An integrated cost-based approach for real estate appraisals
Jingjuan Guo • Shoubo Xu • Zhuming Bi
Published online: 13 February 2013
� Springer Science+Business Media New York 2013
Abstract Real estate appraisal information systems have
been studied by many researchers in the past including
those systems that have integrated geographic information
systems, artificial neural networks, etc. This paper proposes
a new integrated approach for real estate appraisals which
can be used in real estate appraisal systems to improve
efficiency and accuracy. Motivated by the identified limi-
tations of existing cost approaches for real estate apprais-
als, we integrate some elements from sales comparison
approach and income approach into the cost approach to
improve the accuracy of the valuation of real estate
appropriately. As a result, the new integrated cost-based
approach is capable of taking all of the major factors into
accounts; these factors are closely related to the assets of
real estate in one way or another. In the implementation of
the new approach: (1) the concept of replacements cost is
revisited and expanded to consider dynamic, environ-
mental, and cultural factors in real estate appraisals; (2) the
conventional depreciation values and depreciation rates are
replaced by adjustment values and coefficients to include
both the positive and negative impact on the changes of
real estate value; (3) the theory of technology economics is
applied, six forces have been systematically analyzed to
determine replacement costs; and finally, (4) different
methods for value adjustments, including the algorithm
based on artificial neural network, have been utilized to
deal with the randomness and uncertainties of mass data for
the determination of adjustment values and coefficients.
Keywords Technological economics � Information
management for real estate appraisals � Financial
information systems � Financial information management
1 Introduction
Real estate appraisal is to estimate the value of real estate
based on the highest and best use of the property. Real estate
appraisals play significant roles to the health and soundness
of the world’s financial environment. Appraisals are essential
steps before properties can be transacted. Besides the trans-
actions, public interest in real estate markets and investments
trusts has also been grown rapidly [1]. Real estate appraisals
are extremely important to multiple participators: property
sellers and buyers have the great interests in estimating their
personal assets, municipalities and governments need to
determine the revenues which are largely depended on real
estate taxes, the financial institutions need to make their
banking policies and grant mortgage loans with the mini-
mized risks, and properties brokerages need to evaluate real
estate properties to help their clients make judicious deci-
sions [15]. Since the real estate is usually the significant
assets to most of people, undervalued or overvalued real
estates would cause an irreversible loss to owners or buyers.
Besides, since the purposes of appraisals are to estimate the
property’s equity, an over-valuation results in the under-
estimation of the default risk, which can be passed to buyers
or secondary mortgage providers.
It is critical to select an appropriate approach for real
estate appraisals. The statistics has shown that real estates
J. Guo (&) � S. Xu
School of Economics and Management, Beijing Jiaotong
University, Beijing 100044, China
e-mail: [email protected]
S. Xu
e-mail: [email protected]
Z. Bi
Department of Engineering, Indiana University Purdue
University Fort Wayne, Fort Wayne, IN 46805, USA
123
Inf Technol Manag (2014) 15:131–139
DOI 10.1007/s10799-012-0152-7
can be easily undervalued or overvalued. For example,
Cannon and Cole [4] indicated that real estate appraisals
have been over 12 % lower or higher than the subsequent
sales prices; the conclusion was drawn based on the
National Council of Real Estate Investment Fiduciaries
(NCREIF) National Property Index in 1984–2010, which
consisted of two up and down market cycles. It had shown
that appraisals are differed from true values to real estate
significantly; in particular, an appraisal was too low when
the market was hot while too high when the market is cold.
Poor appraisals from inappropriate approaches or inexpe-
rienced appraisers would obviously delay or miss the
opportunities for sales and transactions. The appraisal
errors are actually systematic errors which can be remedied
by improving an appraisal approach and taking into all of
the major factors related to properties’ assets.
Three common types of real estate appraisals are cost
approach, sales comparison approach, and income capi-
talization approach [11]. Since the cost, sales, or incomes
related to the properties vary significantly from one place
to another and from time to time, at a specific location and
a specific time; an experienced appraiser should be able to
select the right appraisal approach for a specific property.
When the regional transactions are inactive or real estate
markets are immature, neither sales comparison approach
or income capitalization approach is applicable due to
the lack of historical transactions or income benchmarks.
In this situation, a cost approach is recognized as the most
appropriate approach for real estate appraisals. A cost
approach is established based on the fact that the value of a
property can be determined by summating the land value
and the depreciated value of further improvements made on
the property.
