I1303035666

IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE)
e-ISSN: 2278-1684,p-ISSN: 2320-334X, Volume 13, Issue 3 Ver. III (May- Jun. 2016), PP 56-66
www.iosrjournals.org
DOI: 10.9790/1684-1303035666 www.iosrjournals.org 56 | Page
Integration of GIS and Fuzzy MCDM approach for Real Estate
Investment analysis
Keyur Kapadia1
, Somya Agarwal2
1,2
(Department of civil engineering, BITS-Pilani, India)
Abstract: Real estate is one of the fastest growing sector nowadays in our country. With increasing population,
demand and foreign investments and flexibly in rules and regulations by various organizations e.g. housing
bank loans schemes etc. have further added to its growth rate. Today most of the cities are facing non–uniform
growth. If one wants to invest in real estate then, where to invest? , how much to invest? Are some of the few
questions that comes to our mind at first glimpse. So this project mainly focuses on the growth and point of
investment in any city.Further it includes site selection for new real estate development with uniform growth of
the city. This includes the use of GIS based software for the same which will be followed by decision making tool
for obtaining the optimum location for investment.
Keywords: GIS, Real estate, Investment analysis, Fuzzy AHP, Fuzzy TOPSIS.
I. Introduction
Real estate is the combination of natural and artificial resources at any place. The buying and selling
takes place for the real property takes place to generate income source. It majorly consists of residential and
commercial places. The residential real estate is type of property containing individual or multiple families
available for non-commercial purpose. Similarly, commercial real estate are the type of property which are
available for business purpose to single or multi-occupant. The real estate plays a major role in overall
development of any city. It also plays a major role in generating GDP for the country. In India the real estate
contributes 20.54 % of total GDP of country. It is one of the most growing sector in India. Generating income
through real estate has become a major source of income for many people, for some it is the only source. The
money earned from other industries are sometime invested in real estate for future source of income. The
investment in real estate are in terms of selling, buying, and leasing of residential, commercial or land type
property.
I.1 why investment analysis needed?
As it is one of the highest growing field and higher GDP contributing Field the investment analysis
becomes a major need so that invested money does not become dead investment or its value doesn’t decrease. A
proper investment analysis is needed in order to prevent the above mentioned situation.
The value of real estate property highly depends on the location of the property and the facilities that
are available nearby. The location plays a major role as for a land situated far away from the city its value will
not increase high rate due to less developed area and also no other facilities would be available so people try to
prevent investment at such location. The return on investment may be high at such location but at slow rate. This
type o location may be helpful if money is to be invested for a longer period of time. This shows the importance
of the location in real estate investment.
Many techniques have been developed recently for investment analysis purpose. MCDM tools are
gaining much focus due to its decision based on multiple criteria making it reliable. But it lacks in considering
the location condition and situation as manually surveying the area is a difficult task to be done to collect data.
This situation can be overcome by incorporating the use of Geographic information system for extracting
location based information electronically just by means of Computers. The major similarity in GIS and real
estate is its requirement of location based information for analysis. This study describes the incorporation GIS
and MCDA tool in order to obtain the best location for investment in real estate. The data extracted from GIS is
been used in MCDM tool to reach the decision for real estate investment location. The analysis has been carried
out considering 3 areas of the Surat city that are being developed most recently. The study area is been defined
in detail in the next section.
II. Methodology
The whole project is divided into two parts. First includes use of GIS technology and the second part
includes use of multi-criteria decision making tool to select appropriate site for real estate investment. GIS is
used to find out the fallow land and built-up areas available in the selected three areas of study area. Then the
calculated area is used to find out the growth rate.

