Build Real Estate Evaluation Model based on Customer Requirement
after COVID-19
WEN-PIN HUANG
Program in Finance and Banking,
Kaohsiung University of Science and Technology,
TAIWAN
CHIH-HSING HUNG
Department of Money and Banking,
Kaohsiung University of Science and Technology,
TAIWAN
Key-Words: Real Estate Investment; Fuzzy Number; VIKOR; COVID-19.
Received: June 13, 2021. Revised: January 24, 2022. Accepted: February 12, 2022. Published: March 2, 2022.
1 Introduction
Real estate is very important necessities for
customer [1]. In Taiwan, male citizen who have
houses under their names in the country have an
average of 1.52 transactions per person, while
female citizen who have houses under their names
have an average of 1.44 transactions per person
(Refer to Table 1). Volume of Taiwan total citizen is
23,548,633. On average, a half of citizens can
possess their house if everyone buys single real
estate. So, real estate not only is an expensive
necessity but also can be bought by major part of
citizens once in a lifetime.
Table 1. Statistics of Taiwan property tax holders
Gender
Volume of
People (A)
Volume of
House (B)
Average
(A/B)
Male
Holder
4,279,686
6,484,769
1.52
Female
Holder
3,507,587
5,047,081
1.44
Total
Citizen (C)
Total
House (D)
Average(D/C)
23,548,633
11,531,850
48.97%
Bilateral matching theory is the core of the theory.
The concept of bilateral matching was first
summarized and put forward by Erwin Ross.
“Bilateral” emphasizes that participants in the
market belong to two disjoint sets. “Matching”
emphasizes the bilateral nature of market exchanges.
And both parties have stability preferences. Bilateral
matching theory takes bilateral matching as the
research object and studies the matching process of
disjoint two parties with stable preferences.
In practical, customer usually choose their house
(real estate) based on their financial ability. This is
the matching process; major part of rich customer
prefers to live in the mansion to show status and
major part of poor customer like to live in small
house based on economics consideration. Bilateral
matching theory can be applied on the real estate
evaluation based on customer requirement. In this
model, customer preference for real estate will be
adjusted by triangular fuzzy number in order to
match customer’s economics ability and luxury
degree of real estate.
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Wen-Pin Huang, Chih-Hsing Hung
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Abstract: - In Taiwan, real estate is not only the high price product but also is necessities. Every family needs
real estate to live. Maybe, some citizens decide to rent house in the short period. But, major part of citizen will
purchase real estate in the future. In past researches, evaluating performance of real estate mainly consider the
function and condition of this house such as location, house type, floor, building age etc. However, the demand
of specific consumer is importance factors to evaluate the performance of this customer. Especially, the
requirement of real estate for consumer has been changed after COVID-19. The goal of this study is to build
the evaluation model and relative criteria to evaluate performance of real estate in order to fit with the
requirement of specific real estate consumer. A case study is implemented for the reader to realize the proposed
method. Sensitive analysis will be executed and proposed method will be compared with traditional multi
criteria method to justify the usefulness of this method. Some conclusion and future research will be taken over
as ending.
This study includes seven main chapters. In chapter
