VIETNAM NATIONAL UNIVERSITY, HANOI VIETNAM JAPAN UNIVERSITY NGUYEN THE HUNG USING BIG DATA TO CONSTRUCT THE RESIDENTIAL PROPERTY PRICE INDEX IN VIETNAM: THE CASE OF HO CHI MINH CITY MAJOR: PUBLIC POLICY CODE: ………………… RESEARCH SUPERVISORS: Dr. Vu Hoang Linh Hanoi, 2019 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com TABLE OF CONTENTS DECLARATION .v LIST OF ABBREVIATIONS .vi LIST OF AND FIGURES AND TABLE. viii CHAPTER 1 INTRODUCTION. Background of the study.
Rationale of the study. Aims and objectives of the study. Structure of the study .7 CHAPTER 2 LITERATURE REVIEW. The Handbook on Residential Property Price Index.
Median/mean transactions price. Stratification or Mix adjustment. The previous residential property price indexes. RPPI of Ireland .12 i LUAN VAN CHAT LUONG download : add luanvanchat@agmail.
RPPI of Austria. RPPI of Malta. RPPI of Thailand. RPPI of Indonesia.
RPPI of Savills Vietnam .17 CHAPTER 3 DEVELOPING RPPI IN VIETNAM, THE CASE OF HO CHI MINH CITY 19 3. The overview of real estate transaction in Vietnam. The data sources on real estate price in Vietnam. Building big data for RPPI calculating.
Calculating RPPI for apartment in Ho Chi Minh City .26 CHAPTER 4 FINDINGS AND DISCUSSIONS .34 CHAPTER 5 POLICY IMPLICATION AND FURTHER STUDY .40 ii LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com DECLARATION I certify that I myself write this thesis entitled “Using big data to construct the residential property price index in Vietnam: The case of Ho Chi Minh City”. It is not a plagiarism or made by others. Anything related to others‟ works is written in quotation, the sources of which are listed on the list of references. If then the pronouncement proves wrong, I am ready to accept any academic punishment, including the withdrawal or cancellation of my academic degree.
Signature iii LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com ACKNOWLEDGEMENTS No one can achieve anything without the help of others. This thesis could not be completed without priceless assistances of many people. I would like to express my gratitude to all of them. Firstly of all, I would like to express my deepest thanks of gratitude to my respectable supervisor, Dr.
Vu Hoang Linh for his friendly and sympathetic assistance and dedicated involvement throughout the process of this thesis. With profound knowledge and experience, he helped me improving my research. Without his instructions, the thesis would be undone. Secondly, I would also like to be grateful to all my dear professors, JICA experts in Vietnam Japan University who conveyed to me numerous courses and knowledge and classmates of the Master of Public Policy, for their helpful as well as practical suggestions.
I will keep in mind all the memories that we had during my time at Vietnam Japan University. Last but not least, I also own a great debt of gratitude to my family and friends for their immeasurable support bot all my degree and in this arduous process of this study. iv LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com ABSTRACT Calculating real estate price index is one of the major challenges for statistical agencies around the world. However, the need for tools to monitor the real estate market is essential from all levels from micro to macro management.
Therefore, statistical agencies of some countries in the world and some real estate companies like Savill Vietnam have built their own methods based on their actual conditions to calculate this index. Thus, it might be impossible to compare the results. Recently, international statistical organizations have jointly published a manual to guide the general methodology for calculating this indicator. In addition, the development of information technology has also brought many new tools to serve economic management including big data sources.
This study attempts to develop the residential property price index (RPPI) in Vietnam with specific in the apartment market in Ho Chi Minh City using big data from property advertisement web portals as a prototype. The hedonic regression method is used to calculate this index. The research results show that the calculation residential property price index from big data source is completely feasible and that is suggestions for using big data to calculate other statistical indicators. Keywords: Big data, Hedonic Regressions, Ho Chi Minh City apartment, Residential Property Price Index, web crawler.
v LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com LIST OF ABBREVIATIONS ABS: Australian Bureau of Statistics API: Application Programming Interface BDP: Big data processing BI: Bank of Indonesia CSO: Central Statistics Office of Ireland Eurostat: The statistical office of the European Union GDP: Gross Domestic Product GRDP: Gross Regional Domestic Product GSO: The General Statistics Office of Vietnam HoREA: Ho Chi Minh Real Estate Association ILO: International Labor Organization IMF: International Monetary Fund MAD: Median absolute deviation MPD: Mobile position data NER: Named Entity Recognition OECD: The Organisation for Economic Co-operation and Development RPPI: Residential Property Price Index vi LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com RFID: Radio Frequency Identification SDGs: Sustainable Development Goals SBV: State Bank of Vietnam UNECE: The United Nations Economic Commission for Europe WB: The World Bank. vii LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com LIST OF AND FIGURES AND TABLE List of figures Figure 1.1 Five characteristics of Big data .1 The house selling/ buying flow in Vietnam .2 The Flow of building database .3 Map of apartments advertised in Ho Chi Minh city .4 Extract data fields from advertisements .5 Distribution of Price .6 RPPI_aparment of Hochiminh City with Jan, 2018 is reference .33 List of Tables Table 3.1 Summary statistics of database .2 Dummy Hedonic Regression result .3 RPPI_apartment in Ho Chi Minh city with Mar,2018 is reference .4 RPPI_apartment in Ho Chi Minh city with Jan,2018 is reference .32 viii LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com ix LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com CHAPTER 1 INTRODUCTION 1. Background of the study Fluctuations in housing prices have important impacts on substantial economy. In the period 2007-2009, the bubble of real esates in Vietnam led to marcoeconomic instability likes high inflation, trade deflicit and affected economic growth.
