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

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[r]

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

i

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

1.3 Aims and objectives of the study

1.4 Research instrument

1.5 Structure of the study

CHAPTER LITERATURE REVIEW

2.1 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

ii

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

iii

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

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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

v

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

vi

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

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RFID: Radio Frequency Identification SDGs: Sustainable Development Goals SBV: State Bank of Vietnam

viii

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

1

CHAPTER INTRODUCTION

1.1 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

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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

1.2 Rationale of the study

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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)

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Figure 1.1 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

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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

Next, Variety refers to the divergent kinds of data, which have been used In the past, basic data are said to focus on how to fit into the assumptions, figures, tables or relational databases, such as financial data With big data technology, there will be multiple types of data (structured and unstructured) including messages, social media conversations, photos, sensor data, video or voice recordings

The following term is Veracity identified as the trustworthiness of the data Many forms of big data like posts on Twitter, Facebook, Instagram, etc., consisting of false information, distorted news which might be unreliable, inaccurate, and less controllable However, the analytical software for big data is able to help users to cope with these data Hence, a large database often create the issues of lacking quality or accuracy Finally, it can be believed that Value is one of the most noticeable Vs in this figure Importantly, businesses make a business decisions in any attempt to gather and process their databases Clearly, it is possible for the users to be trapped into the buzz trap if they start using big data without a clear analysis of cost and benefit

Besides requiring new data processing and management methods, big data offers several benefits to a number of users such as the government bodies, enterprises, researchers in their fields

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disadvantages of big data prompted statistical institution to be more careful when implementing big data as new source of official statistics As mentioned by Hammer et al (2017) “Big Data offers opportunities, challenges, and implications for official statistics that compilers and users of statistics need to be aware of when they start to incorporate big data into their work plan to the extent relevant.” 1.3 Aims and 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 of the overall aims, this research seeks out for the following specific objectives:

(1)Buiding a tool to crawl real estate advertisments from the Internet

(2)Extracting information from advertisments to build up real estate database (3)Calculating the residential property price index in Ho Chi Minh City for

apartment sector 1.4 Research instrument

A quantitative research was employed in this study to measure real estate price index in Ho Chi Minh City (HCM)

To collect and build up effective and relevant data for this study, four research techniques were carried out:

(i) Building the web- crawler tool to collect advertisment from internet (ii) Using the Named Entity Recognition (NER) and Vietnamese address normalization to normalize information

7 1.5 Structure of the study

The study comprised of three parts: Introduction, Development and the Conclusion with five chapters

Chapter 1: Introduction presents the background, rationale, aims and objectives, methods, and design of the study

Chapter 2: Literature Review is intended to give some theoretical background related to calculate residential property price index

Chapter 3: Developing RPPI in Vietnam: a case of Ho Chi Minh City deals with research governing orientation, research methods and presents the situation analysis, data collection instruments, data collection procedures and calculate RPPI The detailed results of the database and a comprehensive analysis on the data collected are focused

Chapter 4: Findings and Discussions shows major findings and discussions for residential housing price index in HCM

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CHAPTER LITERATURE REVIEW

Like any other price indexes, house price index measures changes in the average price of properties reflecting changes in the quality-mix of properties transacted over two periods of time There are many areas of society where individuals or organizations use residential property price indices (RPPIs) directly or indirectly either to influence practical decision making or to inform the formulation and conduct of economic policy, (OECD, 2013), so that this kind of index have a number of important uses

Before 2013, international statistical institutes like IMF, UNSD, ect have not just publiced the muanual as the international guide-line for this index, so that each country has different methods depending on their actual context, and it might be impossible to compare the results After the Handbook on Residential Property Price Index was publiced in 2013 by Eurostat and some major international statistics organizations , some countries have developed their index base on this guide lines

2.1 1 The Handbook on Residential Property Price Index

This handbook is the first comprehensive overview of conceptual and practical issues related to the compilation of price indexes for residential properties and also provide international guidance on the compilation of house price indexes and to increase international comparability of residential property price indexes According to this documents, there are four methods can be applied to calculate this index

2.1.1 Median/mean transactions price

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calculate house prices As residential property price distributions are often mingled (mainly representing the repercussion of the varied and mixed traits of houses, the correlation in income distributions and the zero lower is positive, bounding on transaction prices), this is more preferable to use the median than the mean In addition, because there is no need for data on housing traits, but only the house‟s size or location of the house to measure median or mean, a number of prices can be applied

