”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.
Environmental Issues in Real Estate Transactions Polsinelli PC
Presentation covers basics of environmental law applicable to real estate transactions including key statutes, important liability defenses or "safe harbors", role of due diligence, and how much diligence is required, contractual provision and resources to address environmental issues and keep the deal alive.
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This document provides an overview of a study that uses artificial neural networks to predict house prices. The study utilizes a dataset containing house attributes like square footage and location. These features are preprocessed to address issues like missing values. The neural network is trained on historical housing data and undergoes backpropagation to minimize prediction error. Hyperparameter tuning is performed to optimize model performance. The trained ANN can predict house prices with high accuracy, outperforming traditional regression models. Model performance is evaluated using metrics like mean absolute error and R-squared. The study aims to develop a robust methodology for accurate house price prediction that can benefit various stakeholders in the real estate industry.
This document describes a system for predicting house prices using data mining and linear regression. It analyzes past housing market trends and prices to predict future prices. The system accepts a customer's specifications and uses a linear regression algorithm to search for matching properties and forecast prices. This helps customers invest without an agent and reduces risks. It describes the linear regression algorithm used to analyze past price data and generate equations to predict future prices based on quarterly data. The system works by accepting customer inputs, searching data, returning results, and allowing customers to request future price predictions.
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This document discusses valuation of real estate properties in Karad City, India. It aims to develop a model using fuzzy logic to accurately predict residential property prices. Accurate property valuation is important for real estate investors and markets. Currently, property valuation considers various qualitative characteristics but predicting prices precisely is complex due to different location and use factors. The document reviews the need for accurate valuation and challenges in the valuation process. It also provides an overview of fuzzy logic systems and their use in applications like real estate valuation to account for various intrinsic and extrinsic pricing factors. The overall goal is to help investors value properties at precise rates.
image based appraisal of real estate propertiesVenkat Projects
Real estate appraisal, which is the process of estimating the price for real estate properties, is crucial for both buys and sellers as the basis for negotiation and transaction. Traditionally, the repeat sales model has been widely adopted to estimate real estate price. However, it depends the design and calculation of a complex economic related index, which is challenging to estimate accurately. Today, real estate brokers provide easy access to detailed online information on real estate properties to their clients. We are interested in estimating the real estate price from these large amounts of easily accessed data. In particular, we analyze the prediction power of online house pictures, which is one of the key factors for online users to make a potential visiting decision. The development of robust computer vision algorithms makes the analysis of visual content possible. In this work, we employ a Recurrent Neural Network (RNN) to predict real estate price using the state-of-the-art visual features. The experimental results indicate that our model outperforms several of other state-of-the-art baseline algorithms in terms of both mean absolute error (MAE) and mean absolute percentage error (MAPE).
Review on House Price Prediction through Regression TechniquesIRJET Journal
This document discusses and compares various machine learning regression techniques for predicting house prices, including linear regression, decision tree regression, random forest regression, and support vector regression. It finds that random forest regression generally provides the most accurate predictions while being able to handle both continuous and categorical variables. However, linear regression is simpler to implement and understand. The document evaluates the techniques based on features, advantages, disadvantages, training speed, prediction speed, and understandability to determine the best algorithm for house price prediction.
House Price Prediction Using Machine LearningIRJET Journal
This document discusses predicting house prices using machine learning algorithms. It begins with an abstract that outlines using machine learning concepts to accurately predict real estate prices based on current market factors. The document then provides details on the proposed system, which uses machine learning models and algorithms like linear regression, Lasso, and decision trees to analyze historical housing data and predict future property prices in India with high accuracy. It aims to help buyers, investors and builders by evaluating market trends and identifying affordable properties. The system is demonstrated on housing data from Bangalore, India.
Environmental Issues in Real Estate Transactions Polsinelli PC
Presentation covers basics of environmental law applicable to real estate transactions including key statutes, important liability defenses or "safe harbors", role of due diligence, and how much diligence is required, contractual provision and resources to address environmental issues and keep the deal alive.
House Price Anticipation with Machine LearningIRJET Journal
This document provides an overview of a study that uses artificial neural networks to predict house prices. The study utilizes a dataset containing house attributes like square footage and location. These features are preprocessed to address issues like missing values. The neural network is trained on historical housing data and undergoes backpropagation to minimize prediction error. Hyperparameter tuning is performed to optimize model performance. The trained ANN can predict house prices with high accuracy, outperforming traditional regression models. Model performance is evaluated using metrics like mean absolute error and R-squared. The study aims to develop a robust methodology for accurate house price prediction that can benefit various stakeholders in the real estate industry.
