”Computer Simulation for Real Estate Price Environment” focuses on the price determination of real estate in Mumbai. The project recognizes and quantifies factors that play a crucial role in the final determination of the price of real estate. Major effort lies in recognizing and evaluating non-quantifiable factors like location, local infrastructure, and connectivity, which impact pricing even though they cannot be valued directly in monetary terms. These factors along with the samples of the real estate prices in Mumbai are used to develop a mathematical model that would give accurate predictions of the prices. Finally, this model would be employed to simulate the real world real estate environment, which would enable the buyer as well as the developer to study the market under different scenarios and make intelligent decisions. Also, the noticeable factor in this situation is that the description tends to assume pattern recognition problem, and therefore neural networks with back propagation will be used for implementation. The system shall be trained based on the history in form of data collected for which errors can also be minimized to achieve results with less deviation.

1. Simulation of Real Estate Price Environment
Somil Kadakia, Satyajit Salyankar, Sohin Shah, Abhijit Joshi
D.J.Sanghvi College of Engineering
Information Technology department
ksomil87@gmail.com
satyajitsalyankar@gmail.com
sohinsshah@gmail.com
abhijit@djscoe.org
Abstract-“Computer Simulation for Real Estate Price
Environment” focuses on the price determination of
real estate in Mumbai. The project recognizes and
quantifies factors that play a crucial role in the final
determination of the price of real estate. Major effort
lies in recognizing and evaluating non-quantifiable
factors like location, local infrastructure, and
connectivity, which impact pricing even though they
cannot be valued directly in monetary terms. These
factors along with the samples of the real estate prices
in Mumbai are used to develop a mathematical model
that would give accurate predictions of the prices.
Finally, this model would be employed to simulate the
real world real estate environment, which would enable
the buyer as well as the developer to study the market
under different scenarios and make intelligent
decisions. Also, the noticeable factor in this situation is
that the description tends to assume pattern recognition
problem, and therefore neural networks with back
propagation will be used for implementation. The
system shall be trained based on the history in form of
data collected for which errors can also be minimized to
achieve results with less deviation.
Keywords – Floor space index (FSI), Simulation of Real
Estate Price Environment (SREPE), Base price,
cost/square feet, factors, artificial neural networks
(ANN).
1. INTRODUCTION
The present time estimation of real estate property values
are based largely on speculation. Also consultants and
brokerage firms are mostly relying on experience although
basic real estate techniques do exist in the industry. Also a
property evaluation method like the ready reckoner has its
own discrepancies while valuation of estates.
To construct a mathematical model that predicts the price
of the real estate with certain accepted deviation. Using this
price determined by the mathematical model simulate the
real world real estate environment. The simulation of the
real world real estate environment should aid the client as
well the developer to determine most reasonable price for
the real estate. Also the software should help the developer
to simulate various scenarios to determine the profitability
of his ventures as huge amount of capital is at stake.
2. OVERVIEW OF REAL ESTATE ENIVIRONMENT
Real estate price forecasting is the application of commerce
and technology to predict the state of the area for a future
time and a given situation. Real Estate price forecasts are
made by collecting quantitative data about the current state
of the market in terms of the cost of the plot cost of cement
etc. The human endeavor in determining the profitability is
based mainly upon changes in land costs and prevalent raw
material supply and costs. The dynamic nature of the real
estate market, the massive computational power required to
solve the equations that describe the market, error involved
in measuring the initial conditions, and a warranted
incomplete understanding of processes implies that
forecasts become less accurate as the difference in current
time and the time for which the forecast is being made (the
range of the forecast) increases. There are a variety of end
users to Real estate forecasts. Price warnings are important
forecasts because they are used to guide the customer
regarding the amount that he is spending. Estate forecasts
are used by companies to estimate demand over period of
years. Also forecasting technique can be widely used for
stamp duty collection purposes as the forecast calculator
can be used for property evaluation.
3. OUR APPROACH
Real estate model is different from the other generic
models as it is restricted to specific regions or places
generally inside a city. The real estate costs comprise of
factors that represent the location, the buying power of the
people in that locality, proximity to markets, railway
stations, transportation charges, raw materials, etc. There
are variations in base prices for individual areas inside a
city but the major factors affect the city as a whole remain
the same. Therefore a real estate simulation model is
appropriate for at the most, a city as a whole. Our
approach, SREPE currently focuses on the city of Mumbai.
The various areas within the city are classified as Lower
class (3000-5000Rs/sqft), Middle class (5000-
12000Rs/sqft),
Higher Middle class (12000-20,000Rs/sqft) and Rich class
(20000-aboveRs/sqft). SREPE endeavors to develop a
mathematical model to estimate price for properties in each
sub-area and finally simulate the real estate price
environment for the entire city by employing all the models
and using artificial intelligence.