two, literature review has been discussed. In chapter
three, some notation and operation about linguistic
variable and fuzzy number should be in introduced.
In chapter four, proposed model should be
introduced. After that, case study could be
implemented in order to let reader understand this
method. Sensitive analysis should be executed and
proposed method will be compared with some
traditional multi criteria method to justify the
usefulness of this method. Finally, some conclusion
and future research will be discussed as ending.
2 Literature Review
Below literatures are past study about real estate
relative research. Pourkhabbaz et al. [2] designed
the framework to choose suitable locations for agri-
cultural land by integrating simple additive
weighting (SAW), analytic network process (ANP)
and VIKOR under geographic information system
environment. Ho et al. [3] considered housing goals
and risk attitudes of real estate consumer and used
fuzzy goal programming with an S-shaped utility
function to help real estate consumers select their
preferred house in the internet. Morteza et al. [4]
combined fuzzy technique for order preference by
similarity to an ideal solution (fuzzy TOPSIS) with
analytic network process to analyze suitable tourism
operation location in Qeshm Island of Iran with the
goal of maximizing enterprise profit. Wu and Kou
[5] collected experts’ opinion under twelve criteria
and designed consensus model for integrating their
opinions. AHP is used to analyze real estate
investment target. Del Giudice et al. [6] collected
data in a central urban area of Naples and applied
genetic algorithms (GA) to analyze the relationship
between geographical location of housing units and
real estate rental prices. Based on their experiment,
the predict ability of formula generated by GA is
better than it generated by multiple regression
analysis (MRA). Guarini et al. [7] considered
governmental factors, regulatory dimensions and
technical content to decide which multi criteria
decision method is suitable to handle real estate
selection problem in Europe. In this research, they
designed overall suitability index (IS) to evaluate
and select one kind of MCDM which includes
MACBETH, TOPSIS, ANP, AHP, MAUT,
PROMETHEE and ELECTRE for expert to make
real estate investment selection. Omidipoor et al. [8]
arranged some real estate evaluation criteria such as
land price, area, proximity to transportation stations
etc and implement decision support system by
combined AHP techniques with geographic
information system (GIS) and into the web platform
for making real estate investment decision in in
Tehran, Iran. Renigier-Biłozor et al. [9] integrated
automated valuation model (AVM), Rough Set
Theory (RST), Value Tolerance Relation (VTR) and
Fuzzy logic to evaluate and monitor price of real
estate. Comu et al. [10] collected thirteen real estate
project criteria and integrated project delivery
method (PDM) with Fuzzy analytical hierarchy
process (Fuzzy AHP) to evaluate and prioritize the
appropriate real estate projects in Turkey. Nguyen et
al. [11] designed grey multi-criteria decision-
making support model to analyze real estate
alternatives in Vietnam. In their method,
DEMATEL was applied to acquire subjective
weights of each criterion. Grey relational analysis
(GRA) was used to rank real estate alternatives.
Myskova handled waste water treatment plant
investment project selection problem by RBSTP.
They also analyzed the advantages and
disadvantages of the RBSTP method [12] Above
literatures has been arranged as Table 2.
Table 2. Literature of research
Author
Year
Method
Target
Myskova et
al.
2013
RBSTP
Waste water
treatment
plant
investment