However, the inevitable obstacle of this method is that it is bounded by the buckle of „compositional‟ factors The compositional factors contain the amount of the sales of real estate within specific price level If there is any report about the selling of low-value properties in the area in a monnth, and few of the higher-value properties in that area, this created an implication that there has been a decline in the median or average Nevertheless, most sales of the following month in that area may be in superior properties (i.e., higher values); then this case may imply that the median and average price experienced an increase even the actual overall values may have dropped Compositional change and seasonality may be counted as influential factors on median prices Therefore, it is not likely that the samples of observed transactions can be judged as random Although the median prices are widely used, many other methods are being used in multiple countries to tackle the problem of compositional changes and gain better measures of housing prices

2.1.2 Stratification or Mix adjustment

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countries like Canada, Germany and the United Kingdom It should be notified that there are variations in the approaches applied in specific condition of each country because of the diversity in characteristics of housing markets in different areas Using this method, some small regions (e.g., suburbs) are accumulated into bigger regions and then they compared average of price changes in these bigger regions Another method is to use the stratificatio of price on the basis of the compositional change between lower- and higher-priced suburbs It is obvious that this method can prove highly effectiveness in minimizing the impacts of compositional change For instance, at any time , sold real estate can be classified into groups (or strata) , according to the long run median price of their respective suburbs The mix-adjustment measure of the city-wide price average changes, then calculated as the average changes in the medians for each group

2.1.3 Repeat-sales

Instead of taking each transaction‟s price level as a focus, this method depends on the modifications of the price of real estate properities, which are sold more than once It aims to figure out the same component in the price modification, over a specific period of time However, a hindrance of this repeating sales method is that it is only able with the figures from the transactions which involve real estate with a record of the previous sales Another limitation is that estimating price modications in aspecific period of time, a quarter, it will continue to be rennovated , based on ther sales, occurred in subsequent quarters

2.1.4 Hedonic method

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approach defines properties as some bundle of characteristics such as the price-determining ones (house‟s size, bedrooms, location, etc) And the key idea in which they are “bounded” is that the characteristics are separated and no price in the market for each characteristic, only the characteristic for the house, structure and land, as a whole Therefore, a hedonic method can take account of changes in the composition of transactions in each period In principle, it can also control quality improvements, although the possiblility and feasibility of the method lie on the sufficience and availibility of data on characteristics of residential properties

This handbook also provides three hedonic approaches: (i) the hedonic time dummy approach, (ii) characteristics approach, and (iii) imputation approach This follows previous literature in this area including Triplett (2006), Diewert, W.E., S Heravi and M Silver (2009) and Hill (2010) A problem is that there are many subtitute apparatus for each approach, depending on the estimated period of hedonic coefficients, characteristics, and weights are held constantly; whether double or single imputation will be used for either prices or weights; a direct of indirect formation will be used; chained, rolling window or fixed baskets of characterics, and more

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2.2 The previous residential property price indexes

2.2.1 RPPI of Ireland

In Ireland, from 2016, the Central Statistics Office of Ireland (CSO) has launched the new Residential Property Price Index The new RPPI is based on Stamp Duty data supplied by the Revenue Commissioners, covering all residential property transactions in the State CSO uses hedonic method for the measurement of change of residential property price In this method, transactions over two or more successive periods are pooled and the characteristics which influence price (dwelling type, dwelling size, geographical location and neighbourhood deprivation/affluence) are analysed and their relative contributions to the overall price are estimated By excluding the price change determined by these characteristics independently, CSO are left with a pure price change for a consistent set of characteristics from one time period to another - or more simply - a residential property price index The new RPPI replaces the existing mortgage-based RPPI launched in 2011 The new RPPI is statistically more robust than the existing RPPI and provides for a more detailed geographical breakdown of house price indices The new index is accompanied by a comprehensive range of additional indicators on the residential property market

The hedonic method used for the RPPI uses a log-linear functional form The equation is as follows:

ln⁡(𝑝𝑖𝑡) = 𝑥𝑖𝑡𝛽 + 𝛿𝑡𝐷𝑡 + 𝜇𝑖𝑡

where:

