VIET NAM NATIONAL UNIVERSITY HO CHI MINH CITY UNIVERSITY OF INFORMATION TECHNOLOGY ADVANCED PROGRAM IN INFORMATION SYSTEMS DO HOANG HIEP THESIS GRADUATION REAL ESTATE PRICE FORECAST IN DISTRICT 7 AT HO CHI MINH CITY BY LONG SHORT-TERM MEMORY MODEL BANCHELOR OF ENGINEERING IN INFORMATION SYSTEMS HO CHI MINH CITY, 2021 VIET NAM NATIONAL UNIVERSITY HO CHI MINH CITY UNIVERSITY OF INFORMATION TECHNOLOGY ADVANCED PROGRAM IN INFORMATION SYSTEMS DO HOANG HIEP-18520726 THESIS GRADUATION REAL ESTATE PRICE FORECAST IN DISTRICT 7 AT HO CHI MINH CITY BY LONG SHORT-TERM MEMORY MODEL BANCHELOR OF ENGINEERING IN INFORMATION SYSTEMS THESIS ADVISOR Dr. CAO THI NHAN HO CHI MINH CITY, 2021 ASSESSMENT COMMITTEE The Assessment Committee is established under the Decision. by Rector of the University of Information Technology. — Member ACKNOWLEDGEMENTS First of all, I would like to express my gratitude to Dr.
Cao Thi Nhan for being more than just my thesis advisor during our graduation thesis but since I joined the University of Information Technology as a student of the faculty of Information Systems. With patience, motivation, and immense knowledge, she helped me keep track of the research direction and gave me lots of advice to complete the thesis. Furthermore, she carefully reviews my thesis, and for all the insightful comments, suggestions, and corrections. Besides, I would like to extend my sincere gratitude to Dr.
Ngo Duc Thanh for your invaluable guidance and support during the completion of my thesis. Your expertise and insightful suggestions have greatly contributed to the successful outcome of my work. I am very grateful to my university and faculty for allowing me to prepare my graduation report and to meet the teachers who guided me each semester so that I have enough knowledge to do this essay. TABLE OF CONTENTS TABLE OF CONTENTS .cccssssssssssssssssscnssssnsenssecnsensssenscncassncssencencasencnsenssecnsencsecnsensseeases 2 LIST OF FIGURES.ccsssssssssssssssssenssscseenssscnessssecsesncseenssucassscseenesscnsssencnesscnesscsecsesscseseese 4 LIST OF TABLEG.ccscsssssssssssscssesessesessesscsessssscsesaesessesueseensseesesnesesnesessesnesesseseonsnesesanee 6 LIST OF ABBREVIATIONS.sessssssssssssscssescssenssscnsencssenssscnsescsecnssscnsensscnsensaecascncasencasenscucnsencasensencnsencneenetee 8 CHAPTER 1: INTRODUCTION.
Objective and scope. CHAPTER 2: LITERATURE REVIEW AND THEORICAL BACKGROUND. Data collection and data set generating.-c:cc St St the 5 2. Exploratory- Data AnalySIS.
¿+ TT HH it 7 2. Long-Short Term Memory (LSTM) model and Evaluation Metrics Used. Recurrent Neural Network (RNN). Long-Short Term IMeImOTy.
- - + Sky 26 CHAPTER 3: EXPERIMENT RESUL/T. Data pre-processing 3. +2 1v n TT HH 42 CHAPTER 4: CONCLUSION. Limitations and challenges.
-¿- -- + xxx vs rrrrrerekekrkrkrkrkrerrre 443.8 080000 LIST OF FIGURES Figure 2-1: Data Preparation Process [5] .::cccccseseseseeeeesseeeeeeeeneneeeeseeeseneaeee 4 Figure 2-2: Process Flow of Prediction Model [6]. ¿5-5 << 4 Figure 2-3: Homepage of https://batdongsan.ccccceseeeeeeeeeeeeeeeneee 5 Figure 2-4: The raw dataset. Figure 2-5: Processed Dataset Figure 2-6: Number of category post’s sale .0cccecsceeesesseeeseseesesteseeeseeseeee 7 Figure 2-7: Number of category post’s F€II(. - 5 +5 tt srverrreeeerkrvree 8 Figure 2-8: The proportion of post's sale.