2. 3.1. PROPOSED SOLUTION
Identifying Real Estate Market trends is a very difficult
task and is usually left to the experience of a construction
agency or other broker. So our project aims at developing a
model that can simulate the Real Estate Environment. Then
by using the data already collected the system can be
trained to simulate the real estate environment. Different
scenarios can be generated such as high demand, low
demand, very low supply of raw materials etc. This can
help us get an estimate, which is very close to the actual
prices. In order to get very close estimation the idea would
be that at every training stage the deviation from the actual
prices would be noted. The deviation is recorded and the
system will be trained further to reduce deviation.
3.2. FACTORS IDENTIFIED IN THE SURVEY
Fig 1: Pie chart of factors’ impact on pricing (survey 1).
Some very important factors that normal business
processes do not always take into consideration, but which
greatly influence the business practices could be states as
follows:
INFLATION: Inflation heavily influences all business
practices. As the inflation rises, so do the prices of most
commodities. Hence, it is absolutely necessary to always
keep track of inflation and take it into consideration.
COMPETITORS IN THE MARKET: While indulging into
any business activity, the other market competitors must
always be taken into consideration. Let’s say for example
there are two companies in the market, A and B, selling the
same good. Now it is important for A to keep a track of the
price at which B sells his goods and accordingly adjust the
prices of his goods, to continue his survival in the markets.
HUMAN BEHAVIOR: One must always take the ‘Human
behavior’ into notice before deciding the price of their
commodities. Let’s see how human behavior can influence
prices of the Real Estate Markets.
Consider a group of people who are more inclined to buy a
flat in a building which has people of their own community
living, or another set of individuals who prefer living in a
colony that has its own temple, and so on. This is where the
human behavior comes into play and largely influences the
market prices.
DEMAND AND SUPPLY: The most important factor that
can affect any business is that of demand and supply. Let’s
say the demand for a certain commodity is high, but the
supply is low. This would create a scarcity of the product in
the markets and as a result people would be willing to pay
higher prices to get hold of the commodity. However if the
demand of a commodity is low and its supply is high, there
would be an abundance of that product in the market, but
with only a few consumers. Naturally, the prices of the
commodity would fall. Therefore, demand and supply
plays the most vital role in price estimation.
UNQUANTIFIABLE FACTORS: For our project, the
unquantifiable factors would primarily include distance
from the station, malls in the vicinity, hospitals, etc.
Furthermore, the impact of these unquantifiable factors is
different for people from different classes of the society.
For example, people from the upper classes can afford cars
and petrol. So they would not mind if the schools and
hospitals were a little far off from their flats. They however
would prefer houses where there are malls and multiplexes
in the vicinity. On the other hand, a person from the lower
strata of the society would always look for a house, which
is closer to the schools, hospitals and food markets. He
wouldn’t really mind if the malls and theatres were at a
distance.
3.3. WORKING MATHEMATICAL MODEL OF A SUB-
AREA:
Due to constraints of time, efforts were initially focused on
the development of a sub model for the area of Santa Cruz
(west). Our market researches and survey classified the
area as Higher Middle class area. While analyzing the
demographics of prices in that area it was noted the
expected factors like location, connectivity, area etc did not
have impact on the contrary subtle factors like a) sound
level prevalent in that area and b) the distance from
religious places formed the most critical factors
determining the cost of the property in the area. The
general factors affecting the city were taken into
consideration for determining the base price for the area
under study. Once the base price was determined, a
mathematical equation was formulated mapping the trends
shown by this area using historical pricing chart.
Base price for the sub-area under consideration is
termed as the price per square feet of a property in the area
that has a) infinite level of noise and b) Nearest to the
religious places.
In other words the base price is price that the builder pays
to just get into the area. While working specifically on the
Santa Cruz (west), the important factor was that area is
well connected; therefore connectivity will not play a role
in estimation. Also super built-up will be useful for
estimation. The mathematical representation of real estate
environment took the form of the following equation:
Cost/square feet = aX2
+b (1/Y)+cZ+d
Where:
a, b, c are constant multipliers.
X: the first priority factor of the sound levels of the area

3. Y: the second priority factor of the distance from religious
places
Z: super-built up
d: base price of the area.
3.4. IMPLEMENTATION
The proposed method of implementation would be by
using neural networks. A neural network is an intellectual
abstraction, which would enable a computer work in a
similar way to that in, which the human brain works [1].
Neural networks are rapidly becoming tool of choice for
the analysis of complex data and systems. In general,
neural networks for prediction and classification allow the
user to model the interrelationship between inputs and
outputs where the functional relationships may not be well
defined. Neural networks are a highly nonlinear alternative
to regression analysis. The difference between artificial
neural networks and regression analysis is that a linear
equation is not required; multiple outputs are possible,
tighter fits of data are possible, and it is possible to work
with "noisy" data. The idea of using an independent test set
(from the training set of data) to avoid the over fitting of
the neural network model is an impressive. It was also
emphasized that once the neural network was developed
with the training and test data sets, it is important to apply
the neural network to a production or validation data set.