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𝑥𝑖𝑡 is a vector of explanatory variablesof dwelling i in period t: Total floor area (m2), Dwelling type (semi-detached/ detached/ terraced), Eircode routing key and Deprivation Index

𝛽 is a vector of explanatory price coefficients 𝛿𝑡 is a vector of time period coefficients

𝐷𝑡 is a „time dummy‟ (value=1 if in time period t, otherwise 0) 𝜇𝑖𝑡 is an error term

When the regression is applied to a pool of data covering multiple time periods, the time coefficient 𝛿𝑡 can be derived for each period (except the

reference period, typically the first period, where 𝛿1 = 1)

For any two successive time periods, t-1 and t, the antilog of 𝛿𝑡 divided by

the antilog of 𝛿𝑡−1 provides an estimate of the aggregate quality-adjusted house

price change that has occurred (i.e the change in house prices after changes in the various known explanatory variables have been accounted for)

Thus the index for period t is given by:

𝐼𝑡 = 𝑒𝜕 𝑡

𝑒𝜕 𝑡−1 × 𝐼𝑡−1 Where:

𝐼𝑡 is the index in period t

𝐼𝑡−1 is the index in period t-1 2.2.2 RPPI of Austria

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data to calculate RPPI for single family house and Condominiums for Vienna and whole country The “Location” (specific-address) variables are used to modify the spatial differences of the concencus level The calculation used a hedonic regression model with a fixed structure over time This approach may produce biased estimates if the effects of the variables change over time The RPPI for Austria excluding Vienna was composed of the index for condominiums and the index for single-family houses at a ratio of 70% to 30%, with the aggregated index for condominiums comprising the index for new condominiums and that for used condominiums at a ratio of 12.7% to 87.3% 2.2.3 RPPI of Malta

In Malta, they collect advertisements for the sale of properties in newpapers The property include flats, maisonettes, both in shell and in finished form, together with terraced houses, townhouses, house of character and villas They also use Hedonic regression for calculating this index

15 2.2.4 RPPI of Thailand

In Thailand, they publish monthly this index with the volume of house price indices, consisted of measurements as follows: private house with land, with land, apartment, and land

The dataset for compiling RPPI comes from the structured big data source of Commercial Bank Mortgage Loan

The indices were collected and measured from 17 most common commercial bank‟s mortgage loan in Bangkok and proximate area (Bangkok, Samut Prakan, Nonthaburi, Pathum Thani, Nakhon Pathom and Samut Sakhon) Single detached house with land, town house with land and condominium price indices were compiled by using Rolling window and time dummy hedonic regressions method (3-month moving average), controlling housing characteristics (age, storey, entrepreneur and distance to metropolitan transportation services such as the sky train, the underground and express way)

ln 𝑃𝑡 = ln⁡(𝑃0)+ 𝛽 𝑘

6

𝑘=1

𝑋𝑘+ 𝛼 𝑗 𝑇𝐷𝑗

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𝑗 =2

Where:

Pt = price per square meter; P0 = Base price;

Xk = residential characteristics:

- the distance of the center of the district to the nearest Bangkok Mass Transit System and Metropolitan Rapid Transit;

- The distance of the center of the district to the nearest motorway;

16 - Number of floor;

- Age of house;

- Location, using central business district as a representative area, classified Bangkok into differences zones, and there are vicinities TD = Time dummy

Thus the index for period t is given by: Indext =

exp ⁡(𝛼12 )

exp ⁡(𝛼11 ) × 𝐼𝑛𝑑𝑒𝑥𝑡−1 2.2.5 RPPI of Indonesia

In the development of RPPI using Big Data in Indonesia, Bank of Indonesia (BI) they collect monthly data from two largest property advertisement web portals in Indonesia with more than fifty percent total market share BI secured the data acquisition through a non-disclosure agreements (NDA) with those two web portals

Big data preparation and extraction from web portals server are processed using virtual machines and Hadoop Software with approximately 2.2 million ads every month

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They use the semi-log regression model of pooled time dummy variables since house prices variables were not normally distributed in levels (positively skewed distribution) The basic semi-log hedonic model is represented as follow:

