Simulation of Real Estate Price Environment
Somil Kadakia, Satyajit Salyankar, Sohin Shah, Abhijit Joshi
D.J.Sanghvi Colle...
3.1. PROPOSED SOLUTION
Identifying Real Estate Market trends is a very difficult
task and is usually left to the experienc...
Y: the second priority factor of the distance from religious
places
Z: super-built up
d: base price of the area.
3.4. IMPL...
value of the property. Using ready reckoner, if two
properties with similar specifications and similar
environment, would ...
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Simulation of real estate price environment

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”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.

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Simulation of real estate price environment

  1. 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. 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. 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. 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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