Real estate appraisals are challenging tasks that require
intensive efforts; the study in developing and enhancing
various appraisal approaches has been very active research
field. For examples, Gonzalez and Laureano-Ortiz [11]
argued that a real estate appraisal resembled to the psy-
chological process humans follow in utilizing their past
experiences to solve new problems; therefore, they pro-
posed to use case-based reasoning in valuating real estate.
Note that most popular methods are driven by sales market
data; in particular, for the automated software tools of
property appraisals. In applying these methods, it is com-
mon that comparable properties are similar to the subject
property; in this case, the value adjustments must be made
to deal with the differences. Narula et al. [20] modeled the
real estate appraisals as a multiple linear regression model
for optimization. It has been found difficult to define proper
predictor variables for real estate appraisals; and the ridge
regression technique has been improved by a combination
with genetic algorithm to estimate the values of real estate
appropriately. Ann et al. [1] suggested that cost is not
always the good source of adjustments. To address the
forecasting problems of real estate appraisals, artificial
intelligence and multiple linear regressions were applied to
valuate residential property. In comparing different
investment options in the real estate market; Seck [22]
discussed the substitutability of securitized real estate asset
and appraisal-based real estate assets. The empirical data
showed that the prices for the securitized assets, such as
real estate investment trusts or stock price index of home
building industry, could be changed randomly while the
prices of the appraisal-based assets can be more likely
predicted. Diaz and Hansz [7] proposed a taxonomic
approach to consider the impact of incentives and pressures
for real estate to provide favorable valuations. Lins et al.
[18] proposed a new approach called data envelopment
analysis to estimate the range of values for real estate.
It had shown some advantages in comparison with con-
ventional regression analysis methods which are commonly
used in real estate appraisals. Shiller and Weiss [23] pro-
posed a framework to compare various valuation systems
for real estate appraisals. Since the framework was devel-
oped for mortgage lenders, the best interest was confined to
the maximized benefits of mortgage lenders. Isakson [14]
developed a multiple regression analysis method to verify
the appraisal results of real estate; it was based on the
simplifications that the covariance between the adjusted
sale price of comparable properties and the characteristics
where adjustments are properly made is negligible for a
specific property. Traditional real estate appraisal approa-
ches have been found inefficient and not correct enough.
In financial information management systems, real
estate appraisal information systems have been studied by
many researchers in the past [6, 12, 21, 27, 38]. For
example, Liu et al. [19] developed an appraisal system
while the geographic information system was used as one
source of the real estate information, and artificial neural
network was applied to improve the reasoning process. The
system was implemented in the Matlab programming
environment and the result had shown the improved effi-
ciency and accuracy of system.
In this paper, the solution to the limitation of a cost
approach will be focused. In real estate appraisal, the cost
approach is suitable for the immature markets and the sit-
uations where sales comparison approach or income
approach are inapplicable due to the lack of transactions.
Besides, cost approach can also be expanded and applied in
the insurance valuations and damage claims. The underling
idea of a cost approach is to estimate the assets of real
estate by deducting the value loss from the replacement
cost. Obviously, conventional cost approaches do not take
into accounts of dynamic, cultural, or positive market-
demanding factors. These drawbacks affect the accuracies
of real estate appraisals. In most cases, the estimated values
132 Inf Technol Manag (2014) 15:131–139
123
are much lower than the market values of real estate. The
recent progress on cost approaches have been limited to
perfect modeling processes and take advantages of modern
mathematic tools in real estate appraisals. For example, the
fuzz set theory is applied in selecting transaction cases and
determining adjustment factors; in addition, the multiple
regression method, Markov chain prediction, grey system
theory prediction model and artificial neural network are
also proposed for forecasting the changes of properties’
values [3, 9, 16, 17, 26, 39, 40]. However, the investiga-
tions on the root causes of appraisal discrepancies are
spare.
In applying a cost-based approach, we have observed
that no consideration of dynamic, environmental, and cul-
tural factors is the major cause to the inaccuracies of real
estate appraisal. Therefore, we propose to integrate some
major components from sale comparison approach and
income capitalization approach for the value adjustment of
appraisals. The main purpose is to apply our proposed
method to the development of a more effective real estate
appraisal information system. The rest of the paper is
organized as follow. In Sect. 2, the replacement costs are
revisited to take more significant factors into account; in
Sect. 3, the limitation of conventional cost approaches are
discussed; in Sect. 4, the new methodology has been pro-
posed to adjust the costs based on valuation adjustments
and valuation coefficients; in Sect. 5, the integration of cost
approach with the income capitalized and sales comparison
approach is introduced; in particular, back-propagation
(BP) artificial neural network (ANN) has been applied to
quantify the subjective data in real estate appraisals;
finally, the reported work and new contribution have been
summarized.