𝑙𝑛 𝑝𝑛𝑡 = 𝛽0𝑡 + 𝛿𝜏𝐷𝑛𝜏

𝑇 𝜏=1

+ 𝛽𝑘𝑡𝑧𝑛𝑘𝜏 + 𝜀𝑛𝑡

𝐾 𝑘=1

Where,

𝑝𝑛t is the price of property n at time t,

𝑧𝑛𝑘𝑡 is k characteristics variable of n property at time t,

β0 and βk are intercept and house characteristics parameters, δτ are dummy coefficients

Their hedonic model has building size, lot size, number of bedrooms and number of bathrooms as characteristics variables The number of bedrooms and the number of bathrooms are treated as dummy variables

They have three dummy variables for the number of bedrooms - one and two bedrooms, three bedrooms, and greater than four bedrooms

Four bedrooms is used as a reference based on the highest frequency number of bedrooms They also have three dummies for the number of bathrooms with three bathrooms as reference

2.2.6 RPPI of Savills Vietnam

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services, Savills Vietnam also take on the residential housing complex management Savills Vietnam got the name of the Best Property Consultancy and Best Real Estate Agency in the country with the prestigious award of Asia Pacific Property

For Savill residential property price index, the sample in Hanoi is 161 primary and secondary projects and in Ho Chi Minh city is 172 projects For offices, constant and dynamic baskets are comprised around 195 projects since 2006 In calculating the index, Savills has applied the constant – dynamic basket The constant basket is maintained as price comparison between quarters; however new projects are included in the basket to ensure in-time reflection of the market dynamic

Savills has applied a “liquidity ratio” to adjust the asking price to transacted price discount This ratio is sensitive to market conditions and therefore, can itself be a useful parameter in evaluating market performance

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CHAPTER DEVELOPING RPPI IN VIETNAM, THE CASE OF

HO CHI MINH CITY

3.1 The overview of real estate transaction in Vietnam

Real estate is one of the most dynamic activity sectors in the recent years and is an important market for the Vietnamese economy Estimated from Dragon Capital 2017, the annual turnover of this market is around US $ 12-14 billion, accounting for about 6% GDP of Vietnam in 2017

In the past, the highly active cyclical real estate market has experienced a strong real estate trading period (2006-2010 and 2015-2017) or a quiet period (2010-2014)

However, at every period, the transactions between buyers and sellers are carried out regularly through the following diagram:

Figure 3.1 The house selling/ buying flow in Vietnam

(Source: author)

Step

•Seller posts the advertisement with detail of real estate (price, square, location, characteristics ) to newpapers, website

Step •Buyer search informations and contact to seller

Step

•If argeed, The parties sign a purchase and sale contract at the notary office

•Sign the contract of mortgage at the bank if the buyer has a bank loan

Step

•The seller pays tax at the tax office

•The buyer completes the procedure at the land register office

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3.2 The data sources on real estate price in Vietnam

Apart from methodology, the availability of appropriate data plays a big role in construction price index

From the chart above, there are three types of the secondary data on housing property prices, which can be collected in Vietnam at step 1, step and step 4, each with different advantages and disadvantages:

(i) Data from the commercial banks

The data is often very pure as they were used after the process of documenting However, the fact is that these data are often kept secretly, based on a small number of evaluations It is very difficult to collect data from commercial banks due to this is one of their most sensitive information relating to their secret business strategy , they may not be considered as the model for the whole real estate market as the 90% of business activities in Vietnam are in cash

Likewise, another disadvantage is the fact that banking law does not require the banks to report the detail data to the governmental bodies like State Bank of Vietnam (SBV) or General Statistics Office of Vietnam (GSO)

(ii) Transaction price data from the administrative office like notary office, tax office and land register office

In Vietnam, transaction prices for real estate have to be counted in the system of tax and land register Therefore, those sources represent the whole real estate market However, there are multiple disadvantage in using these kin of data: First, they may be extremely biased, e.g due to the tax avoidance, which may be prevalent over divergent types of real estate property in all kind of space and time

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around the minimum price used to impose taxes charged by the state, not depending on the market price Therefore, the difference may be lower times, times even up to 10 times And the price declared at the tax authority and the land registration agency is the same because they are reported to state agencies Secondly, the data consists of a tiny number of explainable variables (only price, size and location of property) For instance, when the purpose of housing registration is for taxation avoidance, properties will not get registered at all, or may be registered with some irrelevant or misfit information such as square meters of floor, in order to avoid or decrease the tax The consequence, the omitted variable may produce bias assumption and affect the result of the analysis of data,

