Applying multiple linear regression for predicting land price in tien du district bac ninh province

The main aim of the paper is to establish and propose an applied multiple linear regression model based on the factors can affect the price of land in Tien Du District.. Key words: real

The real estate appraisal or land pricing has an increasing importance due to strong growth of the real estate market in Vietnam in the last years In that respect, a permanent preoccupation for specialists is to find newer and better methods to evaluate the real estates

In the international practice, using new approach of appraisal methods is statistical and econometric models The main aim of the paper is to establish and propose an applied multiple linear regression model based on the factors can affect the price of land in Tien Du District The study areas covered by the statistical appraisal are selected from geographical localities, categories or subjects to property taxes

Key words: real estate appraisal, land pricing, multiple linear regression model

ACKNOWLEDGEMENT

After an intensive period of three months, today is the day: writing this note of thanks

is the finishing touch on my thesis to the people who have supported and helped me so much throughout this period

I would first like to show my gratitude to respected supervisor Dr Le Dinh Hai from Faculty of Economics and Business Management, Vietnam National University of Forestry for his continuous support, patient guidance and enthusiastic encouragement during this research

I want to thank you for Prof Dr Lee McDonald, Department of Ecosystem Science and Sustainability, Colorado State University for his valuable and constructive suggestions during the planting of this research work

In addition, I would love to thank various people for their contribution to this project; Special thanks to local people in Tien Du district for providing me helpful information in this study

Finally, I own my gratefully thank to my parents, my friends for their wise counsel and sympathetic ear You always there for me

TABLE OF CONTENT

ABSTRACT

ACKNOWLEDGEMENT

LIST OF FIGURES

LIST OF TABLES

CHAPTER 1 INTRODUCTION 1

CHAPTER 2 STUDY GOALS AND OBJECTIVES 4

2.1 Goal 4

2.2 Specific Objectives 4

CHAPTER 3 STUDY AREA AND RESEARCH METHODOLOGY 5

3.1 Study area 5

3.1.1 Bac Ninh Province 5

3.1.2 Tien Du District 7

3.2 Research methodology 9

3.2.1 The theory of hedonic and multiple linear regression method 10

3.2.2 Framework of factors influencing factors the price of land in market 12

3.2.3 Data collection method: 15

3.2.4 Data analysis method: 16

CHAPTER 4: RESULT 21

4.1 Preparation data 21

4.1.1 Editing 21

4.1.2 Coding 21

4.2 Description data 22

4.2.1 Descriptive statistic quantitative variables on surveyed in Tien Du District 22

4.2.2 Descriptive statistic qualitative variables on land price survey in Tien Du District 22

4.3 Evaluating model 24

4.3.1 Basing on the R- square statistic to evaluate the suitability of model 26

4.3.2 Analyzing ANOVA variance to evaluate the extinction of model 28

4.4 Correlations analysis 28

4.5 Independent sample test (F-test/ Levene’s test) 23

4.6 The result of Linear Multiple Regression 32

4.6.1 Evaluating the independent of variables………31

4.6.2 Checking the defect of model……….32

4.6.3 The contribution of independent variables into model………32

4.6.4 Evaluating marginal influence……….33

CHAPTER 5: DISCUSSION 35

5.1 The evaluating the reality of regression 35

5.2 The solutions for achieving applying multiple linear regression in Tien Du district 36

5.2.1 Solution based on building land pricing system 36

5.2.2 Solution based on building data source 36

5.2.3 Solution based on knowledge on multiple linear regression model on land pricing of appraisers 37 CHAPTER 6: CONCLUSION

CHAPTER 7: REFERENCES

CHAPTER 8: APPENDICES

LIST OF FIGURES

Figure 3.1 The map of Bac Ninh province 6

Figure 3.2 The map of Tien Du District, Bac Ninh province 8

Figure 3.3 Research framework 9

Figure 3.4: Factors influences the price of land 13

Figure 4.1 Regression standardized residual……….31

Figure 4.2 Normal P-P plot……… 31

Figure 4.3 Scatterplot………32

LIST OF TABLES

Table 3.1: Sampling design in Tien Du district 15

Table 4.1: Codebook of questionnaire items 21

Table 4.2: Description of quantitative variables 22

Table 4.3 Description of qualitative variables 22

Table 4.4 Multiple linear regression Model summary output 24

Table 4.5 Multiple linear regression ANOVA output 27

Table 4.6 Correlation between factors and land price 27

Table 4.7 Result of independent Samples Test for the social infrastructure affects to land price 28

