gauged by
the proportion of the extra value of the tertiary industry in the
whole GDP of per province.
Population scale is shown as the permanent resident
population of each province at the end of the year. To some
degree, it reflects the impact on real estate development
investment based on the demand level.
The rate of the urbanization is shown by the proportion of
the whole urban population to the whole population of each
province. It reflects the process of the transformation of
agricultural population into non-agricultural population, as
well as the size of the city and the level of infrastructure.
2.3. Model Setting
By virtue of the distinctions in the panel figures, a fixed
impact model is built for analyzing. Based on the above
discussion, the following measurement model is proposed.
 ℎ

=

+ 

ln d

+ 

ln d

+ 

ln d

+ ln

+


 ℎ

= 

+ 

ln d

+ 

ln d

+ 

ln d

+
ln

+ 

(1)
The subscripts i and t represent the province and time,
respectively, and ε_it represents the random disturbance term.
X refers to a suit of the control variables which might impact
the real estate advancement and investing behavior, such as
economic development level (gprgdp), industrial structure
(structure), population size (scale), and urbanization rate
(urbanrate).
3. Estimation Results and Analysis
3.1. Estimated Results and Analysis of the Impact of Total
Dependency Ratio on Real Estate Investment
Table 1 shows the influence of the whole dependency rate
of the population on real estate advancement investing based
on estimates at the level of the nation.
Table 1. Impact of the Total Dependency Ratio of the Population on Real
Estate Development Investment (Based on the Nation Level).
Variable Model 1 Model 2 Model 3 Model 4
lntdr -4.632*** -4.277***
-1.578*** -0.349
(-9.70) (-9.57) (-6.603) (-1.340)
lnodr
lnydr
lngprgdp 0.011 -0.061*
(0.434) (-1.786)
lnstructure -0.022 0.281
(-0.097) (1.119)
lnurbanrate 5.563*** 4.595***
(36.181) (24.953)
lnscale 3.255*** 1.113***
(8.856) (14.782)
constant 23.418*** 22.138***
-35.467*** -19.798***
(13.60) (13.62) (-11.320) (-12.572)
N 527 527 517 517
R
2
0.160 0.851
Inspection type FE RE FE RE
*
T-statistics in parentheses, *** p<0.01, ** p<0.05, * p<0.1 (Same below)
It is shown in Models 1 and 2 that the coefficients of the
entire population dependency rate variables are all essential
at the level of 1%, and are negatively correlated with real
estate development investment. For every 1% grows in the
total population dependency rate, real estate development
investment decreases by about 4.6%.
The control variables affecting real estate advancement
investing are added to the trial of the fixed effect model. The
result shows that the goodness of fit reaches 0.85. The total
dependency ratio of the population is still significantly
negatively linked to the real estate development investing at
the level of 1%. This shows that the growth in the total
population dependency rate brings about a decrease in level

