A Study on Customer Satisfaction in the Real Estate Market

A Study on Customer Satisfaction in the Real Estate Market
Based on PLS Path Modeling
YANG Wei zhong, ZOU Shu liang
Nuclear energy economics & management research centre, University of South China, P.R.China,
421000
Abstract With the increasing competition in the real estate market, more and more enterprises recognize
the importance of fighting for market share and winning customers. This makes the real estate
developers pay more attention to the customer satisfaction. We have constructed the customer
satisfaction model in the estate market based on the CCSI model, ACSI model, and many scholars’
research results, and used the partial least squares (PLS) path modeling method for estimating, thus,
factors which most affect customer satisfaction have been identified. Some implications about
developing strategies and market investigation for the real estate firms are inferred.
Keywords Customer satisfaction, PLS, PLS path modeling, Real estate market
1 Introduction
With the accelerating urbanization of China, the real estate consumption market has become another
hot spot, the buyer’s market has taken shaped, and customers has more requirements for the housing
quality and the locate environment. Different cultural standards, different areas, different levels, and
different economic status of the customers have different needs on the commercial house, in this
environment, real estate development enterprises should understand customer needs and behaviours, and
develop corresponding construction standards in order to change potential customers into real
customers.
Cardozo first did the experimental study of customer satisfaction in 1965. From then on, researches
have done many studies on customer satisfaction, but there are few studies in the real estate industry
because of the special attributes of commercial houses. Roy and Cochrane, in their study of speculative
house building, argued that changes were needed in most aspects of the business organization to develop
and implement a customer-focused strategy. Anders H. Westland’s research on the customer satisfaction
and financial results in the Swedish real estate market supported that the customer satisfaction was
highly related to profitability. Li Yu and Ling Sheng made deep study on customer satisfaction
assessment indexes of housing industry. The work of foregoing scholars enriches theory on customer
satisfaction in the real estate market. Scholars have made big progress in this field, however, systematic
and qualitative research on the factors influencing customer satisfaction in the real estate market is still
in exploration phase.
This research has constructed the customer satisfaction in the estate market model based on the CCSI
model, ACSI model, and many scholars’ research results. To provide decision support for the real estate
development companies to developing strategic planning, and to help them provide more satisfactory
houses to customers, thereby increasing corporate profits and activating the real estate market, is our
main purpose.
2 Methodology
2.1 Research model
Customer satisfaction cannot be measured directly, so a research model and a unique statistical
analysis tool are needed. The model is composed of two submodels: the structural model and the
measurement model. The structural model includes the relation between the latent variables and is
represented in Fig.1. Customer satisfaction is the central variables of this model, having as antecedents
the customer expectations, perceived quality of the commercial house and services, and perceived value.
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The main consequents of customer satisfaction as specified by the model are customer complaints.
Unlike the ASCI and CCSI models, the construct customer loyalty is not designed in this model because
most of customers rarely have repurchase intention except a few professionals engaged in the real estate.
The model therefore consists of one exogenous latent variable (customer expectations) and four
endogenous variables. Tab.1 lists the manifest variables (in a reflective way) associated with each latent
variable.