2 Replacement cost: revisit and analysis
Replacement cost in a cost approach refers to a summation
of all of the necessary costs, payable taxes and anticipated
profit to acquire or rebuild the new real estate project
equivalent to the subject property. A replacement cost
represents a typical cost of a specific real estate, which can
be valued as the amount of cost to construct this property
under a given market environment; therefore, it is also
called fair cost [31, 37]. Based on the principle of substi-
tution, the value of an existing property can be measured
by the cost of constructing a substitution with the same
utilities within the property [5]. From this point of view, a
replacement cost also reflects the cost of an equivalent
property with the same utilities of the subject property. The
replacement cost can be different from the value of the
same property based on the highest and best use. In other
words, real property with the same replacement cost can be
of different use. It is unnecessary to correlate replacement
cost to functions, utilities and the ways to make profits.
Traditionally, the replacement cost in a cost approach
focuses on the long-term cost with a hidden assumption of
an equilibrium of market in a long period of time. The
replacement cost under this assumption represents the
construction cost at the specified market environment when
the appraisal is conducted; however, it does not take into
consideration of other important factors such as those
caused by the relations between supplies and demands.
Based on the aforementioned discussion, it is our
observation that traditional replacement cost in a cost
approach has a narrow meaning which covers only the part
of the market price of the real estate. To enhance real estate
appraisals, we propose to integrate some major components
from sales comparison approach and income capitalization
approach in assessing the replacement cost. The replace-
ment cost will be revised to include (1) the cost with the
best functions and utilities, (2) some invisible costs which
cannot be represented by the price under the highest and
best use, and (3) the adjustment cost reflecting the influ-
ences of the relations of supplies and demands when the
appraisal is performed. As a result, the revised replacement
cost will be an average market price of the subject property
with the given functions and utilities in a market at long-
term equilibrium.
3 Limitations of traditional cost approaches
In this section, the discussion is focused on the limitations
of traditional cost approaches in dealing with the adjust-
ment of price factors.
3.1 Influence of price factors
The market value of a real estate property can be deter-
mined based on the principle of substitutions; the major
factors under consideration include the market environment
and its fluctuation, the relations of supplies and demands,
surrounding economic atmosphere, and income capitali-
zation. Most of the researchers have emphasized on the
influence of external factors on the value of real estate, and
the significant external factors are material, economic,
social and governmental factors. Based on the theory of
technology economics, the evaluation criteria of technol-
ogy proposals are eight elements, i.e., politics, defense,
society, culture, technology, economy, environment and
natural resources [2]. In applying the theory of technology
economics for real estate appraisals, the influence of poli-
tics and defense can be integrated as the government factor,
and the influence of technology, environment, and natural
resource can be integrated as the material factor [24, 25,
Inf Technol Manag (2014) 15:131–139 133
123
30]. However, the culture factor has to be taken into con-
sideration individually. In modern society, culture and
civilization has been treated as a comprehensive indicator
of the cohesion and creativity for a nation to compete with
others and gain its strength over time. Therefore, the
evaluation of the cultural factor should be distinguished
from those of other factors. In real estate appraisals, it
becomes necessary to consider the cultural factor as an
independent adjustment factor. As a result, the price
adjustment of real estate should be determined by material,
economic, social, governmental, and cultural factors.
3.2 Important cost factors yet considered
In a cost approach, the replacement cost is mainly land cost
and construction cost, and the other costs are considered by
applying the value depreciation, which is called as loss or
devaluation alternatively.
Depreciation can be only applied to the situation where
the factors bring an adverse effect on the value of real
estate. As a consequence, traditional cost approaches are
not able to take into account of the positive influence on
real estate asset from various factors. More specifically, (1)
invisible capitals relevant to nonmonetary factors cannot be
directly included in replacement cost. It brings the diffi-
culties in reflecting historical and cultural assets of ancient
buildings, cultural and tourism value of real estate prop-
erty, and the value of the brand property in the replacement
cost. (2) The favorable assets brought by better functional
designs of the subject property cannot be easily considered;
since existing cost approaches focus on the adjustment of
depreciation corresponding to the adverse factors; while
a property with better functions and utilities cannot be
adjusted as functional increments. (3) The change of the
environment has either positive or negative impact on
the real estate assets. Again, existing approaches ignore the
real estate appreciation led by the improvement in envi-
ronmental conditions. Note that the adverse impact on the
surrounding environment can be treated as a devaluation
factor related to a functional loss, while the increased
utilities and investment benefits from the improvement of
environment have to be taken into consideration separately.