Integration of GIS and Fuzzy MCDM approach for Real Estate Investment analysis
MCDM tool is used to analyze all parameter for the three alternatives i.e. three developing areas taken
in this study of the Surat city. Results from the two methods fuzzy AHP and fuzzy TOPSIS are compared.
The overall project work methodology adopted is as below.
Figure 1Methodology
II.1 Study Area
In this study, Surat city of Gujarat is considered due to emerging growth in real estate as study area. It
is one of the top most growing city in the world and is ranked 2nd
as the most developing city. Surat city is also
known as the diamond hub of the world and is the one of the major textile hub of India. Due to both the trades,
the money flow in the city is very high. Recently Surat city and its adjoining areas have started developing to
great extent in terms of infrastructure and overall growth. Real estate investment is one the major ongoing trend
of investment in the city. Continuously growth in the adjoining areas has further added to the same. The
considered study, consists of three newly developing areas for the analysis i.e. Vesu, Pal &Unn. These newly
developing areas are considered as good alternative for investment as the return on investment is high due to
rapid growth rate.
Though these developing areas have high rate return on investment but they to some extent they lack in
basic facilities. For example, Unn has poor public transport facility thereby leading to poor accessibility to the
central business district of city even though investment in real estate is high. These developing areas does not
have high return on investment in current situation but on forecasting future, high rate of return is expected.
Further in the study, areas are considered on the basis of the recent development and fallow land availability.
Second study area of the Surat city taken for study is vesu. It is one of the fastest growing area with
maximum growth rate, high return on investment, maximum number of facilities available (commercial
complex, hospitals, school) in the vicinity of residential areas.
At last, pal located on the opposite side of the river Tapi. It is also the developing area and is
considered as the good option for real estate investment. Though the development in public transport facility is
going on, still it is considered as the good option for investment
II.2 Parameter selection:
Selection of parameters for any study is one of the most important factor as the result may vary a lot on
the basis of the parameters considered.
According to the ideal real estate site, it should have all the lowest values of input and the highest
values of outputs. It is noted inputs and outputs can be spatial criteria as well as economical criteria. Hence
observable criteria, such as facilities available (commercialcomplex,school,hospital) in the vicinity of the
residential areas, growth rate, return on investment, public transport facilities available etc. are used.
Parameters selected for study are:
 Growth rate
 Land value
 Return on investment
 Public transport available
 Basic facilities (market, hospital etc.)
II.3 Data Collection:
Data for different areas is collected by surveying the area, contacting the contractors for land rate, using
Google earth and GIS for calculating the areas of built-up land and fallow land etc.

II.4 GIS (Geographic Information System)
GIS and Real estate can be much related as both are based on location. Real estate investment depends
very much on location due to different parameters such as land use, transport facilities, commercial space
available etc.. for this reason GIS can be successfully incorporated in Real estate investment procedure, as all
the data can be represented by means of map and also the data regarding the above parameters can be easily
extracted. GIS helps reduce the effort of physically visiting the site and analysing the situation of any area.
Instead with help of GIS the data regarding the development at any location can be observed electronically and
can be used for evaluation. The growth rate for any site can be obtained by means of GIS. The effort required
for collecting and analysing data can be narrowed down effectively. Performance of any site regarding the real
estate can be evaluated in terms of growth for the site.
GIS is used in this study for the purpose of digitization of real estate data and the data regarding fallow
land available, total built up area were extracted and used to obtain the growth rate of a particular location in
city. For this purpose GIS based software Esri’s ArcGIS was used to obtain data.
The procedure followed to extract data is as described below.
1. Obtain satellite map for the site required for analysis.
2. Geo-reference the map image using geo-referencing tool in ArcGIS.
3. Further form required polygon shape files.
4. Form polygonal map using by drawing polygon and digitize the map.
5. Obtain the data regarding the area from the polygon formed by area option in attribute table for the shape
file.
II.5 MCDM (Multi-criteria decision making Methods):
MCDM is concerned with structuring the decision and planning problem. It helps in obtaining the
decision through mathematical manipulation in the structured problem. MCDM takes the decision makers
individual criteria priorities and provides the overall ranking or priority.
MCDM is general term used for different techniques used for obtaining priorities in different ways.
Some of the different methods of MCDM includes Weighted sum, PROMTHEE (Preference ranking
organisation method for enrichment of evaluation), ELECTRE (Elimination and choice expecting reality)- I, II,
III, IV, AHP (Analytical Hierarchy process), Fuzzy sets, Multi-attribute analysis, Multi-criteria utility theory,
Cluster analysis, TOPSIS (technique for order of preference by similarity and ideal solution, DEA (Data
Envelope analysis), VIKOR, further may of this techniques has been merged and applied in order to overcome
the limitations and disadvantages of each other.
II.5.1 Fuzzy Analytical Hierarchical Process
The present study includes evaluation of criteria for investment in real estate in the Surat city on the
basis of various factors i.e. facilities available, land rate, transportation, etc by fuzzy AHP method i.e.Buckley
and by the Fuzzy TOPSIS method. Five vital parameters for investment in real estate are considered. The whole
study is decomposed into various hierarchical levels. The topmost level shows selection of the most effective
area for real estate investment in the newly developed area of the city Surat while next level deals with the
criteria for selection and the third level deals with the alternatives.
In this study Buckley’s method of Fuzzy AHP and Fuzzy TOPSIS has been effectively applied for a
case study of selection of area for real estate investment in the Surat City .The proposed methodology of fuzzy
AHP approach using triangular fuzzy numbers which are used to perform the pair-wise comparison among
criteria on the basis of their relative importance and the areas available on the basis of each criteria. Second
methodology includes Fuzzy TOPSIS. Relative weights obtained from the Buckley’s method of criteria-criteria
matrix are used as used in Fuzzy TOPSIS to form weighted fuzzy normalized decision matrix.