Table 4.8 Result of independent Samples Test for the location affects to land price 29

Table 4.9 Result of independent Sample Test for the security affects to land price 23

Table 4.10 Result of independent Sample Test for the shape affects to land price 24

Table 4.11 Coefficient table of multiple linear regression 31

Table 4.12 The contribution of independent variables……….32

Land is one of our most precious assets It encompasses surface, space, soil,

provision of food and water which not only provide special energy for the living on Earth but also create a basis for urban and industrial development by constructing economic, cultural, society, security and defense (Verheye 2007) This resource is fixed in position and limited in area It can’t be increased or lost itself Therefore, land is an irreplaceable resource In traditional societies it is a common good and cannot be alienated nor sold However, in a modern free market system, because of the overpopulation growth and the development of economic society, the demand of using land become bigger and more necessary than ever leading to land is a commodity that is desired and can be exchanged

The exchanging of land associated with property It is also called earnings of land The difference value between land in rural and urban environment is very clear In a rural environment land is primarily a basis for crop production and a source for food supply in general It provides space for living, construction and the development of a variety of social activities so land has thus a production value; it is a primary commodity and a commercial asset While in an urban or suburban environment the expected earnings are mainly linked to the type and nature of buildings that can be constructed on the land, and the services that can

be generated from them: business, commerce, residential, public services, etc (Verheye 2007) With the development of population also the industrialization, people need more land

to produce food, construct infrastructure, etc Land is sold and exchanged basing on the valuation of them This is created the real estate market In this market, price of land is “the value of ownership of stipulated rights in perpetuity, and equal to the estimated present value

of the expected future appropriations of rents It is however also affected by uncertainties about net rent, interest rates and inflation In other words, the value of land depends as well as

on the evolution of rents (Dunkerley 1983) From determining the price of land, land pricing activities occur

Land pricing is considered as one of important fields in economy According to land pricing result, the government and the people who use land, will have the right decisions in management, business and civil transactions Land pricing is the foundation which is serviced for buying and selling, exchanging and transferring land It is also the basis for some policies about compensation of land when the government collects land and calculates the property From that, Land pricing not only does stabilize the land market but also contributes in ensuring the fairness in society, especially in dissolving the conflict about building and implementation of the land laws

Alternatively, nowadays, the price of land in market significantly changes year by year leading to the land pricing activities meet a lot of difficulties In some developed and developing countries in the world, especially Western countries such as the UK, Sweden, etc with the development in clearly tasks about real estate of agencies that have detail valuations land price from the central to local levels and train large of scientific or professional staff in specialized universities They recovered this problem and made the land pricing is established consistently and develop fast by estimating property value in a specific way Mass appraisal (It incorporates mathematical and statistical techniques and at present) has been developing since 1970s by determining all the factors such as location, security, surrounding, etc., from that , evaluate how them can affect to price of land To analyze the factors of influence to the real estate value, hedonic theory (or the real estate valuation theory) are applied primarily As

a technique multiple regression method mostly was used in mass appraisal This made the land market has high exclusiveness because it provides precise property information for appraisers and clients (BOŽIĆ, MILIĆEVIĆ et al 2013), and creates the foundation for the developing of economy and absolutism while this field has still been new in Vietnam

Vietnam saw the significant difference between land price from government and land price from real market The price of land in real market is not recorded in exact paper In land contract which is collected by the governors, the people make value of real estate equal 1/10 the value that they make a deal This lose the tax contributing for the country In addition to, the lack of the unity between two prices causes the people who is revoked land by the officials don’t reach the agreement on price compensation for land users when land acquisition, site clearance and relocation This make a lot of shortcomings in managing and using land Therefore, dealing with the limitations, building the table for land price is necessary with determining factors and how they affect to price of land according to suitable way is necessary

Tien Du Commune, Bac Ninh Province is on the way to integrate and develop On recent years, the social- economic activities also the projects relating to them become more diverse and abundant Especially, the development of infrastructure that puts more pressure on land The land is used more and more and its price is fluctuated leading the problems related

to the disparity in land price between the government and reality Therefore, the determining the factors which affects to the price of land by using multiple linear regression (which based

on the hedonic method) to build efficient assorted- land price bracket is the important thing to reduce this difference This also is useful for regulating the land market

Although, the determination of factors affecting the price of land is necessary, until now, there has no specific research about it for predicting land price in Tien Du district, Bac Ninh Province With the purpose is application land price method into practicality, I have

chosen “Applying Multiple Linear Regression for predicting land price in Tien Du District, Bac Ninh Province” to be my research