As a summary, only the devaluations are considered in
existing cost approaches, some positive impacts, such as
the situation when the market demands exceed supplies,
should also be considered in real estate appraisals to reflect
the value fluctuations due to non-equilibrium markets.
4 Proposed improvement of cost approach
In calculating the replacement cost in dynamic and turbu-
lent environment; it becomes necessary to take into account
both of positive and negative factors. Both sides of factors
are reflected in the revised replacement cost of real estate.
In existing cost approaches, there are three adjustment
factors in determining the replacement cost: materials,
functions and economical depreciation. Based on the dis-
cussion in Sect. 3.2, the culture factor has the contribution
to real estate asset since it becomes more and more
important to our society. Therefore, the culture factor is
considered as the fourth adjustment factor. Since the values
of the construction and the land are assessed separately, it
is necessary to categorize material, functional, economical
and cultural factors under the catalogues of the construc-
tion value and the land value. At the end, the value
adjustment factors of real estate should be illustrated in
Fig. 1. Correspondingly, the value of the subject property
is estimated by Eqs. (1) and (2). Note that individual
adjustment value or adjustment coefficient in Eqs. (1) or
(2) can be positive or negative.
V ¼ Vl þ Vb þ Vm þ Vf þ Ve þ Vc ð1Þ
After the classifying the adjustments of values into the
types of land value and construction value, respectively,
V ¼ Vlð1þ Rml þ Rfl þ Rel þ RclÞ þ Vbð1þ Rmb þ Rfb
þ Reb þ RcbÞð2Þ
where V the appraisal value of real estate, Vl the replacement
cost of land, Vb the replacement cost of construction, Vm, Rml,
Rmb material value adjustment, physical value adjustment
coefficient of land and construction, Vf, Rfl, Rfb functional
value adjustment, functional value adjustment coefficient of
land and construction, Ve, Rel, Reb economic value adjustment,
economic value adjustment coefficient of land and construc-
tion, and Vc, Rcl, Rcb Cultural value adjustment, cultural value
adjustment coefficient of land and construction.
4.1 Calculation of replacement cost
Based on the theory of technology economics, six basic
forces are used as evaluation criteria to assess technology
proposal [33–36]. To refine the replacement cost, the
resources for labor and production have been decomposed
into six types: labor, capital, material, resources, transit and
time. To construct a real estate, the labor cost is the sum-
mation of the manpower consumption of designers, con-
structors and managers during the real estate project, which
can be represented by wages. The capital cost relates to the
occupation of facilities and constructions, which can be
described by the deprecation expense and overhaul spends.
The material cost includes the use of the raw materials,
components and parts, which takes into account of the cost
of all material consumptions. The transit cost occurs during
the service of the human flow, the material flow and
134 Inf Technol Manag (2014) 15:131–139
123
information flow, which represents the cost in logistics.
The nature resource cost mainly refers to the land occu-
pation, which is equivalent to the monetary compensation
of the land expropriation, demolition, land transferring fees
and expenditure on environmental impact. The time-related
cost is the time taken during the real-estate development,
which is valued in terms of capital cost [28, 29].
Due to the variety and abundance of the resources
involved in the development of real estate, the costs on six
forces are further decomposed into land-related costs and
construction-related costs. The land-related costs are
calculated based on the level of compensation for land
expropriation and demolition at the time of appraisal; while
the labor, capital, material and transit resources costs of the
constructions are divided into direct costs and indirect
expenses. The direct costs can be determined by taking the
references of similar real estate projects. The indirect
expenses are primarily management fees and sales
expenses. Indirect expenses can be reasonably estimated
based on the direct costs with the adjustment of cost
coefficients. The capital cost is calculated over the entire
construction period as the total investment the project
needs. The development taxes are straightforward and can
be estimated by following the related regulations of
country and area. Besides, the income capitalization can be
estimated by the industry average profit margin. At the end,
the replacement cost can be calculated by
V ¼ ðCh þ Ca þ Cm þ Cn þ Ctr þ CtÞ þ P
¼ ðCl þ Ccl þ PlÞþ ½ðCh þ Ca þ Cm þ CtrÞð1þ roÞ þ Ccb þ Pb�
ð3Þ
where Cl land acquiring and developing cost, Ccl capital
cost of land developing, Pl land developing profits,
Ch human resource consumption, Ca capital resource con-
sumption, Cm material resource consumption, Cn nature
PhysicalValue
AdjustmentFactors
FunctionalValue
AdjustmentFactors
EconomicValue
AdjustmentFactors
Design Life Span
Construction Quality
Maintenance Conditions
Function Layout
Building Equipment
Decoration and Fitment
Historial Value
Brand and Art Value
Laws and Regulations
Interest Rate
Price Level
Market Supply and Demand
culturalValue
AdjustmentFactors
Land
Building
Land
Building
Land
Building
Land
Building
Land Development Status
Safety of Structure
Land Natural Conditions
Nature of Use
Population and SocialDevelopment
Fashion and Preferences
Economic GeographicalCondition
City and CommunityEnvironment
Culture Value
ValueAdjustmentFactors forReal EstateAppraisal
Fig. 1 Adjustment factors in
real estate appraisals
Inf Technol Manag (2014) 15:131–139 135
123
resource consumption, Ctr transit resource consumption, Ct
time resource consumption, ro overhead rate, Ccb capital
cost of house-building, and Pb profit of house-building.