Science Journal of Business and Management 2023; 11(1): 12-17 15
of social purchasing power and a decrease in housing
demand, thereby weakening the investment intensity and
scale of real estate development. The impact of industrial
structure and economic development level on real estate
development investing isn`t significant. It might be caused by
the fact that the upgrading of the structure of the industry has
little change in the housing scale, and the short-term
fluctuation of economic development level is small.
Urbanization rate and population size are both significantly
positively correlated with the real estate investing at the 1%
level. For a 1% growth in urbanization rate, the real estate
advancement investing increased by 5.6%, which reflects the
relationship between new urbanization construction and
housing demand.
3.2. Estimated Results and Analysis of the Impact of
Juvenile Dependency Ratio and Old Age Dependency
Ratio on Real Estate Investment
Table 2 shows the impact of juvenile dependency ratio and
old age dependency ratio on real estate investment.
Table 2. Impact of Juvenile Dependency Ratio and OLD Age Dependency
Ratio on Real Estate Development Investment (Based on the National Level).
Variable Model 1 Model 2 Model 3 Model 4
lnodr 2.674*** 2.777*** 0.563*** 0.560***
(11.65) (11.902) (3.23) (2.7)
lnydr -5.111*** -4.320*** -1.919*** -0.606***
(-20.971) (-18.669) (-10.758) (-2.985)
lngprgdp 0.042* -0.048
(1.732) (-1.434)
lnstructure -0.202 0.157
(-0.947) (0.633)
lnurbanrate 3.574*** 1.043***
(10.319) (13.054)
lnscale 4.649*** 4.213***
(25.374) (18.808)
constant 16.093***
13.336*** -34.950*** -18.072***
(15.093) (12.614) (-12.240) (-11.317)
N 527 527 517 517
R
2
0.596 0.871
Inspection
type FE RE FE RE
The model regression consequences reflect that the ratio of
the juvenile dependency wields an essential passive
relationship over the real estate development investing. For
every 1% growth in the ratio of the juvenile dependency, the
real estate advancement investing decreases by nearly 2%.
On the one hand, the growth in the ratio of the juvenile
dependency might bring about a relative decrease in labor
force, thus affecting the social demand for housing. For
another, the increase in the ratio of the juvenile dependency
makes the proportion of children and adolescents larger, and
the corresponding parents of the children and adolescents
may be positive. In the age group of wealth accumulation, the
purchasing power of housing is limited, the demand
decreases, and the investing in the real estate advancement
decreases accordingly. The relationship between the real
estate development investing and old-age dependency rate
has diverged, showing an essential positive correlation. For
every 1% growth in the ratio of the old-age dependency, real
estate investment increases by about 0.56%. Judging from
estimated coefficients, the sensitivity of juvenile dependency
rate to the real estate development investing is far higher
compared with that of the old age dependency rate. Thus,
these ratios of the juvenile dependency and the whole
population dependency have the same influence upon the real
estate investing. In addition, the level of economic
development and industrial structure are not significant for
the investment of the real estate. Level of urbanization rate
and population size both significantly affect the investment
of the real estate at the level of 1 percent.
3.3. Estimated Results and Analysis by Region
Considering the uneven level of economic development in
the eastern, central and western regions of China and the
differences in real estate investment, it is necessary to divide
the area to study the impact of population structure on real
estate investment in different regions. Table 3 presents the
estimation results based on the regional level.
Table 3. Regional Impact of Demographic Structure on Real Estate
Development Investment.
Variable East Central West
lnodr 0.089 0.871*** 0.465
(0.412) (3.206) (1.226)
lnydr -1.290*** -1.905*** -2.494***
(-5.031) (-8.493) (-6.710)
lngprgdp 0.142*** -0.009 0.035
(2.983) (-0.304) (0.922)
lnstructure 0.815* -0.867*** -0.436
(1.911) (-3.387) (-1.153)
lnurbanrate 6.391*** 5.374*** 3.657***
(15.197) (22.689) (11.863)
lnscale 2.239*** -0.205 5.473***
(5.737) (-0.166) (6.608)
constant -37.143*** -5.024 -40.867***
(-11.539) (-0.490) (-6.046)
N 183 133 201
R
2
0.893 0.946 0.858
The estimation results exhibit there exists evident regional
distinctions in the influence of the rate concerning the
old-age dependency upon real estate development investment.
The ratio of the elderly dependency exerts an essential active
effect upon the real estate development and investment in the
central area, while it exerts no pivotal influence within
eastern and western areas. The impact of the juvenile
dependency ratio upon the spatial distribution of real estate
development investing among disparate areas is essential at
the level of 1%, whereas the extent of influence is disparate.
The estimated coefficient shows that the juvenile dependency
ratio has the most prominent impact on real estate investment
in western areas, followed by the central area, together with
the eastern area. Due to the overall relative lag in the
economic advancement level, public infrastructure, along
with the employment opportunities inside the western area,
numerous young and middle-aged labor force outflows,
especially the total number of children, which affects the
juvenile dependency ratio. Children's parents go out to work