(iii) Data on quotation prices/ asking prices from real estate platforms

The main useful purpose of this data is their opportune By asking the prices, the indices are collecting and using this information, which can provide a prompt estimate of housing prices than those indices that are based on the transactions The other advantage is the crowed information relating to the house Seldom, the data consists of many records and evaluation with detailed indicators of the property information The sellers always want to give full features of the house to impress the buyer Therefore, the information in the advertisements is the most complete and detailed in all sources of information

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However, the big data can be so biased that the selling price in consent of the sellers and buyers may not reflect the seller‟s preferred price Multiple studies (e.g Knight, 2002) demonstrated that the prices may be varied in accordance to the date, time, location; it makes differences with the estimated price and the actual transaction prices However, Chihiro Shimizu et.al (2011) conducted a study, in which examined the classification of real estate price at constructing stages of the house or transaction process of buying and selling, he found that there is an existing distinction between the housing prices However, one study quantitate the differences, which were removed by using the quantile hedonic regressions, proposed by Machado and Mata (2005), reflecting only small number of differences This implied that the housing prices at distinctive stages of the house‟s transaction process could be comparable, and therefore useful in making up a house price index, as long as they are carefully modified and analyzed properly

To ensure the use of RPPI proposed in the thesis, the author will use the latest data in case of time so the author can ensure to make the most of the data as the data is very detailed about the prices of real estate and the accessibility of the property

3.3 Building big data for RPPI calculating

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The advertisements in the areas, the projects is updated on many different websites constantly and fully with the information such as address, asking price, square, number of bedroom, number of restrooms

To collect real estate advertisements, the major specialize websites for real estate was selected basing on search volume, amount of traffic, number of visitors and number of registered members As the result, the most popular websites for real estate include:

https://batdongsan.com.vn/; http://www.batdongsan.vn/ ; https://alonhadat.com.vn; https://www.chotot.com/; https://bds.rongbay.com/; https://nhadat24h.net/; http://www.muabannhadat.vn/; http://diaoconline.vn/; https://muaban.net

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Figure 3.2 The Flow of building database (Source: author)

The advertisement from these websites normally have free contains depending on the buyers‟ post and the words have the free style also Therefore, it is necessary to standardize the contents of the advertisement to build the cleaned database, for example:

- Unit value of price: some of this is in million Vietnam-dong and other is billion so we need to convert to million Vietnam-dong;

- Address of apartment: Quận (District) can be written as: quận (district), Q (the first letter of Quận) or only the name of the district… so I need to convert to Quận (district) using Named Entity Recognition (NER) and Vietnamese address normalization to normalize this information

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In this study, a database of advertisements for apartments in Ho Chi Minh City from January, 2018 to August, 2018 contains 106,518 records

Figure 4:

Figure 3.3 Map of apartments advertised in Ho Chi Minh city (Source: author)

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From the existing data set, by using the Big data processing (BDP) tool, these data fields for each of the apartment advertised in Ho Chi Minh city as follows: district / price / number of bedroom / number of restroom / month / year was extracted

Figure 3.4 Extract data fields from advertisements (Source: author)

3.4 Calculating RPPI for apartment in Ho Chi Minh City

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In this study, two important factors affecting Ho Chi Minh City's apartment price was pointed out:

(1)Location;

(2)Characteristics of the apartment: such as size, number of bedrooms, number restrooms

Regarding to the importance location, as Harold Samuel, the founder of Land Securities, one of the United Kingdom's largest property companies said it is paramount in real estate

Therefore, we need to include this variable in the hedonic model We already have listings of each apartment for sale including the address of this Ideally, we have information regarding the location of the apartment such as the distance from the apartment to the city center, public utilities such as school, hospital, etc

In the past, the Google Application Programming Interface (API) service could help us in calculating this distance, even they provide us with information about time spent traveling by motorbike or car from that apartment to the centers at rush hour and regular hours

However, this service is currently limited by Google by request new resources, which is beyond the scope of this thesis So the district information was used to represent the location variable in the model

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The model does not mention the quality of construction because all apartment buildings that are allowed to be built must comply with construction standards of Vietnam

At the same time, the Ministry of Construction of Vietnam has no standards for the classification of formal apartments according to criteria such as high-class, intermediate or popular Therefore, this type of apartment has not been mentioned