Figure 2-9: The proportion of post's sale. Figure 2-10: Average price of saÌle. ưu 20 Figure 2-11: Average price of T€IIK. - + + tt St ng re Figure 2-12: The distribution of project real estate Figure 2-13: Number of posts sale following by project Figure 2-14: Number of posts rent following by project Figure 2-15: Sale price of pÏaCe.--- ST HH HH ng Hư Figure 2-16: Format data price.
Figure 2-17: RNN model [13]. Figure 2-18: LSTM model [13] Figure 3-1: Environment running Figure 3-2: Configuration. Figure 3-3: Step by step of experiment processes Figure 3-4: Add libraries to practice LSTM model Figure 3-5: Dataframe of sale Figure 3-6: Normalize the rent dataset Figure 3-7: Dataframe of rent Figure 3-8: Normalize the rent dataset Figure 3-9: Create and reshape sale matrix. Figure 3-10: Create and reshape rent matrix Figure 3-11: Create and fit LSTM model Figure 3- 12: Training MSE and validation MSE [12].
Figure 3-13: MSE of sale and rent Figure 3-14: The predicted result of sale Figure 3-15: The predicted result of sale Figure 3-16: The selling price data in 2015 “8008092” [25] 4 Figure 3-17: The stored data use for train model in 2015 “80080927”. 47 Figure 3-18: The selling price data in 2023 “34121215” [26]. Figure 3-19: The selling price data in 2015 “8004059” [27]. Figure 3-20: The stored data use for train model in 2015 “8004059”.
Figure 3-21: The selling price data in 2023 “31928898” [29]. Figure 3-22: The rent price in 2016 “8000576” [30] Figure 3-23: The stored data use for train model in 2016 “8000576”. Figure 3-24: The selling price data in 2023 “32971380” [31] Figure 3-25: The rent price in 2016 “8006099” [32] Figure 3-26: The stored data use for train model in 2016 “8006099”. Figure 3-27: The selling price data in 2023 “36228195” |34],.-- --‹-+ Figure 3-28: The selling price of apartments in Ho Chi Minh City increased faster than the rental price [22] .0 cece cece + + SE 2 T102 21 1H 0111011 trên 55 LIST OF TABLES Table 2-1: Data Description.
Table 3-1: The result 1* experiment of model evaluation parameters. Table 3-2: The closest and highest mean value 1* experiment Table 3-3: The result 2" experiment of model evaluation parameters. Table 3-4: The closest and highest mean value 2TM experiment Table 3-5: The result 3" experiment of model evaluation parameters. “ Table 3-6: The closest and highest mean value 3 experimen(.
LIST OF ABBREVIATIONS LSTM Long-Short Term Memory RNN Recurrent Neural Network MSE Mean Square Error RMSE Root Mean Square Error HCMC Ho Chi Minh city ABSTRACT To study the impact of factors on housing prices, I propose to build different predictive models based on deep learning to identify existing real estate data to predict prices more accurately. housing or its changing trends in the future. Considering that the factors that influence housing prices vary widely, the proposed predictive models fall into two categories. The first is based on many factors that are characteristic of real estate.
The real estate market is one of the most price-focused and volatile. This is one of the key areas for applying machine learning ideas on how to enhance and predict costs with high accuracy. This examination means to predict house prices in Ho Chi Minh city, specifically district 7 with Long-Short Term Memory model. It will help clients to put resources into a request without moving towards a broker.
The result of this research proved that the model gives the highest accuracy. Background In the economy nowadays, real estate is a high-value asset and plays a particularly important role. Real Estate is not only the need basic service of people in terms of the place where to live but is an indispensable means of production in most industries, but also a viable investment channel and great benefit. However, reality shows that valuing real estate is always a difficult problem.