For the Prediction, the neural strategy optimizes the
number of neurons in the hidden layer. The more neurons,
the more precise is the memorization of the training data.
Fewer neurons make the network more general. Predictor
optimizes or balances the number of neurons in the hidden
layer.
Well-thought out hybrid models based on simulation and
neural networks can be used to predict and examine impact
of various parameters on performance. A good simulation
model provides not only numerical measures of system
performance, but provides insight into system performance.
For real estate simulation environment, the general factors
will help decide the base prices of the sub areas. Area
specific factors play an important role in price
determination within the sub areas. Factors are the
selection criteria of the project. While interviews with
consultants and developers, along with determining factors,
they also ranked the factors in terms of impact on the
project. These impacts are important for priority. Initial
weight to factors in the equation will be assigned. For
Santa Cruz (west) sub area weights will be assigned to the
factors of sound levels, distance from religious places and
super built up. Then based upon output and its deviation
from the expected output, the weights will be modified.
After extensive surveys and interviews with the brokerage
firms, historical real estate price chart is prepared to
compare with output of module.
3.5. STEP-BY-STEP INSTRUCTIONS FOR TRAINING
A NEURAL NETWORK
Steps 1 Involve the import of the data to be analyzed.
Sample data is provided in an Example file. This is the data
that is used to train the network. This data can also be used
to examine how well the trained neural network makes
predictions.
Fig 2: Neural network module
Steps 2 At this point, you have the option of training the
new neural network or using a neural network that has
already been trained. You don't have to use all of the data
to train the neural network. You can select part of it to train
and leave the remainder (usually at the end of the file) for
testing the trained neural network.
Step 3 Selection is made of the columns representing the
inputs and a single output. The Neural strategy optimizes
the number of neurons in the hidden layer. The more
neurons, the more precise is the memorization of the
training data. Fewer neurons make the network more
general.
Step 4 The actual output is compared with the network's
current predictions that are used to check the current level
of learning statistics or the relevant importance of the
inputs. The Neural strategy allows the complete training of
data without "over fitting" of the data.
4. ISSUES AND CHALLENGES
During initial phase of the project, we noticed that although
there are real estate models for many cities across the
world, Mumbai city lacks one. There are primitive
techniques used for evaluation and estimation, but they
lack teeth as they don’t count for the unquantifiable factors,
example: human emotions. Due to lack of an existing base,
the priority was to do extensive market research to note
down the trends and factors. For factor identification
purposes, we interviewed several developers, consultants
and valuators in the real estate business. During theses
interviews, it was also noticed that many developers and
consultants were not parting with all the factors. Therefore
after an extensive round of interviews with several
developers, we were convinced that most of the crucial
factors were covered as they started repeating themselves
in subsequent interviews. While referring to the existing
evaluation technique, the ready reckoner, it was noticed
that certain crucial factors were missing that decided the

4. value of the property. Using ready reckoner, if two
properties with similar specifications and similar
environment, would have the same value. This is actually
not true in case of Mumbai city. This statement was also
supported by an article in Times of India, dated January
20th
2009. For cost/square feet, brokerage firms were
interviewed. Also during these interviews, it was evident
that lot of past experience and speculation plays during
determining cost of property. When work was focused on
the mathematical model, it was seen that, apart from
general factors, some new area specific factors played a
crucial role. Therefore all in all, the most important
assignment was to reduce the deviation from reality by
accounting for all the factors possible.
5. CONCLUSION
The discovery of certain facts about Mumbai through the
project demystifies the enigma of the real estate scenario in
Mumbai. The standard convention applicable to most other
places does not hold true for Mumbai, this is perhaps the
reason why there are no real estate models for Mumbai.
The model has the potential to empower an average
constructor with the ability to determine the ‘Most
Reasonable Price’ for his property without looking for
reference prices. Once fully completed, the project will
have the distinction of accounting for the preferences of the
people of Mumbai in the final price of the real estate in the
city. The project demands the application of Engineering
Mathematics, Artificial Intelligence and meticulous
application of the principles of Project Management. This
propelled us to implement all our learning of engineering in
the project, thus the experience has been thoroughly
enjoyable and at the same time abundantly enriching.
ACKNOWLEDGEMENTS
We would like thank Dr. R. Narasimhan for helping out in
designing mathematical model of a sub-area. We would
like to also thank Shanghvi Consultants for providing
inputs during the survey of real estate market
REFERENCES
[1] Back propagation and neural networks by Philippe Crochat and
Daniel Franklin.
[2] Neural Networks: A Comprehensive Foundation - Simon Haykin.
[3] Neural Networks: A Systematic Introduction-Raul Rojas Available
at: http://page.mi.fu-berlin.de/rojas/neural/index.html.html
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