From January, 2018 to August, 2018 the database contains 106,518 observations After removing the spurious values of price data and the outliers‟ value, the database for calculating is 77,076 observations with the summary statistics:

Table 3.1 Summary statistics of database

Price (mil vnd) Size (m2) Restroom bedroom Min : 495 Min : 47.11 Min : Min : 1st Qu.: 1450 1st Qu.: 62 1st Qu.: 1st Qu.: Median : 1900 Median : 70 Median : Median : Mean : 2236 Mean : 73.65 Mean : 1.848 Mean : 2.088 3rd Qu.: 2800 3rd Qu.: 81 3rd Qu.: 3rd Qu.: Max : 5590 Max : 136.6 Max : Max :

29

Figure 3.5 Distribution of Price

The Hedonic regression model with the equation was applied:

ln 𝑃𝑡 =𝛽0𝑡 + 𝛽 𝑘

6

𝑘=1

𝑋𝑘 + 𝛼 𝑗 𝑇𝐷𝑗

12

𝑗 =2

Where:

Pt = price ;

𝛽0𝑡 = intercept;

Xk = residential characteristics: - the number of bedrooms; - the number of restroom; - size of apartment (m2);

30 TD = Time dummy

In this model, March was chosen as the reference because it has the most observations in this sample

Table 3.2 Dummy Hedonic Regression result

Coefficients:

31

factor(district)773 3.687e-01 3.957e-03 93.189 < 2e-16 *** factor(district)774 3.217e-01 1.080e-02 29.778 < 2e-16 *** factor(district)775 -4.552e-02 5.171e-03 -8.804 < 2e-16 *** factor(district)776 -2.696e-01 3.143e-03 -85.792 < 2e-16 *** factor(district)777 -4.538e-01 4.858e-03 -93.414 < 2e-16 *** factor(district)784 -5.864e-01 4.203e-02 -13.952 < 2e-16 *** factor(district)785 -3.073e-01 5.126e-03 -59.946 < 2e-16 *** factor(district)786 -3.103e-01 5.011e-03 -61.923 < 2e-16 ***

Signif codes: ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’

Residual standard error: 0.2337 on 77044 degrees of freedom Multiple R-squared: 0.7234, Adjusted R-squared: 0.7233 F-statistic: 6501 on 31 and 77044 DF, p-value: < 2.2e-16

The result shows that the adjusted R- square is 0,7233 meaning that 72,33% of the variance for a dependent variable that's explained by variables in this regression model

With these P-values, all of independent variables have statisticial significances Apply the formular to calculate the index for month t is given by:

𝐼𝑡 = 𝑒

𝜕𝑡

𝑒𝜕𝑡−1 × 𝐼𝑡−1 Where:

𝐼𝑡 is the index in month t 𝐼𝑡−1 is the index in month t-1

32

From this result, the RPPI_apartment in Ho Chi Minh city was calculated as belows:

Table 3.3 RPPI_apartment in Ho Chi Minh city with Mar,2018 is reference

Month.year

RPPI index

(exponentiation/ anti log of time dummy

coefficients) Mar, 2018 =100

Jan, 2018 95.33

Feb, 2018 99.01

Mar, 2018 100.00

Apr, 2018 100.69

May, 2018 102.59

Jun, 2018 107.63

Jul, 2018 107.24

Aug, 2018 105.31

(Source: author)

Table 3.4 RPPI_apartment in Ho Chi Minh city with Jan,2018 is reference

Month.year RPPI index

(Jan, 2018 =100)

Jan,2018 100.00

33

Mar,2018 104.89

Apr,2018 105.61

May,2018 107.61

Jun,2018 112.90

Jul,2018 112.49

Aug,2018 110.46

(Source: author)

Figure 3.6 RPPI_aparment of Hochiminh City with Jan, 2018 is reference (Source: author)

100,00

103,86 104,89 105,61

107,61

112,90 112,49 110,46

95,00 105,00 115,00

34

CHAPTER FINDINGS AND DISCUSSIONS

The calculation results for the apartment price index in Ho Chi Minh City continuously increased in the first months of 2018 and decreased in the July and August, 2018

This trend is in line with the opinion of real estate experts In the first haft of year 2018, people's demand for housing has risen sharply while banks and financial institutions have offered incentives to help people buy houses and large value assets (Zing.vn 2018) The July and August of Gregorian calendar are corresponding to the July of Lunar calendar In the Vietnam culture, this month does not bring the lucky to the buyer so that the price went down