project
selection
Pourkhabbaz
et al
2014
(1) SAW (2) AHP
(3) VIKOR
Agri-cultural
land
selection
Ho et al.
2015
(1) Fuzzy goal
programming (2)
S-shaped utility
function
Real estate
selection
Morteza et
al.
2016
(1) ANP (2) Fuzzy
TOPSIS
Tourism
operation
location
selection
Wu and Kou
2016
(1) consensus
model (2) AHP
Real estate
selection
Del Giudice
2017
Genetic algorithms
Real estate
price
forecasting
Guarini et
al.
2018
(1) suitability
index (2)
MACBETH
(3)TOPSIS(4)ANP
(5)AHP (6)MAUT
(7)PROMETHEE
(8)ELECTRE
Real estate
investment
selection
Omidipoor
et al.
2019
(1)AHP (2)GIS
Real estate
investment
decision
Renigier-
2019
(1)Automated
Real estate
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Biłozor et al.
valuation model
(2)Rough Set
Theory
(3)Value Tolerance
Relation (4)Fuzzy
logic
price
forecasting
Comu et al.
2020
(1) Project
delivery method
(2) Fuzzy AHP
Real estate
investment
project
selection
Nguyen et
al.
2020
(1) DEMATEL (2)
Grey relational
analysis
Real estate
investment
decision
According to above researches, each kind of multi
criteria decision making method (MCDM method)
has been used to make real estate investment
decision. Besides, some paper will integrate
geographic information system (GIS) and
machining learning technology to forecasting price
of real estate. However, major part of citizen buys
real estate (house) for living and only a few citizens
purchase real estate as investment target [13].
Owner-occupied homebuyers will purchase real
estate according to their housing needs and the
future price trend of real estate is not their main
consideration.
To my best knowledge, major part of literatures
discusses about real estate investment mainly
consider the future price trend of real estate. In the
past literatures, real estate has been considered as
investment target. But, the function of real estate is
used to live. So, it needs a mechanism to evaluate
performance of real estate based on live requirement
of owner-occupied homebuyers. This mechanism
does not exist in the past research until now. The
goal of this study is to build the evaluation model
and relative criteria to evaluate performance of real
estate in order to fit with the requirement of specific
real estate consumer.
3 Preliminary
3.1 Linguistic Variable
Linguistic variable is the useful tool which can let
experts or decision makers to express opinion
friendly.
Definition 1. Suppose that 
means
linguistic variables. Above linguistic variables will
compose as linguistic term set V. z represents the
scale of linguistic variable [14-15].
Definition 2. Suppose that F:V->R means the
transfer function. The execution process of above
function is to transfer linguistic variable into real
value r (r [0,1]) [16]. The execution calculation
process of above function could refer to.
=r
(1)
Definition 3. There are two characteristics in
linguistic set V[16].
If 󰇛󰇜>󰇛󰇜>
(2)
Suppose that Neg() means negative function in
linguistic set V.
Neg󰇛󰇜=
(3)
Definition 4. Suppose that  :R->V means
linguistic transfer inverse function which can
transfer crisp value r (r[0,1]) into linguistic variable
[16].
󰇛󰇜=󰇛󰇜
(4)
3.2 Triangular Fuzzy Number
Triangular fuzzy number is important concept in
fuzzy theory. The definition of triangular fuzzy
number can refer to Definition 5 and Definition 6.
Definition 5. Suppose that G means fuzzy set
G=󰇝󰇛󰇜󰇞, where K means the universe
of discourse, 󰇟󰇠 means the membership
function, 󰇛󰇜 means the membership degree of
element k to Set G [17].
Definition 6. Suppose that 󰆻=󰇛󰇜 mean
triangular fuzzy number (TFN). , and
represents the left point, middle point and right
point in TFN. TFN can be formulated by follow
function [18].
󰆻󰇛󰇜=