II.5.1.1Buckley’s method of Fuzzy AHP:
Normal Fuzzy does not includes pair-wise comparison and hence relative importance of criteria cannot
be found whereas normal AHP does not takes into account the uncertainty associated with the mapping of
human decision into numerical form. But integrated Fuzzy AHP includes pair-wise comparison and the
triangular fuzzy number for mapping of human decision more accurately.
Step by step procedure for Buckley’s method is given below:
1. Weights of the matrix among criteria is determined and the weights of different alternative(areas for
investment) on basis of each criteria are also determined to find out the overall weights for real estate
investment in three different areas of Surat city.
2. The normalized weights for each investment area and criteria are determined by the sum of weights
obtained from Buckley’s method and then dividing each weight from the sum.
Figure 2Hirearchy for Fuzzy AHP
II.5.2 Fuzzy TOPSIS:
In this study, Fuzzy TOPSIS was also used as the merit of using a fuzzy approach to the TOPSIS is to
assign the relative attributes using fuzzy numbers instead of precise numbers for suiting the real world in fuzzy
environment. The method is based on the concept that the chosen alternative should have the shortest distance
from the positive- ideal solution (i.e. minimal gaps in each criterion from ideal situation) and for the negative
ideal solution, criteria has maximum gaps in each criterion.
Step wise procedure is shown below:
1. Using fuzzy AHP to determine weights of criteria
2. Construct the performance matrix among criteria and areas selected for investment.
3. Find normalized decision matrix
4. Find weighted fuzzy normalized decision matrix.
5. Determine the distance from positive ideal solution and negative ideal solution.
6. Obtain degree of satisfaction and degree of gap.
Figure 3 Hierarchy for Fuzzy TOPSIS
Step 1
•Formation of pairwise comparision matrix for criteria and alternatives.
Step2
•Define fuzzy geometric mean and the fuzzy weights of each criterion by hseih.et.al(2004) .
•ri = (ai1 *……..*aij *……*ain )1/n
•wI =ri *(r1 +……. +r i +……+rn )-1
Step 3
•Normalize the fuzzy weights obtained after the fuzzy geometric mean.
•For normalisation summation of the fuzzy weights is obtained and each individual fuzzy weight is divided by the
sum.

Case Study:
The steps described were used to extract data using ArcGIS.
Step wise procedure with example is show below.
STEP 1:
Obtain the satellite image of the study area (in this case Google maps was used). A sample satellite
image file is shown below.
Figure 4satellite image for vesuFigure 5 Geo-referencing using ArcGIS
STEP 2: Geo-referencing:
The Latitude and longitude at 4 points were obtained from the Google maps. It is as described below.
The control points were drawn initially and the control point were connected to the exact point on map
and the maps were geo-referenced. The geo-referenced map image is as shown below for the same.
STEP 3: Create Polygon shape file for Built-up and Fallow land.
After the shape file has been created create polygon for the built up and fallow land area. The image for
the same is shown below for single area.
After successful formation of polygon according to the specified area in map the polygon map,
similarly for other two areas of study area polygon maps were formed. The maps obtained are shown in result.
Fuzzy AHP:
The fuzzy triangular numbers are used to express linguistic variables into numerical form. Further these
triangular numbers reduces uncertainty associated with the human decision mapping. FTN (fuzzy triangular
number) used in this study are shown in table 1.