This result is also consistent with the report of the authorities, real estate association and real estate company

35

In the third quarter of 2019, the GRDP of Ho Chi Minh City continues to reach 7.89% and the real estate business service continue to be a driving force for this growth with 9,64% moving up

The reasing of housing price is also mentioned in this report as a consequence of the rumors surrounding the expansion of traffic routes

The Ho Chi Minh Real Estate Association (HoREA) reported the real estate market in Ho Chi Minh city in the first half of 2018 on report By which, the supply of apartments in Ho Chi Minh City decreased sharply by 45% compared to the same period in 2017 As the result, the price of apartment in Ho Chi Minh recorded the significant increases from December, 2017 In addition, the infrastructure is getting better and better, with the continuous improvement of the airport and port system, roads connecting with neighboring provinces are also gradually completed (Long Thanh - Dau Giay highway, Ben Luc highway - Long Thanh, Ring Roads 1, 2, 3, Metro Line No ) also the driving force for this price increase

The price increase of the Ho Chi Minh City real estate market is not only reflected in reports of state agencies but also in reports of real estate service companies

According to Savills Vietnam, the Savills Property Price Index (SPPI) in Ho Chi Minh City has increased continuously in the first and quarters of 2018 In particular, the increase in the second quarter of 2018 was recorded as the highest increase in years

36

In addition, the RPPI calculation method of this study complies with the guidelines of the international statistical agencies combined with the experience of some of the previous countries with similar socio-economic circumstances as Indonesia This result is reliable for use in other economic analyzes

CHAPTER POLICY IMPLICATION AND FURTHER STUDY

5.1 Policy implication

Big Data is a interesting source for official statistics (Glasson et al., 2013) as it enables the potential production of speedy and considerable relevant official figures at relatively low costs

Many statistics agencies consider big data as a new data source for official statistics in the compilation of official statistics for the purpose of evidence-based decision making Innovations are needed in the daily production of official statistics, which requires real partnerships with the private sector, new skills and infrastructure, and clear links between available Big Data sources and the Sustainable Development Goals (SDGs) indicators

The Statistics Bureau of Japan uses big data to compiling Consumption Trend Index, the Australian Bureau of Statistics uses big data to compile the Consumer price Index, and the Statistics Netherlands compiles some indicates such as road sensor, population at noon also based on big data

37

including the potential for monitoring and reporting on the sustainable development goals Other while, many conferences have hosted by statistics international bodies like the United Nations Statistics Division, International Monetary Fund, the statistical office of the European Union, International Labor Organization,…

Up to now in Vietnam, all of indicators are compiled from survey data The Viet Nam Statistical Development Strategy 2011-2020, Vision to 2030, signed by The Prime Minister, has identified that application information technology on statistical production is one of main goals of official statistics system Consequently, the calling big data in official statistics is fitting with the requirement of internal and the trend of external

This study has shown that the calculation of the real estate price index, as one of the indicators of the national statistical indicator system, from big data source is completely feasible in Vietnam With the advantages of big data and taking experiences from other countries, Vietnam should consider the use of big data in state statistics as a necessary issue to produce more policy-reflecting figures than socio-economic status in terms of saving time and financial resources In the current conditions of Vietnam, besides calculating RPPI, some areas can apply big data to produce statistics such as (1) calculating and now-casting consumer price index from the price listed on the website of supermarkets and online shopping websites, (2) calculating the number of inbound tourists, migration based on the mobile position data (MPD) from telecommunication service providers (3) determine the phase growth of rice plant using machine learning through photos of smartphones and satellites with high resolution

38 Statistical survey

2 Administrative data

3 Statistical Reporting regime

It is therefore necessary to supplement the big data source for official statistics in the new law

5.2 Further study

This study reviews the methodology of RPPI calculations based on country experience as well as the international guidelines from OECD handbook The study also analyzes the advantages and disadvantages of real estate price data sources in Vietnam, thus provides the most appropriate method adapted to Vietnam's context

With the increasing popularity of the internet in Vietnam and the support of information technology, the compilation of big data on real estate in Vietnam is feasible, qualifying to apply the Hedonic regression method in RPPI calculations

39

40

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