This document describes a system for predicting house prices using data mining and linear regression. It analyzes past housing market trends and prices to predict future prices. The system accepts a customer's specifications and uses a linear regression algorithm to search for matching properties and forecast prices. This helps customers invest without an agent and reduces risks. It describes the linear regression algorithm used to analyze past price data and generate equations to predict future prices based on quarterly data. The system works by accepting customer inputs, searching data, returning results, and allowing customers to request future price predictions.
IRJET- Valuation of Real Estate in Karad CityIRJET Journal
This document discusses valuation of real estate properties in Karad City, India. It aims to develop a model using fuzzy logic to accurately predict residential property prices. Accurate property valuation is important for real estate investors and markets. Currently, property valuation considers various qualitative characteristics but predicting prices precisely is complex due to different location and use factors. The document reviews the need for accurate valuation and challenges in the valuation process. It also provides an overview of fuzzy logic systems and their use in applications like real estate valuation to account for various intrinsic and extrinsic pricing factors. The overall goal is to help investors value properties at precise rates.
image based appraisal of real estate propertiesVenkat Projects
Real estate appraisal, which is the process of estimating the price for real estate properties, is crucial for both buys and sellers as the basis for negotiation and transaction. Traditionally, the repeat sales model has been widely adopted to estimate real estate price. However, it depends the design and calculation of a complex economic related index, which is challenging to estimate accurately. Today, real estate brokers provide easy access to detailed online information on real estate properties to their clients. We are interested in estimating the real estate price from these large amounts of easily accessed data. In particular, we analyze the prediction power of online house pictures, which is one of the key factors for online users to make a potential visiting decision. The development of robust computer vision algorithms makes the analysis of visual content possible. In this work, we employ a Recurrent Neural Network (RNN) to predict real estate price using the state-of-the-art visual features. The experimental results indicate that our model outperforms several of other state-of-the-art baseline algorithms in terms of both mean absolute error (MAE) and mean absolute percentage error (MAPE).
Review on House Price Prediction through Regression TechniquesIRJET Journal
This document discusses and compares various machine learning regression techniques for predicting house prices, including linear regression, decision tree regression, random forest regression, and support vector regression. It finds that random forest regression generally provides the most accurate predictions while being able to handle both continuous and categorical variables. However, linear regression is simpler to implement and understand. The document evaluates the techniques based on features, advantages, disadvantages, training speed, prediction speed, and understandability to determine the best algorithm for house price prediction.
House Price Prediction Using Machine LearningIRJET Journal
This document discusses predicting house prices using machine learning algorithms. It begins with an abstract that outlines using machine learning concepts to accurately predict real estate prices based on current market factors. The document then provides details on the proposed system, which uses machine learning models and algorithms like linear regression, Lasso, and decision trees to analyze historical housing data and predict future property prices in India with high accuracy. It aims to help buyers, investors and builders by evaluating market trends and identifying affordable properties. The system is demonstrated on housing data from Bangalore, India.
Housing Price Prediction using Machine LearningIRJET Journal
This document discusses using machine learning algorithms to predict housing prices. It first outlines factors that influence housing prices, such as location, structural characteristics, and neighborhood quality. It then evaluates several machine learning algorithms for predicting prices, including multiple linear regression, decision tree regression, and evaluating their respective accuracies. Specifically, decision tree regression achieved the highest accuracy of 84.64% on a Boston housing dataset. Overall, the document examines how machine learning can accurately predict housing prices based on relevant attributes.
The document discusses a proposed project to model the return on investment of incorporating "Spaces In Between" (SIB) elements into real estate developments in China. It outlines several methodologies for evaluating existing projects that incorporate SIB elements: an economic model to collect metrics like rent, occupancy, and expenses to compare to industry baselines; an SIB placement model using foot traffic counts to determine optimal quantities; and a culture/political model accounting for local decision-making processes and consumer preferences. It also summarizes several example projects in Denver that utilize various SIB elements to help test the models. The goal is to demonstrate the value of SIB elements to stakeholders in order to expand their inclusion in developments worldwide.
This document discusses predicting real estate prices using machine learning algorithms. It first provides background on the importance of accurately predicting housing prices. It then describes collecting real estate data and analyzing it to gain insights. Various machine learning regression algorithms (linear regression, decision tree regression, gradient boosting, random forest regression) are applied to the data and their results are compared to determine the most accurate algorithm. Visualizations of the data are also created to understand correlations between attributes. The experimental results show the predicted output after entering property inputs into the selected algorithm. In conclusion, a flexible real estate price prediction solution is presented and different technologies are evaluated for feasibility.