Customer
expectations
Perceived
value
Customer
satisfaction
Customer
complaints
Perceived
quality
Fig.1 latent model for customer satisfaction in the real estate mark
Tab.1 manifest variables, factor loadings ( λ ) and composite reliability ( ρ )
Latent
variables
Customer
expectation
s ξ1
ρ =0.709
λ
Manifest variables
）overall expectations for quality of the commercial house（ x ）
）expectation regarding customization of the commercial house（ x ）
3）expectation for potential increases in house appreciation（ x ）
1）overall evaluation of quality experience of the house（ x ）
2）perceived project quality of the house （ x ）
3）how well the house meet the customer’s area requirements （ x ）
4）perceived traffic conditions around the location（ x ）
5）perceived social human environment around the location（ x ）
6）perceived living environment around the location（ x ）
7）quality of the property management services provided （ x ）
1）rating of quality given price（ x ）
2）rating of price given quality（ x ）
1）overall satisfaction（ x ）
2）fulfillment of expectations (performance that falls short of expectations)
（x ）
0.526
1
0.817
0.650
21
0.683
22
0.795
23
0.759
24
0.672
25
0.782
26
0.841
Perceived
value ξ 3
ρ =0.891
0.836
Customer
satisfaction
0.836
ξ4
ρ =0.855
0.890
12
13
0.792
Perceived
quality ξ 2
ρ =0.906
11
2
27
31
0.890
32
41
42
Customer
complaints
1
ξ5
ρ =1.000
1.000
Notice: The
composite
）to what extent do the customers intend to complain to the real estate
dealers? （ x ）
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reliability of
manifest
variables
is
measured by the
formula:
ρ = (∑ λ h ) 2 (∑ λh ) 2 + ∑ (1 − λh ) 2
k
k
k
Since constructing this model through the application of the ASCI and CCSI models, and we aim to
not probe why the latent variables affect each other, but to what extent they influence each other, we
advance these following research hypotheses without further specification:
H1 Expectations for quality, customization and potential increases will positively influence the
perceived quality;
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H2 Perceived value will be positively influence by the expectations for quality, customization and
potential increases;
H3 Perceived quality in real estate market will have a positive effect on perceived value;
H4 Expectations for quality, customization and potential increases will have a positive effect on
customer satisfaction in the real estate market;
H5 Quality experience, project quality, area, traffic condition, social human environment living
environment and property management services will positively influence the customer satisfaction in the
real market;
H6 Customer satisfaction in the real estate market will be positively influence by the perceived value;
H7 Customer satisfaction in the real estate market will negatively influence the customer complaints.
Based on the latent model and associated indexes of the model, a questionnaire was designed with 15
questions, and all questions are on a scale of 0 to 10. The value 0 corresponds to a very negative point of
view, and a value of 10 to a very positive opinion. About 600 questionnaires were mailed out along with
a letter. This letter detailed the purpose of this study, and encouraged customers to participate without
disclosing personal information. The number of returned questionnaires was 298, but after a procedure
of pre-filtering, only 233 were usable. In our research, we used the SmartPLS 2.0 for estimating which
is a software application for (graphical) path modeling with latent variables.
2.2 PLS path model specification
The structural model equation can be written as:
ξj =
β jiξi + ς j
(1)
∑
j ≠i
β ji =path coefficient: ς j =residual term.
Before giving the measurement model equation, we must specify the type of relationship between latent
variables and manifest variables. There are 2 ways: the reflective way, the formative way,
In the reflective way, the manifest variables are considered dependent variables:
x jh = λ jhξ j + ε jh
2a
（ ）
λ jh =loading; ε jh =residual term
In the formative way, the manifest variables are considered independent variables:
ξj =
∑π
jh x jh + δ j
…. (2b)
h
π jh =weight; δ j =residual term
Without loss of generality, it can be assumed that residuals and manifest variables are scaled to zero
means and latent variables are standardized to have zero mean and unit variance so that scale ambiguity
can be discarded in the above equations.
2.3 PLS path model estimation
The PLS method for estimating structural equation model has two stages: the first estimates the
observation of the latent variables (case values) with an iterative scheme. The second estimates the
parameters of the structural equations and measurement model.
PLS is supported by an iterative process that iterates between two estimations of the latent variables:
the external estimation and the internal estimation. In each iteration the external estimation produces an
estimate for each latent variable as a weighted mean of their associated manifests. The external
estimation Y j is given by:
(3)
Y j ∝ X jW j
a (j×k) outer weight matrix, and the symbol ∝ means to normalize the result of
operation. The internal estimation produces another estimate for the latent variables. Here each
variable is obtained as a combination of the external estimation of the other latent variables directly
connected to it. The internal estimation Z j is computed as:
Where
W j denotes
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Zj ∝
∑e Y
(4)
ji i
i≠ j
Where e j denotes a (j×i) inner weight matrix, and various weighting schemes have been used in this
contest, the best well-known being: the centroide, the factorial and the structural schemes. Wold
favored the centroide scheme while Wynne W. Chin discusses other weighting schemes should be
chosen according to the sign of the correlation between latent variables. However, the choice often
makes little difference.