4.2 Value adjustments and adjustment coefficients
To calculate value adjustments and adjustment coefficients,
the multi-level comprehensive evaluation method in the
theory of technological economics is applied [29].
First, real estate adjustment factors in Fig. 1 are evaluated.
Each factor has been decomposed layer by layer until the
details are sufficient to qualify the corresponding cost. As a
result, the comprehensive evaluation has been divided into
three levels [13]: the first level is for the comprehensive
adjustment coefficients of land or construction, the second
one is for the comprehensive adjustment coefficients which
include material, function, economics and culture, and the
lowest level includes the specific indicators which have some
impacts on the values of land and construction.
Weights are assigned for each factor to aggregate all of the
costs involved in the hierarchical structure. Firstly, begin-
ning with the lowest level, according to the characteristics of
each factor, use a scoring method or indexing method to
define a quantified value to take into account the contribution
of this factor to a higher level of a comprehensive factor.
Secondly, in determining the weights of cost factors, the
relative importance between two factors is specified by the
experts, and the judgment matrices could be constructed
through comparing about their relative importance. Thirdly,
the value adjustment coefficients at the middle level are
calculated with the least square method with the multipli-
cation of the satisfaction coefficients and the importance
coefficients. Following the similar steps from the bottom to
top levels, the values for land and construction can be cal-
culated from the comprehensive adjustment coefficients.
There is no doubt that the implementation of above method
relies on available knowledge, experience and the under-
standing of the real estate appraisers. For example, the scores
are determined by the subjective judgments and ratings from
the valuators. Due to the uncertainties how the factors can
affect the value of the real estate, a systematic method is
helpful to reflect the degree of influence on these cost factors
quantifiably. Therefore, some widely used approaches, such
as the back propagation (BP) artificial neural network (ANN)
model, the income capitalization approach, sales comparison
approach, have be integrated into our cost approach to deal
with the uncertainties and dynamics.
4.2.1 ANN-based income capitalization approach
For a real estate yielding an income, its cost factors will
be eventually reflected by the sale (vacancy) rate and/or
its rental price; therefore, the impact of each cost factor
can be measured by the corresponding change of the
vacancy rate, rental price, and the operating cost to sus-
tain the impact. Do and Grudnitski [8] proposed to use an
ANN method for real estate appraisals, it has been widely
used for the classification, clustering, and forecasting. By
applying an ANN-based method, the impacts of the cost
factors on the new income of the real estate are predicted
by using massive historical data of the property or other
similar properties. In the process of evaluating, firstly,
four major cost factors are normalized based on the
accumulated data from the past, i.e., the statistics
approach is used on the data of similar projects and expert
knowledge to convert data into quantified numbers
between [0, 1]. The standard feature factors are then set to
the qualitative factors. The available data of cost factors
and the standard feature factors are compared to trans-
form qualitative factors into the relative value between [0,
1] based on the degree of similarity. In applying BP
neural network, the values of cost factors are set as the
inputs, and the annual income is set as only output. The
information included in data samples can be automati-
cally retrieved by the network and stored as network
weights. Through self-adaption and self-organization, the
neural network is capable of memorizing, recalling, and
imagining the information from the data samples about
the cost factors [10].
In applying the BP-ANN, the eigenvalue of the real
estate is input, and the annual income is predicted.
Meanwhile, the predicted annual income is compared
with the average annual earning, and the difference is the
capitalized income to measure the adjustment value. This
method can be used to estimate the value change caused
by multiple factors or a single factor. For the value
change caused by multiple factors, the comprehensive
adjustment value could be gained by capitalizing the
difference between the predicted annual income and that
of an alternative real estate. For the value change aroused
from a single factor, the value adjustment can be capi-
talization of the discrepancy of the annual income caused
by unique material, functional, economic or cultural
factor. Value change can be calculated in Eqs. (4) and (5),
respectively. When the value adjustments Da, Db, Dc,
Dd are positive, the corresponding cost factors influences
the real estate values positively; otherwise, the influence
will be the depreciation.