The document discusses implementing a dynamic pricing system for apartment rentals. It begins with an abstract and introduction describing using revenue management principles from other industries like hotels to set optimal rental rates. It then discusses the characteristics of apartment firms, noting both similarities like perishable inventory but also differences from hotels like longer average lengths of stay and fewer transactions. The remainder of the paper will outline the pricing system and modules for setting new lease rates to maximize revenue.
Our aim was to develop algorithms which use a broad spectrum of features to predict real prices. Algorithm applications rely on a rich dataset that includes housing data and macroeconomic patterns. An accurate forecasting model will allow Sber bank to provide more certainty to their customers in an uncertain economy.
1
Sales: 1
Technical Staff: 2
Legal Considerations
In order to operate this business legally, there are a few key legal considerations that must be
addressed:
Business Registration: The business must be properly registered as a legal entity in Ontario.
Based on the capital requirements and ownership structure, a sole proprietorship or corporation
would likely be appropriate.
Food Safety Regulations: As the business will be growing and selling food products, it must
comply with all applicable food safety laws and regulations. This includes things like proper
handling, storage, and transportation of food. Organic certification may also be desirable.
Building/Zoning Regulations: Installing aquaponic units on building properties
The Executive MBA Program with a specialization in Strategy and Leadership is specifically designed for executives and top managers of various companies. It covers all major topics relevant to the successful leadership and management of the organizations. The aim of the Program is to equip professionals with relevant business knowledge and tools in order to improve their own and company’s performance, identify weaknesses and increase efficiency.
This document discusses sources of identification from market level data and solutions to precision problems when estimating demand models. It focuses on adding assumptions like a pricing equation to bring more information from existing data. The pricing equation assumes Nash equilibrium in prices and is estimated jointly with the demand equation. This adds degrees of freedom compared to estimating demand alone. The pricing equation provides information on price elasticities and markups that help identify demand parameters. The document also discusses using micro data and adding cost function assumptions to the model.
This document contains a strategic plan for a construction management company called GO-LEAD Consultants. It includes sections on company introduction, vision, mission, goals, values, organizational culture, and strategic planning considerations. The strategic planning section discusses analyzing competitors, identifying target clients and markets, and determining areas of specialization. It also covers maintaining a flexible plan through ongoing monitoring and improvement. The document provides an overview of GO-LEAD's founding, services, and strategic direction.
This document contains a strategic plan for a construction management company called GO-LEAD Consultants. It includes sections on company introduction, vision, mission, goals, values, organizational culture, and job descriptions. The strategic plan outlines analyzing competitors and the market, determining areas of specialization, and setting short, mid, and long term goals. It emphasizes values like respect for the environment, safety, professionalism, teamwork and innovation. The organizational culture focuses on trust, work-life balance, and using breaks to recharge.
This document provides information on the Microeconomics course DPB 10013, including its credit hours, prerequisites, structure, intended learning outcomes, assessment methods, and syllabus. The key points are:
1. DPB 10013 is a 3-credit hour compulsory core course on microeconomics for the Diploma in Islamic Banking and Finance program. Assessment includes exams, quizzes, assignments, and a case study.
2. The syllabus covers basic microeconomic theories including supply and demand, market equilibrium, elasticity, production, and costs. It examines how individuals and societies make choices given scarce resources.
3. Students will learn to apply microeconomic concepts to solve business problems
The document provides a project proposal for determining real house prices using machine learning and data science. A team of 3 students - Abdullah, M. Suleman, and Umais - will predict house prices based on factors like location, size, and condition by applying algorithms like linear regression and random forest regression to housing data. They will develop a mobile app to provide accurate price predictions to help customers and investors.
Predicting_housing_prices_using_advanced.pdfAyesha Lata
This document discusses various regression techniques that can be used to predict housing prices based on different housing characteristics and features. It first provides background on housing price prediction and factors that influence prices. Then it describes several regression algorithms (hedonic pricing model, artificial neural networks, lasso regression, XGBoost) that can be used to predict prices. The document uses the Ames Housing dataset to test a lasso regression model and analyze impact of features like size, bedrooms, location on prices. The goal is to determine the most accurate advanced methodology for housing price prediction.
This document summarizes a study on surge pricing in transportation network economies. It begins by explaining how dynamic pricing allows prices to change quickly based on demand without significant costs. Dynamic pricing is common in sharing economies and industries with digital sales. The transportation industry, including ridesharing services, uses dynamic pricing where prices surge to match supply and demand. However, consumers have complained about excessive surge pricing in some cases. The document aims to analyze surge pricing as a potential case of excessive pricing and how authorities should regulate dynamic prices. It provides background on dynamic pricing applications and benefits across industries before focusing on its use and effects in the transportation sector.