CHAPTER 2 STUDY GOALS AND OBJECTIVES 2.1 Goal

Applying Multiple Linear Regression Model to predict the land price in Tien Du District, Bac Ninh Province

CHAPTER 3 STUDY AREA AND RESEARCH METHODOLOGY

3.1 Study area

3.1.1 Bac Ninh Province

The area of Bac Ninh province is the smallest in Vietnam with 822,7km² (GSO, 2014) However, the province has population density (1,375 persons / km²) classed 3 out of 64 provinces and cities in country It is the second highest province’s population density just only lower than population density of Ha Noi and Ho Chi Minh City and is higher 5 times compared with the average population density of the country (274 people / km²) This significantly affected to meet the needs of land use of 1.1312 million people (GSO, 2014) inside the province Located in the North of Vietnam It borders the Hanoi City to the west and southwest, Bac Giang province to the north and east, Hai Duong province to the southeast and Hung Yen province on the south Bac Ninh is one of 13 provinces of Minh Mang King, was first established in Bac Ky in 1831 It also is a province in the north of the Red River delta where existed the beauty of the traditional villages and folks through hundreds of years The topography is relatively flat with the dense network rivers from 1.0 – 1.2 km/km² (Northern Hydrometeorological Observatory) Therefore, mostly slope direction is from north

to south and from west to east, is expressed through surface water runoff poured on Cau, Duong and Thai Binh River The topography not only affects to the slope direction but also results in the climate of this province is representative for tropical monsoon, with distance reasons and pretty cold and less rain in winter but hot and rainy in summer The annual temperature varies between 17.4 to 29.4 Degrees Celsius and the annual precipitation is 1500mm, depending on season Bac Ninh is in focal economic region so it has high standard living of population The growth of GDP amounts to 17.86% (GSO, 2010) In 2012, the provincial gross domestic product (GDP) of over 13607 billion (ranked 9th nationally and 2nd Red River Delta region) and in 2016 the GDP is up to 50799 billion (statistic report of Back

Ninh Province, 2016) For only 4 years, GDP increases 4 times The fast development of economy bases on the shift economic structure towards industrialization: industrial area and construction accounted for 77.82%; Services 16.57%; Agriculture, forestry and fisheries was 5.61% This is the demand of land is increasingly

Tien Du District, Bac Ninh Province was chose to be a case study because of the following reason It is one of the main districts in province, restructuring economics into industrialization It needs more infrastructure so putting a lot of pressure on the land use Therefore, this area also is a focal point of reducing the different level between the land price

of government and market which plays an important role for land pricing effectiveness

www.skyscrapercity.com

Figure 3.1 The map of Bac Ninh province

3.1.2 Tien Du District

a) Geographic location

Tien Du District is a rural area in BAC Ninh Province with total area is 956865 ha It bordered Yen Phong district to the north, Thuan Thanh district to the south, Que Vo district to the east, Tu Son town to the west The district has three national highways 1A, 1B, 38 and

276, 295 provincial road runs through the city connecting to Bac Ninh, Hanoi capital and surrounding provinces which contributing the exchanges economy (consumption products) and cultural of provinces with another places

b) Topography

Tien Du district has the topography is quite flat with the slope smaller than 30˚ (except from low mountainous such as Lim, Van Khang, Che, Phat Tich, etc has elevation ranges from 20-120m) This region is sloping to the sea, directs northwest- southeast The average elevation ranges from 2.5- 6.0 m compared with sea level

Due to the tropical monsoon climate type, the region is hot, humid, rainy and directly influenced by monsoon During the year, weather is divided into two seasons: rainy and dry seasons The rainy season starts from May to October, precipitation is erratic fluctuations through the year, rainfall / month from 125,2mm (10, May) to 283,3mm (August) and often distributed unevenly throughout the year Every year, there are two main monsoons: the northeast monsoon and the southeast monsoon; northeast monsoon starts from October last year to March next year and the Southeast monsoon is from April to September Average monthly temperatures ranges from 23.4˚C – 29.9˚C The temperature also distributes on the season, dry season average temperature> 23˚C, average winter temperature <20˚C

http://www.gis.downappz.com/vn/bac-ninh/tien-du.html Figure 3.2 The map of Tien Du District, Bac Ninh province

c) Soil and land use

Tien Du has different kinds of soil but almost are belongs to 2 main groups: Fluvisol and Acrisol Fluvisol group includes Recent Alluvial Gley Soils (on alluvium of the Red River), Greyi- Eutric Fluvisols; Plinthic Fluvisol and Waterlogged Alluvial soil while Grey soils on Old Alluvium belongs to Acrisol group Therefore, soil in Tien Du district has high porosity leading to well- drained water and be able to plant rice and crash crops