Vt ¼ 1� xð ÞXn1
t¼1
Dat
ð1þ iÞtþXn2
t¼1
Dbt
ð1þ iÞtþXn3
t¼1
Dct
ð1þ iÞtþXn4
t¼1
Ddt
ð1þ iÞt
!
ð4Þ
136 Inf Technol Manag (2014) 15:131–139
123
Rt ¼ð1� xÞ
V0
Xn1
t¼1
Dat
ð1þ iÞtþXn2
t¼1
Dbt
ð1þ iÞtþXn3
t¼1
Dct
ð1þ iÞtþXn4
t¼1
Ddt
ð1þ iÞt
!
ð5Þ
where VT, RT comprehensive adjustment value and coeffi-
cient, n1, n2, n3, n4 income period influenced by material,
functional, economic and cultural factors, Da, Db, Dc,
Dd the discrepancy of annual incomes between the subject
property and other properties caused by material, func-
tional, economic and cultural factors, i capitalization rate,
and x income tax rate.
An income capitalization approach can also be used to
calculate the value adjustments of construction when the
cost factors have an impact on the income in the period of
construction. Eqs. (6) and (7) are used for the calculation.
When material, functional, economic and cultural factors
lead to the increase of surplus service life, at, bt, ct, dt are
positive; otherwise, these variables are negative when the
factors lead to decrease.
Vt ¼ 1� xð ÞXDn1
t¼1
at
ð1þ iÞtþXDn2
t¼1
bt
ð1þ iÞtþXDn3
t¼1
ct
ð1þ iÞtþXDn4
t¼1
dt
ð1þ iÞt
!
ð6Þ
or
Rt ¼ð1� xÞ
V0
XDn1
t¼1
at
ð1þ iÞtþXDn2
t¼1
bt
ð1þ iÞtþXDn3
t¼1
ct
ð1þ iÞtþXDn4
t¼1
dt
ð1þ iÞt
!
ð7Þ
where at, bt, ct, dt average market income determined by
material, functional, economic and cultural factors in future
t year, and Dn1, Dn2, Dn3, Dn4 increased or decreased of
remaining useful life of the property affected by material,
functional, economic and cultural factors.
4.2.2 ANN-based sales comparison approach
As shown in Fig. 1, although the land cost is the main
component value of the real estate, the other factors,
especially the material, functional, and cultural factors
have their impacts on real estate majorly through con-
struction. In a cost-based approach, the land cost can be
estimated from the land market information. Therefore, the
challenge of the evaluation lies in the value adjustment of
construction. However, the value adjustment of construc-
tion can be extracted by using the sales comparison
approach. Since the value loss is determined by the sellers
and the buyers in the market, the common cost that the
constructions have in their sales prices are analyzed in
massive information sales data, and then this common cost
is compared with that of the subject property, the differ-
ence is the value adjustment of construction. This approach
is suitable for the real estate appraisal when the comparable
transactions are similar to the construction. However, the
land conditions are quite different, or the sale cases and
the estimated properties are not in the same region. One
has to follow the procedure as below to determine value
adjustment,
1. Estimate the replacement cost for each comparable
transaction;
2. If possible, use the market sales data of the virgin
space as an estimation of the land value of each
comparable transaction;
3. Deduct the land value from a selling price to retrieve
the construction cost of each comparable transaction.
4. The discrepancy between the replacement cost and the
value of the real estate will be the adjustment value.
By comparing the adjustment value with the corre-
sponding replacement cost, the value adjustment
coefficient can be obtained. The resulted calculation
equations are shown in Eqs. (8) and (9). When
P \ (Vb ? Pl), the factor will produce the positive
influence to the building; otherwise, the influence will
be embodied as loss.
VT ¼ Vb � Pb; Pb ¼ P� Pl;
and
VT ¼ Vb � ðP� PlÞ ¼ ðVb þ PlÞ � P ð8Þ
or
RT ¼ðVb þ PlÞ � P
Vb
ð9Þ
where P sale price of the real estate, Pb the construction
unit value, and Pl the land unit value.
Note that the similar constructions are not necessary
having the same value adjustment. For the generalization,
one can still use the ANN-based approach to predict the
effect of construction’s value adjustment caused from dif-
ferent factors. The value influences of material, functional,
economic and cultural factors do not need to be distin-
guished. Therefore, one can choose main factors influenc-
ing the construction cost as the inputs. The value
adjustment of construction (Vb ? Pl - P) of the actual
sold transaction will be the only output. Reasonable sim-
ulation results can be obtained by training the test data.