16 Wenjing Xiang and Gang Xia: Research on Relationship Between Population Structure and
Real Estate Investment with Big Data
to obtain higher wages, so the demand for housing in the
outflow areas decreases, which further reduces the investing
of the real estate advancement. For the sake of control
variables, the level of the economic advancement wields an
essential significant impact over the real estate advancement
investing just within eastern areas. The structure of the
industry has a significant impact on real estate development
investing in central and eastern regions. Besides, the
population size wields an essential impact over the real estate
investing within eastern and western areas, along with in the
central area. The impact of urbanization rates in different
regions on the real estate advancement investing is
significant at the level of 1 percent, and the sensitivity is the
strongest in the eastern region and the weakest in the western
region.
3.4. Impact of Population Structure on Real Estate
Development Investment Under Different Urbanization
Stages
According to the international urbanization stage division
standard, the national sample data is divided into two parts
with 60% urbanization as the cut-off point and estimated
separately. Table 4 presents the estimated results under
different urbanization rates.
Table 4. Estimation Results of Different Urbanization Rates.
Variable urbanbrate>60% urbanbrate<=60%
lnodr 0.191 0.327
(0.94) (1.41)
lnydr -0.711** -2.142***
(-2.03) (-9.76)
lngprgdp 0.109*** 0.006
(2.69) (0.18)
lnstructure 0.668 -0.121
(1.28) (-0.49)
lnurbanrate 3.724*** 4.371***
(3.65) (22.07)
lnscale 2.976*** 5.685***
(8.13) (8.65)
constant -33.567*** -49.450***
(-8.62) (-9.23)
N 107 406
R
2
0.826 0.873
The estimation results show that the impact of the juvenile
dependency ratio on real estate development investment has
significant differences under different urbanization rates. The
coefficient is only -0.71. In the process of low urbanization,
the juvenile dependency ratio significantly affects real estate
development investment at the level of 1%, and the impact
coefficient is -2.14. Under the high urbanization rate, the per
capita income, total social wealth, and employment
opportunities are far better than those in the low urbanization
development stage, and the population outflow is relatively
small. Therefore, the change of the juvenile dependency ratio
has relatively little impact on the housing demand, and has a
relatively small impact on real estate development.
Investment constraints are relatively weak. Under the low
urbanization rate, the increase of the juvenile dependency
ratio means that the labor force bears a heavy responsibility.
In order to obtain more employment opportunities and higher
wages, the proportion of population outflow increases, which
has a greater impact on the real estate market. This has led
developers to significantly reduce investment in real estate
development in areas with low urbanization rates.
4. Conclusions and Recommendations
A theoretical model of real estate development and
investment data analysis support system is constructed to
obtain relevant information on population structure and the
real estate market through data mining. Empirical research
is used to demonstrate these consequences. At the level of
the nation, ratios of the total population dependency and
youth dependency are significantly passively linked to the
real estate development investment. The ratio of the old-age
dependency is apparently actively connected to the real
estate development investment. From a sub-regional level,
the ratio of the old-age dependency wields an essential
active influence over the real estate advancement investing
just inside the central region. The juvenile dependency ratio
in different regions has a significant influence upon spatial
distribution of the real estate advancement investing, but the
degree of influence varies. If the level of economic
advancement is higher, the juvenile dependency ratio on the
real estate development investing will be less binding. This
shows the labor force in western regions goes to eastern and
central regions in pursuit of a higher standard of living,
which directly affects the housing demand and investment
scale in the western region. In different stages of
urbanization, the influence of population structure on real
estate development investment is different. Under high
urbanization rate, the negative impact of juvenile
dependency ratio on real estate development investment is
weak, indicating that the level of urbanization has improved
and the intensity of population mobility has weakened. Real
estate development investment does not fluctuate much but
at a lower urbanization rate, population mobility increases
to have a greater impact on real estate development
investment.
Based on the research conclusions, we put forward
suggestions on the supply-side structural reform of the real
estate industry. First, the government and developers need to
pay attention to the use of big data technology in the real
estate market and increase the reliability and validity of
decision-making through the analysis of the relationship
between population structure and real estate development
investment. It is also neceesary to promote the sustainable
development of the real estate market. Secondly, the
construction of the western region needs to be strengthened
to promote the balanced and coordinated development of
various regions and reduce the intensity of population flow,
Finally, differentiated policies must be formulated according
to the urbanization construction process to introduce
agricultural transfer population according to local conditions.

Science Journal of Business and Management 2023; 11(1): 12-17 17
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