Table 1Fuzzy linguistic rating variable
Linguistic Variable Triangular Fuzzy Number Triangular Fuzzy Number
Equally important 1 (1,1,3)
Weakly important 3 (1,3,5)
Weakly important 5 (3,5,7)
Very strongly important 7 (5,7,9)
Absolutely important 9 (7,9,9)
Figure 6 Graphical fuzzy representation of Fuzzy number
Response matrices:
Response matrices were formed for the criteria and alterative. The real value matrix formed for criteria
and alternatives are shown in table 2. This values were used to rate the alternatives w.r.t the parameters (table 3).
Also pairwise comparison matrix (table 4) was formed in order to obtain weights of each criteria by means of
Fuzzy AHP.
Table 2 Real value matrix
Vesu Pal Unn
Growth rate 93% 100.00% 75%
Return on investment 42% 40% 50%
Land rate 100000.00 70000.00 50000.00
Facility available 7 5 3
Transportation 5 3 1
Table 3 Alternative to criteria rating
C1 C2 C3 C4 C4
A1 (5,7,9) (7,9,10) (3,5,7) (5,7,9) (3,5,7)
A2 (3,5,7) (7,9,10) (1,3,5) (5,7,9) (1.3.5)
A2 (1,3,5) (3,5,7) (0,1,3) (3,5,7) (7,9,10)
Table 4 Pair-wise comparision matrix
C1 C2 C3 C4 C5
C1 (1,1,1,) (0.2,0.33,1) (0.142,0.2,0.33) (0.11,0.142,0.2) (0.2,0.33,1)
C2 (1,3,5) (1,1,1,) (1,3,5) (3,5,7) (5,7,9)
C3 (3,5,7) (0.2,0.33,1) (1,1,1,) (1,3,5) (1,3,5)
C4 (5,7,9) (0.142,0.2,0.33) (0.2,0.33,1) (1,1,1,) (1,1,3)
C5 (1,3,5) (0.11,0.142,0.2) (0.2,0.33,1) (0.33,1,1) (1,1,1,)
Calculation steps for obtaining weights of each alternatives w.r.t individual criteria are shown.
Alternatives were rated according to the fuzzy scale shown in table 1.
For criterion C1(Facility Available):
Pair-wise comparison matrix of alternatives w.r.t. criteria C1.
Table 5 Pairwise comparison matrix of alternative w.r.t criteria C1
VESU PAL UNN
VESU (1,1,1) (3,5,7) (5,7,9)
PAL (0.142,0.2,0.33) (1,1,1) (3,5,7)
UNN (0.11,0.142,0.2) (0.142,0.2,0.33) (1,1,1)

Geometric mean in row wise pattern (li,mi,ni)
Geometric mean for all the alternatives were obtained as follows.
𝑟i = (𝑎i1 *……..*𝑎ij *……*𝑎in )1/n
Lower bound l1= (1*3*5) ^ (1/3)
Middle bound m1 = (0.142857*1*3) ^ (1/3)
Upper bounds u1 = ((1*7*9) ^ (1/3)
Fuzzy weights for each criterion
𝑤I =𝑟i *(𝑟1 +……. +𝑟i +……+𝑟n )-1
Table 6 Relative fuzzy weight
RELATIVE FUZZY WEIGHTS
A1 (0.431844, 0.71471, 1.146215)
A2 (0.132019, 0.218494, 0.382072)
A3 (0.044006, 0.066796, 0.116803)
Defuzzified weights and normalized weights
To convert the triangular fuzzy number into single crisp value defuzzication is used. Prioritization of
alternatives is done on the basis of normalized weights obtained.
DFVi (Defuzzified value) = (lwi +mwi+uwi)/3
Normalization of weight = DFVi/ 𝐷𝐹𝑉𝑖
Table 7 Final weight matrixw.r.t C1
Defuzzified values Normalized value
0.764256118 0.70482535
0.244195102 0.225205784
0.07586863 0.069968866
Similar procedure for obtaining GM and Relative fuzzy weight were used for all the criteria. Only
pairwise comparison matrix and final defuzzified and normalized weights are shown further for other criteria.
For criteriaC2 (Land Rate)
Table 8 Pair-wise comparison matrix for alternative w.r.t. C2
VESU PAL UNN
VESU (1,1,1) (3,5,7) (5,7,9)
PAL (0.142,0.2,0.33) (1,1,1) (3,5,7)
UNN (0.11.0.142,0.2) (0.142,0.2,0.33) (1,1,1)
De-fuzzification is carried out to convert triangular fuzzy number into single crisp value for further
normalization. De-fuzzified values and normalized values for land rate criteria are shown below in the form of
table
Table 9 final weight matrix w.r.t. C2
DEFUZIFIED VALUES NORMALIZED VALUE
A1 0.764256118 0.70482535
A2 0.244195102 0.225205784
A3 0.07586863 0.069968866
For criterion 3(Transportation)
Accessibility to region is one of the major reasons for development. Better the transportation facility
available better is the growth rate and higher is the development.
Table 10 pairwise comparison matrix for alternative w.r.t. C3
VESU PAL UNN
VESU (1,1,1) (1,3,5) (5,7,9)
PAL (0.2,0.33,1) (1,1,1) (3,5,7)
UNN (0.11,0.142,0.2) (0.142,0.2,0.33) (1,1,1)
De-fuzzified weights are further normalized weights. Ranking is done on the basis of normalized
weights for transportation facility.