(5)
3.3. Maximum Deviation Method
The maximum deviation method under linguistic
environment is developed by Wu and Chen [19].
Generally speaking, the maximum deviation method
will be used to obtain the weights according to the
maximum deviation ideas. The concept of
maximum deviation method is that the criteria
should possess relatively more weight if experts’
opinions in this criteria has relatively high deviation
degree.
Definition 7. Suppose that

means performance
of real estate i respect to dimension j for expert h’s
opinion. According to the content of past literatures,
the function of maximum deviation method can be
described as follows [19-21].
= 󰇡

󰇢󰇡

󰇢



󰇡

󰇢󰇡

󰇢




(6)
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where means the importance of expert h, n
means the volume of real estates.
4 Proposed method
4.1 Notation of Proposed Method
General speaking, real estate selection problem
should be formulated based on Table 3[22-23].
Table 3. Notation of Proposed Method
Set Name
Set Name
Description
Member
set
={}
k means volume
of consumers in
the family
Member
importance
set
={}
k means volume
of Members in
the family
Real estate
target
R={}
m represents
volume of Real
estate target.
Criteria set
C={}
n represents
volume of
criteria.
Criteria
Weight set
W={}
n represents
volume of
criteria.
Decision
matrix X
󰇯 
  

 
󰇰
Decision
matrix E
󰇯 
 

󰇰
4.2 Execution Process of Proposed Method
At the beginning, real estate enterprise will organize
a committee for handling real estate evaluation
problem and invite some experts into this committee
(Experts comes from university, company and
government). Real estate enterprise will decide real
estate criteria and real estate target for the member
in family to evaluate performance of each real estate
based on family member’s requirement. And
maximum deviation method is used to evaluate
weight of each criterion. VIKOR is employed to
analyse which real restate is suitable to this family.
The execution step of proposed method is as follows
(Refer to Fig 1).
Fig. 1: Execution process of proposed method
Step 1. Organize committee
At first, real estate enterprise should invite expert to
organize this committee.
Step 2. Decide criteria
Experts will collect real estate relative literatures
and decide criteria.
Step 3. Decide real estate alternative
Real estate enterprise will select some real estate
alternative for family members to consider to
purchase based on their requirement.
Step 4. Family members express their opinion
Family members’ opinions (the performance of real
estate targets respect to the entire criteria) should be
collected. Suppose that  be the opinion of family
member k about performance of country i with
respect to criterion j.
Step 5. Integrate family members’ opinion
Family members’ opinion can be integrated by
follow equation
=󰅿󰇡
 󰇢
(7)
Step 6. Transfer family members’ opinion as
evaluation value.
Although, high performance of real estate is good
for family member. However, it also means high
price for real estate with high performance because
every consumer will be attracted in this kind of real
estate. So, a suitable real estate should be in-
qualified (not over-qualified) for the family because
the over-qualified real estate can generate a few
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benefits for family but the price in this kind of real
estate will be very high because the quality is
relatively high compare with other real estate.
In this research, three kinds of function have been
used to transfer family members’ opinion into
suitable evaluation value. The function is as follows.
(1) Decrease function (Refer to Fig 2)
󰇡󰇢=

(8)
where =, =
In decrease function (Refer to Fig 2), the
evaluation value of  is high if the original family
members’ opinion is low.
(2) Triangular Fuzzy Number function (Refer to Fig
3)

󰇡󰇢=





(9)
where =, = is the best prefer quality for
family.
(3) Level function (Refer to Fig 4)
󰇡󰇢=


(10)
where =, =, and are critical point for
family member.
Fig. 2: The membership function of decrease
function
Fig. 3: The membership function of triangular fuzzy
number
Fig. 4: The membership function of level function
Step 7. Calculate weight of each criteria
And then, weight of each real estate criterion based
on maximum deviation method should be acquired
based on below equation.
= 󰇡󰇢󰇡
󰇢



󰇡
󰇢󰇡
󰇢




(11)
Step 8. Calculate PIS and NIS of real estate
Afterward, positive ideal solution (PIS) and
negative ideal solution (NIS) must be calculated
based on below equation.
=
(12)
=
(13)
Step 9. Calculate total performance and
individual regret value of real estate
Total performance of real estate (the largest
group utility)  can be acquired by following
equation.
=


(14)
The individual regret value of real estate (the
smallest individual regret) can be acquired by
following equation.
=

(15)
Step 10. Calculate Q value
Q value will be calculated to acquire rank of
each real estate for family.
=v*
+( 1-v)*