More seriously, there has been a considerably increase in housing price and its reversal in the United States which resulted during the global financial crisis. As an asset price in the measurement of inflation, property price becomes an important leading indicator of economy‟s dynamic since investment in a property sector is a long-term type of investment. Property statistics could provide an early sign of economic cycle movement. Rising of property prices often leads to an expansionary phase period (boom) whereas falling of property prices indicate a contractionary phase (bust) (Eurostat, 2013).
The requirement for suitable indexes enabling one to record changes in real estate prices with precision was extremely crucial in such good conditions. Not only does this assist policy makers but also market participants seeking the time when housing prices hit either bottom or top. Thus, it is necessary to develop housing price indexes that can adequately capture housing market trends. The development of such indexes, however, is not an easy task due to the fact that residential properties are heterogeneous in terms of their structural characteristics such as location, size, and facilities.
Each of the house consisting of the location, maintenance and the appliances has different and distinguished characteristics in various degrees, so there are no two houses that are identical in 1 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com terms of quality. Even if the location and basic structure maintain an equivalence at two periods of time, ages or quality of the buildings and the houses are not the same through out the time due to renovations and depreciation of the structure. Furthermore, houses are infrequently sold, meaning that the limited frequency of transaction data available so that it is very difficult to apply the “like with like” method to house pricing as the method of other price index as consumer price index or producer price index. Consequently, the development of the housing price indexes was one of the most difficult tasks for national statistical agencies in terms of methodology.
However, as nations need indicators to help reflect the real estate continuance in macro management of economy, they go on constructing various methods in order to calculate the property prices indexes based on their actual context. Thus, it might be impossible to compare the results. Rationale of the study Recently, many researchers and international statististical agencies have developed the methods of compiling appropriate residential real estate price indexes. The Handbook on Residential Property Price Index, co-ordinated by Eurostat, ILO (International Labor Organization), IMF (International Monetary Fund), OECD (the Organization for Economic Co-operation and Development), UNECE (The United Nations Economic Commission for Europe ), WB (the World Bank) was published.
This coordination produced the very first multidimension abstract of the conceptual and practical issues, which is related to the collection of residential and real estate property price indexes. Moreover, this book also offers guides on the collection of house price indexes and helps increase international comparative study on residential property price indexes as well. Based on this document, each country develop its own way to calculate the index which are pretty adaptable with the practical situation. 2 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com In Vietnam, the real estate is crucial for the economic growth with more than 14 trillion US dollar turnover per year (Dragon Capital, 2017), but up to now, the real estate indicator system reflecting on its position in the picture of the whole economy including price index is very poor.
Until the present, related to the real estate price indicator, only the real estate service provider named Savill is publishing the housing price index, whose methodology and data sources are unclear for the public. Thus, it is major utilised in Savill‟s business purpose without the other researchers or policy makers. Consequently, system of appropriate indexes for real estate is necessary for policy makers, analysts, and financial instituttions to have deeper knowledge on the real estate market and financial market as well as to monitor impacts on Vietnamese economy and the health of the financial market. In recent time, information technology has grown very quickly and has created an extra-large amount of digital data known as “Big Data” – “a term that describes the large volume of data- both structured and unstructured - that inundates a business on a day-to-day basis” (the SAS, Inc).
It is simple that big data is more abundant and complicated data compared to the basic data such as survey data. Especially, new data sources might originate from the Internet, the satellite, post in the social network, etc. This kind of data could be from the different forms such as images, sounds. These packs of data are so extremely resilient that basic data software cannot analyze them.
However, these data can be used to solve all sources of your problems that you were not able to get rid of before. Big data has five important characteristics known as 5Vs as the figure: 3 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.1 Five characteristics of Big data (Source: Yuri Demchenko) As can be seen from the figure above, there are 5Vs of Big Data including Volume, Velocity, Value, Veracity, and Variety. The first is that Volume is defined as the large sources of data generated every millisecond. It could be all the emails, messages, pictures, clips, memes, etc.
With big data scientist, these data will be stored and widely used with the help of the distributed systems in different locations. 4 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com In term of Velocity, this phenomenon means how fast the new data generated and spread around. For example, online messages, which spread viral instantly, or the captured photos by the satellite, transferred the data processing software. Therefore, big data technology allows scientists to simultaneously analyze the data while it continue generating without putting it into the databases.