In recent years, the housing bubble has always had a "burst" cycle about every 10 years, causing many consequences, and even triggering an economic crisis. This situation occurs because there are many causes which it is difficult to eliminate or not completely solve. One of them is the problem of adverse selection due to an imbalance of information between buyers and sellers. Most people have a great need for owning an apartment or house, hence these attract investors who are constantly evolving in today's real estate market.
With a vast amount of information about the market that makes customers take a long time to invest, not to mention many scammers taking advantage of the situation is inevitable. The main goal of this thesis is to present a methodological framework for using modern machines and deep learning techniques to incorporate external data, in real estate price forecasting. To experiment with the forecasting ability of Long-Short Term Memory (LSTM) methodology, it helps buyer/renter and seller, especially those who don't know a lot of information to make a more precise choice and risk reduction. This model is suitable for very large data sets, which can be arbitrarily expanded, which is also the advantage of applying information technology in data analysis.
Due to the needs of new graduates, they often have a need to find themselves a place to stay/apartment after their term in the school dormitory expires. Additionally, real estate prices change every day, especially during relocation seasons. Usually, the average young person will stay for about 1 year before moving to a new place. Initially, Recurrent Neural Network (RNN) is used to predict some value using their internal memory to process arbitrary sequences of inputs (short-term).
However, the simple RNN system is not good enough to do the prediction, so I decided to use the more complicated LSTM architecture in RNN system excellent at remembering values for either long or short duration of time. To have a result better, I must create 9 predicion models specifically designed for the real estate market by collecting abundant data from public data. Additionally, future studies should attempt to extract parameters that can ensure higher reliability by performing numerous simulations, even if via a trial-and-error approach. The application will support users: ¢ Know the market price trend.
e Make a plan to manage your finances. The data used in this thesis is available from the website https://batdongsan.vn and mainly focus on district 7 at Ho Chi Minh city. In particular, 2 typical transactions of real estate, namely rent and sale. The full list of data variables is given in Section 2.
There are various considerations influencing the price of properties. The price of real estate is influenced by several important factors like: e Location factor e Trending buy/rent factor e Sale unit price factor 1. Objective and scope 1. Objective e Understand the implementation of business data analysis and machine learning on providing results.
¢ Develop price prediction model based on attributes of district 7: price, price level, sale unit price, unit price, city, district, ward, category, start date, end date, lat, long. se The method helps predict future prices and know distribution of apartment/condominium in district 7. Scope Using Long-Short Term Memory Algorithm for the value predictor of real estate prices. Besides, RMSE is the main metric used for an evaluation in terms of the efficiency 10 1.
Thesis structure e Chapter 1: Introduction ¢ Chapter 2: Literature review and theorical background e Chapter 3: Experimental result ¢ Chapter 4: Conclusions 11 CHAPTER 2: LITERATURE REVIEW AND THEORICAL BACKGROUND 2. Related work With today's technology area, the application of machine learning in all fields is essential, especially in the real estate sector. Because real estate prices change significantly, buyers/sellers have a headache thinking about when they can buy or change the price appropriately. This section describes the previous work done by several researchers in the selected domain of housing price prediction.
Following are the contributions of various researchers in this domain: In 2016, Hujia Yu and Jiafu Wu are students studying at Stanford University applied Regression and Classification on real estate prices [1]. House prices are forecasted with copious regression techniques including Lasso, Ridge, SVM regression and Random Forest. According to this paper, for a regression problem, the most effective is SVR with Gaussian kernel with RMSE of 0.5271, however, visualization for SVM was difficult due to its high-dimensionality. Following its analysis, living area square feet, the material of the roof, and the neighborhood have the greatest statical significance in predicting a houses sale price.
In the 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), a group of students studied at KJ Somaiya College of Engineering used Machine Learning and Neural Networks to house price prediction [2]. This paper aims to make evaluations based on every basic parameter that is considered while determining the price. This model used various regression techniques in its pathway, and the results are not solely determined by one technique rather it is the weighted mean of various techniques to give the most accurate results. The results proved that this approach yields minimum error and maximum accuracy than individual algorithms applied.