Then these outer weights can be obtained iteratively by means of series of ordinary least squares
applied to each block of manifest variables. A distinction is made between two modes of weight
estimation according to the relationship between a latent variable and its manifest variables.
Mode A (for reflective variables):
(5a)
w jh = cor ( x jh , Z j )
Mode B (for formative variables):
W j = ( X j′ X j ) −1 X j′Z j
Before starting the iteration process, the weight
Wj =
（1，0…0）.
Wj
(5b)
is initialized with random values, for example,
After that, the algorithm obtains the estimation of the new weight W j ( 2) by means of computation the
equations from (3) to (5). Then iterate the procedure until convergence (using a stopping rule based on
relative change from previous iteration). From this process each latent variable is determined both by the
inner and outer structure. Finally, case values for the latent variables are obtained, allowing using the
Ordinary Least Squares (OLS) to estimate, in a non-iterative way, the structural model coefficients, as
well as the measurement model loadings.
3 Results
3.1 Test of the measurement model
The PLS method simultaneously assesses the theoretical propositions and the properties of the
underlying measurement model. Internal consistency of measures, i.e. their unidimensionality and their
reliability must be verified first. Unidimensionality is usually satisfied by retaining manifest variables
whose factor loadings (lambdas) are above 0.5. As shown in Tab.1, these estimates of composite
reliability of latent variables range from 0.709 to 1.000, which are well above the threshold of 0.7
suggested by JÖreskog and SÖrbom (1989) and thus acceptable. Internal consistency is implied.
The third property to be verified is discriminant validity, In order to assess this property, this study
calculated square root of average variance extracted (AVE) for each latent variable and compared them
to the intercorrelation of the latent variables. Tab.2 shows that the square root of all AVE estimates for
each latent variable are greater than correlation values between latent variables, thus, discriminant
validity is supported.
Tab.2 The squared AVE, correlations values and Q 2 values
Latent variables
Q 2 values
1.Customer expectation
1
0.004
2.Perceived quality
0.092
3.Perceived value
0.185
4.Customer satisfaction
0.378
5.Customer complaints
0.433
Notes: Diagonal=the squared average
AVE =
∑
k
λh
2
∑
k
λh
2
+
∑
2
3
4
5
0.675
0.420
0.763
0.349
0.482
0.896
0.234
0.691
0.523
0.864
-0.015
-0.493
-0.229
-0.668
1.000
variance extracted, subdiagonals=correlations values,
2
(1 − λ h )
k
Finally even one’s model consists of latent variables with high levels of internal consistency, in order
to be consistent with the causal-predictive goal of PLS, greater focus should be paid on the predictive
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relevance of a model. The extent to which this prediction exercise is successful can be measured by the
Q 2 statistic proposed by Ball (1963). In our study we pick an omission distance (D) =8. Q 2 > 0 indicates
predictive relevance, and Tab.2 shows the Q2 values.
3.2 Test of structural model
The research hypotheses are tested by assessing the direction, strength and level of significance of
the path coefficient estimation by PLS. Tab.3 summarizes the results obtained in testing the research
hypotheses with the PLS method. As an extension of multiple linear regressions, partial least squares
regression has many of the same assumptions. Because the distribution of PLS is unknown, there is no
conventional significance test. However, significance can be tested through bootstrap methods such as
jackknife which is a resampling method. SmartPLS 2.0 can display the t-value as a bootstrapping result
in the path model. in this study if the t-value is larger than the value of t0.05 (14) or t0.025 (14) , it
indicates that the path coefficient is statistically significant.
The hypotheses that the expectations for quality, customization and potential increases positively
influence the perceived quality and value are confirmed by significant path coefficients of 0.420 and
0.178, however, they have a negative effect on the customer satisfaction, with a path coefficient of
-0.117, this means that customers are found to more satisfactory when with less expectations for the
commercial house. Here, a tentative explanation is that the commercial house the estate dealers provided
can’t keep pace with the demands of customers, real estate developers’ excessive publicity may also set
up unrealistic expectations. Thus, hypotheses 1, 2 are supported, and 4 unsupported.
The quality experience, project quality, area, traffic condition, social human environment living
environment and property management services positively influence the perceived value and the
customer satisfaction with path coefficients of 0.407 and 0.610. Hypotheses 3 and 5 are supported.