Table 11Final weight matrix w.r.t C3
A1 0.736114 0.618208
A2 0.368183 0.30921
A3 0.086424 0.072581
For criterion C4 (Growth Rate)
It is the most important parameter in real estate investment. Fallow land area and the built-up areas
calculated for each alternative by using Arc-GIS is further used to find growth rate.
Table 12 Pairwise comparison matrix of alternative w.r.t. C4
VESU PAL UNN
VESU (1,1,1) (0.11,0.142,0.2) (3,5,7)
PAL (5,7,9) (1,1,1) (7,9,9)
UNN (0.142,0.2,0.333) (0.11,0.11,0.142) (1,1,1)
De-fuzzified weights obtained from the Buckley’s method for criteria growth rate are further
normalized to find out the priority of the alternatives
Table 13Final weight matrix w.r.t. C4
A1 0.185691 0.179381
A2 0.788262 0.761476
A3 0.061224 0.059143
For criterion 5(Return on investment)
Investments in real estate are generally done by foreseeing future scope of return. High rate of return on
investment is one of the main reasons for investing in real estate.
Table 14Pairwise comparison matrix of alternative w.r.t. C5
VESU PAL UNN
VESU (1,1,1) (3,5,7) (0.111,0.142,0.2)
PAL (0.142,0.2,0.333) (1,1,1) (0.111,0.111,0.142)
UNN 5 (7,9,9) (1,1,1)
Evaluation of Pair-wise comparison matrix for criteria
Table 15 Pairwise comparison matrix for Criteria
C1 C2 C3 C4 C5
C1 (1,1,1,) (0.2,0.33,1) (0.142,0.2,0.33) (0.11,0.142,0.2) (0.2,0.33,1)
C2 (1,3,5) (1,1,1,) (1,3,5) (3,5,7) (5,7,9)
C3 (3,5,7) (0.2,0.33,1) (1,1,1,) (1,3,5) (1,3,5)
C4 (5,7,9) (0.142,0.2,0.33) (0.2,0.33,1) (1,1,1,) (1,1,3)
C5 (1,3,5) (0.11,0.142,0.2) (0.2,0.33,1) (0.33,1,1) (1,1,1,)
De-fuzzified weights and the normalized weights obtained from the pair-wise criteria matrix are used to find the
ranking of criteria.
Table 16 Final weight matrix for Criteria
DEFUZZIFIED VALUE NORMALIZED WEIGHTS
C1 0.080526 0.054257
C2 0.647407 0.436212
C3 0.392703 0.264597
C4 0.218236 0.147044
C5 0.145284 0.09789
Fuzzy TOPSIS:
1. In fuzzy TOPSIS pair-wise comparison matrix cannot be used instead, alternative and criteria comparison
matrix was used for evaluation of distance from positive and negative ideal solution on basis of which
degree of satisfaction and gaps were determined which were further used to rate the matrix.