When the test data is with a satisfactory accuracy, it can be
used in the valuation to acquire the adjustment value Vt0.
Inf Technol Manag (2014) 15:131–139 137
123
Because the sold transactions were collected from dif-
ferent marketing transaction conditions, and took place in
different times, it’s necessary to further adjust Vt0 as:
Vt ¼ V 0t � f1 � f2 ¼ V 0t �100
sd
� piv
pifð10Þ
where f1 the adjustment coefficient of transaction condi-
tion, f2 the adjustment coefficient of transaction time, sd the
scoring of the comparable sold case, piv the price index of
the appraisal time, and pif the price index of the compa-
rable sold case.
5 Summary and future work
Traditional cost approaches estimate the replacement cost
of real estate mainly based on the costs on land and con-
struction with an assumption of the long-term stable mar-
ket; the definition of the replacement cost is narrow and the
calculation overlooks the dynamic, environmental and
cultural factors which cause the discrepancy between the
appraisal and the true market value of real estate. In this
paper, both of positive and negative factors related to the
value adjustments are considered, in particular, special
attentions have been paid on no-monetary or invisible
factors such as environmental and cultural factors and the
relations of supplies and demands. The new cost-based
approach has been proposed. The value depreciation and
depreciation rate have been substituted by the value
adjustments and adjustment coefficients. This is a break-
through to advance the theory of cost approach.
In implementing the proposed cost-based approach, it
has been integrated seamlessly with income capitalization
approach and sales comparison approach: income capital-
ization approach is utilized to determine the land related
value adjustments and sale comparison approach is utilized
to determine the construction related value adjustments.
Artificial neural networks are used in both cases to deal
with dynamics, uncertainties, as well as the subjective
knowledge from experts. Major cost factors have been
divided into material, functional, economic and cultural
factors; these factors are further assessed under the cata-
logues of land value and construction value, respectively.
The replacement cost has been evaluated by qualifying the
contributions from six basic forces of a technology pro-
posal under the theory of technology economics. The new
method has theoretically supported the innovative idea on
improving the appraisal accuracy with the consideration of
dynamic, environmental, cultural factors. Besides, it has
firstly integrated sales comparison and income capitaliza-
tion approaches in the cost-based approach. Since ANN-
based methods have been inventively applied to determine
the adjustment coefficients and values, the implementation
of new cost approach requires high-performance computers
to deal with massive market data, which may increase
complexity of the evaluating process. Therefore, our future
research effort in this field will be focused on (1) the
exploration and refinement of cost factors in detail levels to
further increase the accuracy of estimated value of real
estate; (2) the adaption of parallel computing to improve
the efficiency for the determination of the adjustment
coefficients and values; and (3) the development of a
commercial software for real estate appraisals [3],
Acknowledgments This project was partially supported by the
NSFC (National Natural Science Foundation of China) Grant
71132008 and the Changjiang Scholar Program of the Ministry of
Education of China.
References
1. Ann J, Byun H, Oh K, Kim T (2012) Using ridge regression with
genetic algorithm to enhance real estate appraisal forecasting.
Expert Syst Appl 39:8369–8379
2. Betts R, Ely Silas (2000) Basic real estate appraisal, 5th edn,
South-Western
3. Bi X, Su W, Wang L (2012) An analysis on the macroscopic
growth process and stage of information systems development in
Chinese enterprises. Inf Technol Manage 13(4):273–280
4. Cannon S, Cole R (2011) How accurate are commercial real
estate appraisals: evidence from 25 years of NCREIF sales data.
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1824807
5. Cao J (1997) Theory and method on modern real estate valuation,
Publishing House of Zhongshan University, Guangzhou, ISBN:
306012797 and 9787306012791
6. Chen X, Fang Y (2012) Enterprise systems in financial sector—
an application in precious metal trading forecasting. Enterp Inf
Syst. doi:10.1080/17517575.2012.698022
7. Diaz J, Hansz J (2010) A taxonomic field investigation into
induced bias in residential real estate appraisals. Int J Strateg Prop
Manage 14(1):3–17
8. Do A, Grudnitski G (1992) A neural network approach to resi-
dential property appraisal. Real Estate Apprais 58(3):38–45
9. Duan L, Xu L (2012) Business intelligence for enterprise sys-
tems: a survey. IEEE Trans Industr Inf 8(3):679–687
10. Gao Z, Li Z (2008) System engineering. Northwestern Poly-
technic University Press, Xian
11. Gonzalez A, Laureano-Ortiz R (1992) A case-based reasoning
approach to real estate property appraisal. Expert Syst Appl 4(2):