Tab.3 Results of hypothesis testing γ =path coefficients, t0.05 (14) = 1.7613 t 0.025 (14) = 2.1448
γ
Results
Hypotheses
t -value
Relationship between latent variables
R2
，
（
）
，
H1
Customer expectations → Perceived quality
0.177
0.420
>2.1448
Supported
H2
H3
H4
Customer expectations → Perceived value
Perceived quality → Perceived value
0.258
0.178
0.407
>1.7613
>2.1448
-0.117
>1.7613
Supported
Supported
Unsupport
ed
Supported
Supported
Supported
Customer expectations → Customer satisfaction
0.535
Perceived quality → Customer satisfaction
0.610
>2.1448
H5
Perceived value → Customer satisfaction
0.270
>2.1448
H6
Customer satisfaction → Customer complaints
0.446
-0.668 >2.1448
H7
A significant path coefficient of 0.270 confirms the hypothesized positive effect of perceived value
on the customer satisfaction; hence, hypothesis 6 is supported.
The seventh research hypothesis is supported by the presence of a significant path coefficient of
-0.668, indicating that the customer satisfaction negatively influences the customer complaints. This
result indicates that the immediate consequence of increased customer satisfaction is decreased customer
complaints. It follows Hirschman’s (1970) exist-voice theory, when dissatisfied, customers have the
option of exiting (e.g., going to a competitor) or voicing their complaints in an attempt to receive
retribution. A satisfied customer is more likely to give positive recommendation to the candidate
customers with fewer complaints and will bring more customers to the corporations; it seems to be
reputation and positive word-of-mouth due to improved customer satisfaction that can win more
customers.
The explanatory power of the model was also shown in tab.3. R 2 values show that customer
expectations, perceived quality and value in the real estate market account for 53.5% of variance in
customer satisfaction. In turn, customer satisfaction significantly explains 44.6% of variance in
customer complaints. Customer expectations and perceived quality account for 25.8% of variance in
their perceived value. And customer expectations can explain 17.7% of variance in perceived quality.
Given the high explanatory power of the resulting model, it is likely to predict customer complaints on
customer satisfaction in the real estate market and enhance understanding of customer satisfaction.
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4 Conclusion
PLS path modeling technique for the estimation of structural equation models has mainly two phases:
the first includes the construction of a model, the construction of a questionnaire using this model, the
collection of customer data using this questionnaire, and the specification of the model, the second
includes the estimation and evaluation of the model. In our study, A hierarchy of the influence of the
latent variables on customer satisfaction in the real estate market can be established using structural
model: .Perceived quality; .Perceived value; .Customer expectations, and the results obtained for
all latent variables are satisfactory with great values of Q2 and R 2 .
Real estate investment has the characteristic of high risk because of long construction period, overall
high prices, and few frequency of consumption compared with common products, investments to
improve customer satisfaction are first cost driving, but will, towards the latter part of the time period
examined, significantly and positively impact profitability (Anders et al., 2005), and Customer
satisfaction is one of the key criteria for evaluating the success of the whole business. Real estate
developers should pay more attention to the consumer preference, manage customer needs and provide
high quality products and services, and only in this way can win the heart of consumers. As our above
empirical study and analysis have given some implications, real estate firms are advised to implement
customer satisfaction strategy, establish the customer satisfaction assessment indexes which mainly
consist of factors such as overall housing quality, spatial planning, location, prices, social and natural
environment, services and etc , and set accurate market positioning after identifying and targeting the
potential customers. Corporations that provide customers with customized and high quality commercial
houses can obtain good reputation, achieve social, economic and environmental benefits, and hence
acquire and maintain competitive advantage in this competitive market.
Finally, as to the model, manifest variables should be more accurate and more quantitative. However,
it is still useful for the real estate firms making strategy, and can be referred by other studies.
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The Authors can be contacted from Email: [email protected]. This research is funded by Scientific Research
Fund of Hunan Provincial Education Department (No. 05A046) and the National Fund Program of Social Sciences
(No. 07BJY075).
The author can be contacted from e-mail : [email protected]
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