Table 17Alternatice to Criteria Rating Matrix
C1 C2 C3 C4 C4
A1 (5,7,9) (7,9,10) (3,5,7) (5,7,9) (3,5,7)
A2 (3,5,7) (7,9,10) (1,3,5) (5,7,9) (1.3.5)
A2 (1,3,5) (3,5,7) (0,1,3) (3,5,7) (7,9,10)
2. Max upper bound value in the above matrix =10
3. Weights used in fuzzy TOPSIS are from Buckley’s relative weights of pair-wise criteria matrix.
Table 18 Relative fuzzy Weight matrix
Criteria RELATIVE FUZZY WEIGHTS
C1 (0.024613,0.05211,0.1648) 0.05211 0.164856
C2 (0.184689,0.5219,1.235) 0.521965 1.235567
C3 (0.097038, 0.28392 0.797151
C4 0.072812 0.142005 0.439892
C5 0.040286 0.112158 0.283407
4. Weighted fuzzy normalized decision matrix is obtained using the formula
𝑉 = 𝑣𝑖𝑗 𝑛×𝑛
𝑣𝑖𝑗 =𝑟𝑖𝑗 ∗ 𝑤𝑗
𝑟𝑖𝑗 =
𝑙𝑖𝑗
𝑢𝑗
+ ,
𝑚𝑖𝑗
𝑢𝑗
+ ,
𝑢𝑖𝑗
𝑢𝑗
+
For C1 –C1 value of pair-wise comparison matrix
Lower Bounds =(5*0.024613)/10
Similarly, all other values of the weighted normalized decision matrix are calculated.
5. Now distance from ideal point is calculated for both positive and negative distances
Table 19 TOPSIS distance matrix
d+(from ideal) d-(from negative)
0.328640469 1.542274296
0.479720865 1.388897996
0.789184668 1.066693165
6. Now closeness coefficient values are calculated. The smaller the value of positive closeness coefficient i.e.
smaller is gap from positive ideal point and vice- versa for negative closeness coefficient.
Table 20 Closeness coefficient matrix
cc+(gaps) cc-(satisfaction)
0.175658 0.824342
0.256725 0.743275
0.425235 0.574765
Cc+ is calculation as follows:
cc+1 = 0.3286/ (0.328640469+1.542274296)
III. Results And Discussion
The results obtained by following the methodology are shown and discussed in detail.
III.1 GIS RESULTS:
The digitized map obtained by means of GIS for all the three areas are shown below. The below images
shows the digitized map for the year 2014-2015. Similarly the digitized map were formed for the year 2004-
2005.
Figure 7 pal Polygon mapFigure 8 unn Polygon Map

Red color shows the built-up area and green, pink and blue area shows the fallow land available.
The area result obtained by means of GIS is as shown in table below for all the study area taken.
Site Total area Built-up area Fallow land
Vesu 5.778 1.993 2.08
Pal 2.08 0.58 0.99
Unn 2.91 1.079 1.329
III.2 Fuzzy AHP:
The criteria weights and weights of alternatives w.r.t to individual criteria using Fuzzy AHP are shown in table
Criteria weight VESU PAL UNN
C1 0.054257 0.704825 0.225206 0.069969
C2 0.436212 0.704825 0.225206 0.069969
C3 0.264597 0.618208 0.30921 0.072581
C4 0.147044 0.179381 0.761476 0.059143
C5 0.09789 0.179852 0.059222 0.760926
Overall weight for each alternative were obtained by simple weighted sum and the alternatives were ranked
based on overall weight
Vesu Pal Unn
0.553254 0.31004 0.136706
1 2 3
III.4 Fuzzy TOPSIS:
Also the Results obtained by means of Fuzzy TOPSIS are tabulated below. The ranking given were on
the basis of degree of satisfaction value obtained.
Site Degree of satisfaction Rank
Vesu 0.824342 1
Pal 0.743275 2
Unn 0.574765 3
The result obtained on the basis of study describes the best site for investment as vesu in both the cases
(Fuzzy AHP and Fuzzy TOPSIS). This result obtained can be supported as it can be seen observed from the
parameters data that most of the parameters are in favor of the first area. Such as public transport facilities
available is best in vesu while pal and Unn falls below it. Similarly in case of return on investment and growth
rate vesu surpasses the other two alternatives. The only issue was found to be higher investment amount in vesu
due to its higher growth rate.
IV. Conclusion
The paper describes the incorporation of GIS based MCDM approach for real estate investment. GIS
based MCDM is said as input obtained from GIS were further used in MCDM to evaluate the alternatives in
basis of parameters. The GIS provides data from the visual factors while the MCDM techniques provide
solution based on objective approach. The further more detailed analysis can be carried out by selection of
different evaluation parameters which are more location based. The GIS can further be used to extract more
location based data and can be used as input for different MCDM techniques which will help reach the goal. The
paper describes an alternative for real estate investment approach. The result obtained can be verified with real
world situation.

Acknowledgement
This research is the product of many people who helped us throughout the study. We are grateful to
PhD scholar Harish Puppala for their guidance throughout the project.
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