229–246
12. Guo J, Gong Z (2011) Measuring virtual wealth in virtual worlds.
Inf Technol Manage 12(2):121–135
13. Huang S (2006) The research of historical buildings evaluation
index system. Ph.D. Thesis, Tongji University
14. Isakson H (1998) The review of real estate appraisals using
multiple regression analysis. J Real Estate Res 15(1/2):177–190
15. Kettani O, Oral M, Siskos Y (1998) A multiple criteria analysis
model for real estate evaluation. J Global Optim 12(2):197–214
16. Li L (2011) Introduction: advances in e-business engineering. Inf
Technol Manage 12(2):49–50
17. Lin Y, Duan X, Zhao C, Xu L (2012) Systems science method-
ological approaches. CRC Press, Boca Raton, FL
18. Lins M et al (2005) Real estate appraisals: a double perspective
data environment analysis approach. Ann Oper Res 138(1):79–96
138 Inf Technol Manag (2014) 15:131–139
123
19. Liu X, Deng Z, Wang T (2011) Real estate appraisal system
based on GIS and BP neural network. Trans Nonferrous Met Soc
China 21:s626–s630
20. Narula S, Wellington J, Lewis S (2012) Valuating residential real
estate using parametric programming. Eur J Oper Res 217(1):
120–128
21. Qian Y, Jin B, Fang W (2011) Heuristic algorithms for effective
borker deployment. Inf Technol Manage 12(2):55–66
22. Seck D (1996) The substitutability of real estate assets. Real
Estate Econ 24(1):75–95
23. Shiller R, Weiss A (1999) Evaluating real estate valuation sys-
tems. J Real Estate Financ Econ 18(2):147–161
24. Song B, Xu S (2009) The theory of material flow substance. Syst
Res Behav Sci 26(2):251–258
25. Tian Y, Xu S, Wang L, Chaudhry P (2009) On relationships
between material flow and economic development in an eco-
nomic–material flow system. Syst Res Behav Sci 26(2):259–267
26. Wang P, Xu L, Zhou S, Fan Z, Li Y, Feng S (2010) Novel
Bayesian learning method for information aggregation in modular
neural networks. Expert Syst Appl 37(2):1071–1074
27. Xia Y, Su W, Lau R, Liu Y (2011) Discovering latent commercial
networks from online financial news articles. Enterp Inf Syst. doi:
10.1080/17517575.2011.621093
28. Xiong Y, Guo J, Zhou F, Xu S (2007) Theory of construction
project optimization resources scheme. J Beijing Jiaotong Univ
(Soc Sci Ed) 6(2):18–22
29. Xiong Y (2006) Theory and methodology of construction project
resource allocation optimization. Ph.D. Thesis, Beijing Jiaotong
University
30. Xu L, Swanson G, Samuelson K (2009) Systems science and
enterprise integration, technological economics and the theory of
material flow. Syst Res Behav Sci 26(2):123–127
31. Xu S (1988) The theory of technological economics. Jiangsu
People’s Publishing House, Nanjing. ISBN 7-214-204-300
32. Xu S (2006) Theory of six forces on factors of production.
J Beijing Jiaotong Univ 5(3):15–20
33. Xu S (2009) Theory of six forces of essential factors of pro-
duction. Syst Res Behav Sci 26(2):211–218
34. Xu S (2009) Doubling guarantees quadrupling—theory and
practice. Syst Res Behav Sci 26(2):225–234
35. Xu S (2009) A theoretical study on commodity material flow.
Syst Res Behav Sci 26(2):235–249
36. Xu S (2011) Study on comprehensive appraisement theory of tech-
nology program. J Beijing Jiaotong Univ (Soc Sci Ed) 10(1):36–39
37. Xu S (2012) Technological economics, Economic Science Press,
Beijing, ISBN:978-7-5141-0908-5
38. Xu S, Xu L, Basl J (2012) Introduction: advances in e-business
engineering. Inf Technol Manage 13(4):201–204
39. Zeng L, Li L, Duan L (2012) Business intelligence in enterprise
computing environment. Inf Technol Manage 13(4):297–310
40. Zhou S, Xu L (2001) A new type of recurrent fuzzy neural network for
modeling dynamic systems. Knowl Based Syst 14(5–6):243–251
Inf Technol Manag (2014) 15:131–139 139
123