(16)
where v mean decision parameter ，=,
=, =, =.
The fewer ，the better real estate.
5 Case Study
A case study is implementing in practical real estate
selection problem for this research. One of Taiwan
real estate enterprise wants to help a family to select
the suitable real estate for this family to live. The
family has five members. Among them, four adult
families will evaluate performance of each real
estate. But, each family member has different
preference for selecting real estate respect to each
criterion. Proposed model has been designed for
helping the family to select suitable real estate to
live. The relative process is executed as below step
based on proposed method.
Step 1. Organize committee
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One of Taiwan real estate enterprise wants to
redesign real estate criteria and seven experts are
invited to organize the committee.
Step 2. Decide criteria
Experts collected seven criteria to evaluate
performance of real estate. Those criteria are space
(), location (), building age (), floor (),
surrounding life functions () and equipment and
decoration ( ). Those criteria are suitable to
evaluate performance of real estate. There are some
literatures which apply above criteria to make real
estate selection (refer to Table 4).
Table 4. Real estate selection criteria
Criterion
No.
Criterion Name
Literatures
()
space
[1,24, 27]
()
location
[5, 23-24]
()
floor
[6-7, 24-25]
()
building age
[9, 24, 27]
()
surrounding life
functions
[10, 24, 26]
()
equipment and
decoration
[7, 27]
Step 3. Decide real estate alternative
In this study, five real estate alternatives are chosen
for making real estate purchase decision based on
family members’ requirement.
Step 4. Family members express their opinion
In this case, the family has five members. Among
them, four adult families will evaluate performance
of each real estate. Family member’s opinion can
refer to Table 5 and Table 6.
Table 5. Linguistic variable
Linguistic variable
Abbreviation
Extremely Poor
EP
Very Poor
VP
Poor
P
Medium Poor
MP
Fair
F
Medium Good
MG
Good
G
Very Good
VG
Extremely Good
EG
Table 6. Performance of each real estate
Family Member 1’s
opinion
Family Member 2’s
opinion
F
G
EG
MP
F
MG
VG
EG
MP
G
MG
F
P
EG
VG
MG
VG
P
MG
EG
VG
F
F
G
VG
MG
MG
MP
MG
EG
MG
F
P
P
P
F
MG
MG
P
MP
MG
G
G
MP
MG
VG
VG
MG
F
VG
F
MG
MG
MG
MG
MG
G
F
MG
P
Family Member 3’s
opinion
Family Member 4’s
opinion
F
G
VG
MP
F
MG
MG
VG
F
G
MG
MG
MP
EG
EG
G
MG
MP
VG
EG
VG
F
F
G
VG
G
MG
MG
MG
EG
F
MP
MP
MP
P
MP
VP
MP
P
EP
MG
F
G
F
F
G
VG
MG
MG
MG
MG
MG
VP
G
F
VG
G
MG
MP
F
Step 5. Integrate family members’ opinion
In this work, formula 7 is used to integrate family
members’ opinion. (Please refer to Table 7)
Table 7. Family members’ integration opinion (crisp
value type)
0.5625
0.7500
0.9375
0.4063
0.6250
0.6563
0.6563
0.3125
0.8750
0.9688
0.7813
0.5625
0.5000
0.6875
0.9375
0.5000
0.4063
0.4063
0.2813
0.2188
0.7188
0.7500
0.6875
0.5000
0.6563
0.6563
0.6875
0.4688
0.5938
0.4688
Step 6. Transfer family members’ opinion as
evaluation value.
In this study, each criterion will use each kind of
function to transfer family members’ opinion as
evaluation value. The transfer function which is
used in each criterion can refer to Table 8. The
transfer result can refer to Table 9.
Table 8. The transfer function and its parameter for
each criterion
Criterion
No.
Criterion
Name
Function
Parameter
()
Space
Triangular
Fuzzy
Number
function
=(5/8) EG
()
Location
Decrease
function
()
Floor
Level
function
=(4/8) F
=(6/8) G
()
Building
age
Decrease
function
()
Surrounding
life
functions
Triangular
Fuzzy
Number
=(5/8) MG
()
Equipment
and
decoration
Triangular
Fuzzy
Number
=(5/8) MG
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Table 9. Transfer result of family member’s opinion
0.9000
0.6667
0.1667
0.3499
1.0000
0.3437
0.3437
0.6875
0.1250
0.0312
1.0000
1.0000
0.5000
1.0000
0.5000
0.5000
0.5937
0.5937
0.7187
0.7812
0.7499
0.6667
0.8333
0.2000
0.9165
0.9165
0.8333
0.2499
0.9501
0.2499
Step 7. Calculate weight of each criteria
In this study, maximum deviation method (formula
11) are applied to acquired weight of each criterion.
(Refer to Table 10).
Table 10. Weight of each criterion
Weight
0.0938
0.1421
0.0831
Weight
0.2252
0.1769
0.2789
Step 8. Calculate PIS and NIS of real estate
Positive ideal solution and negative ideal solution
can be calculated as Table 11.
Table 11. PIS and NIS
PIS
1.0000
0.6875
1.0000
0.7812
0.9165
0.9501
NIS
0.1667
0.0312
0.5000
0.5000
0.2000
0.2499
Step 9. Calculate total performance and
individual regret value of real estate
Total performance and individual regret value of
each real estate can be calculated as Table 12.
Step 10. Calculate Q value
In this research, decision parameter set up as 0.5.
