Using big data to construct the Residential Property Price Index in Vietnam: The case of Ho Chi Minh City

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 TABLE OF CONTENTS DECLARATION iii ACKNOWLEDGEMENTS iv ABSTRACT v LIST OF ABBREVIATIONS .vi LIST OF AND FIGURES AND TABLE viii CHAPTER INTRODUCTION 1.1 Background of the study 1.2 Rationale of the study .2 1.3 Aims and objectives of the study 1.4 Research instrument .6 1.5 Structure of the study .7 CHAPTER 2.1 LITERATURE REVIEW The Handbook on Residential Property Price Index 2.1.1 Median/mean transactions price 2.1.2 Stratification or Mix adjustment 2.1.3 Repeat-sales 10 2.1.4 Hedonic method .10 2.2 The previous residential property price indexes 12 2.2.1 RPPI of Ireland 12 i 2.2.2 RPPI of Austria 13 2.2.3 RPPI of Malta 14 2.2.4 RPPI of Thailand 15 2.2.5 RPPI of Indonesia 16 2.2.6 RPPI of Savills Vietnam 17 CHAPTER DEVELOPING RPPI IN VIETNAM, THE CASE OF HO CHI MINH CITY 19 3.1 The overview of real estate transaction in Vietnam .19 3.2 The data sources on real estate price in Vietnam 20 3.3 Building big data for RPPI calculating 22 3.4 Calculating RPPI for apartment in Ho Chi Minh City 26 CHAPTER FINDINGS AND DISCUSSIONS 34 CHAPTER POLICY IMPLICATION AND FURTHER STUDY 36 5.1 Policy implication .36 5.2 Further study .38 CHAPTER REFERENCES .40 ii 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 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 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 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 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 LIST OF AND FIGURES AND TABLE List of figures Figure 1.1 Five characteristics of Big data Figure 3.1 The house selling/ buying flow in Vietnam .19 Figure 3.2 The Flow of building database 24 Figure 3.3 Map of apartments advertised in Ho Chi Minh city 25 Figure 3.4 Extract data fields from advertisements 26 Figure 3.5 Distribution of Price 29 Figure 3.6 RPPI_aparment of Hochiminh City with Jan, 2018 is reference .33 List of Tables Table 3.1 Summary statistics of database 28 Table 3.2 Dummy Hedonic Regression result 30 Table 3.3 RPPI_apartment in Ho Chi Minh city with Mar,2018 is reference 32 Table 3.4 RPPI_apartment in Ho Chi Minh city with Jan,2018 is reference 32 viii ix ... 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‟... transactions price Using the indicators of the main inclination from the distribution of housing price for purchased houses during the period is one of the easiest way to calculate house prices As residential. .. objectives of the study The overarching goal of this research is to calculate the residential property price index in Ho Chi Minh City especially for apartment sector from big data source For the achievement