This thesis proposes implementing and evaluating an order flow imbalance trading algorithm based on the work of Cont et al. The document provides background on the evolution of electronic trading and low latency strategies. It summarizes Cont et al.'s model of using order flow imbalance to predict short-term price changes. The thesis will use a powerful backtesting platform to implement Cont et al.'s predictive model under realistic market conditions, estimating price impact variables over time for each security to improve predictions compared to the original model. The goal is to determine if the model can successfully predict price changes when traded intraday with transaction costs.
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Predictive modeling for resale hdb evaluation pricekahhuey
After going through the pain of buying and selling my HDB recently, I believe a predictive model of resale price is needed. I wrote a model using MLR.
Y = β0 + β1X1 + β2X2 + β3X3 + β4X4 + β5X5 +… + βpXp + Ɛ
This model includes variables like west sun, corridor etc. It would be a great help for buyers and sellers if this model with the support of real data from the authority is available publicly.
1) A major US airline was considering charging additional fees but wanted to analyze the long-term impact on brand, market share, and customer loyalty first.
2) PwC used simulation modeling to analyze how the airline's ticket market share and brand sentiment would be affected by introducing new products or policy changes like fees.
3) Simulation modeling allowed the company to test thousands of scenarios accounting for randomness and uncertainties to better evaluate strategic options compared to traditional methods.
This document summarizes a hedonic home price prediction model developed by Phil Fargason and Jianting Zhao for Zillow. They collected 23 variables related to home characteristics, location, neighborhood attributes, crime, transportation and demographics. Their linear regression model explained 70% of variation in home prices in San Francisco with a mean absolute percentage error of 25%. Key factors correlated with higher prices included property size, number of bedrooms/bathrooms, proximity to transit and colleges, and surrounding home prices.
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Looking for a new home in Istanbul? Look no further than Avrupa Konutlari Esentepe! Our beautifully designed homes provide the perfect blend of luxury and comfort, making them the perfect choice for anyone looking for a high-quality home in the city.
With a wide range of apartment types available, from 1+1 to 4+1, we have something to suit every need and budget. Each apartment is designed with attention to detail and features spacious and bright living areas, making them the perfect place to relax and unwind after a long day.
One of the things that sets Avrupa Konutlari Esentepe apart from other developments is our focus on creating a community that is both comfortable and convenient. Our homes are surrounded by lush green spaces, perfect for enjoying a peaceful stroll or having a picnic with friends and family. Additionally, our complex includes a variety of social and recreational amenities, such as swimming pools, sports fields, and playgrounds, making it easy for residents to stay active and socialize with their neighbors.
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Legal Considerations
In order to operate this business legally, there are a few key legal considerations that must be
addressed:
Business Registration: The business must be properly registered as a legal entity in Ontario.
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would likely be appropriate.
Food Safety Regulations: As the business will be growing and selling food products, it must
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handling, storage, and transportation of food. Organic certification may also be desirable.
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This document discusses sources of identification from market level data and solutions to precision problems when estimating demand models. It focuses on adding assumptions like a pricing equation to bring more information from existing data. The pricing equation assumes Nash equilibrium in prices and is estimated jointly with the demand equation. This adds degrees of freedom compared to estimating demand alone. The pricing equation provides information on price elasticities and markups that help identify demand parameters. The document also discusses using micro data and adding cost function assumptions to the model.
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This document summarizes a study on surge pricing in transportation network economies. It begins by explaining how dynamic pricing allows prices to change quickly based on demand without significant costs. Dynamic pricing is common in sharing economies and industries with digital sales. The transportation industry, including ridesharing services, uses dynamic pricing where prices surge to match supply and demand. However, consumers have complained about excessive surge pricing in some cases. The document aims to analyze surge pricing as a potential case of excessive pricing and how authorities should regulate dynamic prices. It provides background on dynamic pricing applications and benefits across industries before focusing on its use and effects in the transportation sector.
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After going through the pain of buying and selling my HDB recently, I believe a predictive model of resale price is needed. I wrote a model using MLR.
Y = β0 + β1X1 + β2X2 + β3X3 + β4X4 + β5X5 +… + βpXp + Ɛ
This model includes variables like west sun, corridor etc. It would be a great help for buyers and sellers if this model with the support of real data from the authority is available publicly.
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Simulation of real estate price environment
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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