After calculating the Q value of each real estate, the
rank of each real estate is >>>>>.
Table 12. Total performance and individual regret
value
Total
performance
0.3654
0.3703
0.6265
0.4219
0.5041
Individual
regret value
0.2252
0.1502
0.2789
0.1769
0.2789
Q value
0.2914
0.0094
1.0000
0.2121
0.7656
Rank
3
1
5
2
4
6 Sensitive Analysis and Comparison
Result of Different Multi Criteria
Decision Method
6.1 Sensitive Analysis
In order to justify the steadiness of proposed
method, we adjust decision parameter to analyze the
rank of each real estate (Refer to Fig 5). According
to analysis result, we can know that real estate
is the suitable real estate alternative except
decision parameter set up as 1.0. So, proposed
method is relatively steady method in choosing real
estate for the family.
Fig. 5: Sensitive analysis of proposed model
6.2 Comparison Result of Different Multi
Criteria Decision Method
Proposed method also compares with traditional
multi criterion decision making method. According
to experiment result, we can know that real estate
is the “best” real estate which is decided by each
kind of traditional multi criterion decision making
method. But, the sequence of real estate is
ranked No. 3 by proposed method. This is because
proposed method will adjust performance of each
estate based on family members’ requirement. In
some criteria, family members do not need highest
performance of real estate and real estate with
highest performance usually need highest price to
purchase. This will waste family’s budget because
the function of best real estate exceeds the
requirement of family member’ requirement and
benefit of over-qualified function for family
member is low. Best real estate is usually not the
suitable real estate for the family.
Table 13. Rank of each real estate between different
MCDM method
Rank
1
2
3
4
5
VIKOR + Maximum
Deviation Method with
Fuzzy Number Function
VIKOR + Maximum
Deviation Method
without Fuzzy Number
Function
TOPSIS + Maximum
Deviation Method
SWA + Maximum
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2022.19.65
Wen-Pin Huang, Chih-Hsing Hung
E-ISSN: 2224-2899
745
Deviation Method
6.3 Discussion
Although, this detail tool such as triangular fuzzy
number, VIKOR, maximum deviation method is not
new. But, past research does not integrate above
three tool to evaluate performance of real estate.
Because, past research usually evaluate performance
of real estate based on the luxury degree of real
estate.
However, performance of real estate should be
adjusted by triangular fuzzy number based on
customer’s demand and customer’s economics
ability. This point does not consider by past real
estate research.
According to chapter 6.2, each kind of traditional
multi criterion decision making method choose
as the “best” real estate. But, the “best” real estate
does not mean “best” real estate is the “fittest” real
estate for customer because it will waste and go
beyond family’s budget.
7 Conclusion
In this research, the integration method to evaluate
performance of real estate has been built. The
advantage of proposed methods has three points.
(1) Proposed method satisfies consumer’s need
In past research, performance of real estate will be
analysed and evaluated according to experts’
opinion. However, experts’ profession opinion is not
equivalent to consumer’s preference. Proposed
method is the minority research to make real estate
decision based on consumers’ preference and
requirement. Consumers’ preference and
requirement can be easy to adjust by function of
fuzzy number.
(2) The execution result of proposed method is
steadiness
Based on experiment result of case study, we can
know that real estate is the suitable real estate for
family when decision parameter has been changed
between 0.0 and 0.9. So, proposed method is stable
method in execution result.
(3) Proposed method is easy to implement
Each execution step of proposed method is mainly
simple mathematics computation. So, proposed
method is easy to implement by decision support
system for each family to select suitable real estate
based on each family’s preference.
In the future, some scholars can apply proposed
method in relative expensive product fields such as
car, motorcycle and oversea tourism. Above product
can be very expensive in the top-level. So, car,
motorcycle and oversea tourism should be chosen
based on consumer’s requirement and the
requirement should not be over-qualified to avoid
high price which cannot afford by consumer.
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Conflicts of Interest: Authors declare that no
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WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2022.19.65
Wen-Pin Huang, Chih-Hsing Hung
E-ISSN: 2224-2899
747