According to land use state of Tien Du District, the area of land agriculture is 6955.75

ha, accounting for 64.17%; the land for non- agriculture (services, industry, etc.,) is 3815.58

ha (35.2%) and the non- land used is about 67.61 ha (0.63%)

d) Socio- economic characteristics

 Population, Labor and GDP

Tien Du district had 35,000 households comprising 135,000 inhabitants (2015) There are 71,099 people who are working and accounts for 52.8 % population

The Gross domestic product (GRDP) average is 8.5% / year; including industry and construction increased by 9.4%; trade and services increased by 8.9; agricultural, silvicultural, aquatic products increased 1.9% The percentage of economic structure by sector: industry and capital construction is 75.5%; trade and services is 16.6%; agriculture, fishery and forestry career 8.1% Per capita income reached 42, 2 million / person / year, the poverty rate fell from 6.72% in 2010 to 1.6% in 2015

3.2 Research methodology

Figure 3.3 represents whole process of doing the analysis work

Figure 3.3 Research framework

Theory of Hedonic Regression and Multiple regression

Building Multiple Linear Regression with the influencing factors,

the observed variables

Questionnaire revision Statistical description and

comparison

Analyzing regression model

Proposing policy

3.2.1 The theory of hedonic and multiple linear regression method

The hedonic method handled out a lot of aspects in society and economy The original hedonic study dealt with vegetables but the majority of the early hedonic studies were directed toward the automobile (Fraley 2009) Waugh’s “Quality factors influencing vegetable prices (1928) is considered the original paper in hedonic studies Waugh attempted to measure the physical qualities of food that influenced consumer choices Waugh’s article was followed by Court’s “Hedonic Price Indexes with Automotive Examples” (1939) The variables chosen were horsepower, braking distance, window size, tires and seat width Implicit price coefficients (shadow prices) were Hedonic estimated for these characteristics Court realized consumer demand for a car could not be adequately expressed by a single price index Consumer’s satisfaction was based upon different combination of characteristics possessed by

a car and because consumers perceive differences in quality, differences in quality, different models of cars sell at different prices While Goodman (1998) identified three important contributions from Court’s early work that have become part of the standard econometric tools used in contemporary hedonic price studies They are the use of linear semi- log equations, the creation of a chained price index based on adjacent time periods, and the use of time dummy variables The time dummy coefficient is interpreted as an estimate of the pure price change, holding the auto’s characteristics constant over the adjacent time periods The application of hedonic method become more and more developed Until now hedonic models are truly universally applied Another important use of hedonic models is the appraisal of individual housing units It make general improvements in housing price indexes and real estate in land market Follain and Ozanne (1979), Chowhan and Prud’homme (2000), Englund, Quigley and Redfearn (1998) and Tiwari and Hasgawa (2000) are among many examples of studies we could cites that basically aim to improve the precision of housing price benchmarks Appraisers and other property market professionals increasingly use

hedonic models They can be used to improve professional practice of appraisers and chartered surveyors (Dubin 1998), or for undertaking mass appraisal for property taxation and other public purposes Besides that, many studies have tried to recover demand parameters (and sometimes supply and demand parameters) for individual housing characteristics, or groups

of characteristics such as Awan, Odling-Smee and Whitehead (1982), Pasha and Butts (1996), Witte, Sumka and Erekson (1982) and Kaufman and Quigley (1987)

The method of hedonic equations is one way expenditures on housing can be decomposed into measurable prices and quantities, so that rents for different dwellings or for identical dwellings in different places can be predicted and compared (Malpezzi 2003) At its simplest, basing on the characteristics of houses, the hedonic equation with the independent variables which represent the individual characteristics of the dwelling and the regression coefficients may be transferred into estimates of the implicit prices of these characteristics The characteristics of houses (location, structure, neighborhood etc.,) have strong impact to the price of house This is similar to the determination price of land plot Therefore, the hedonic method used multiple linear regression model to determine the factors or characteristic and how they can affect to price a parcel land

Regression analysis is valuable tool for real estate professionals in determining the correlation between building characteristics and the transaction price, as well as to predict future transaction pricing

The property appraisal in general and land appraisal in particular is of great importance for one country and its economy Having the information about land and its values offer broad possibilities for market economy and strongly influence development of the real estate market However, to get the information, special attention should be paid to the mass appraisal methods and its use in developing the tax system and framework for

appropriate property appraisal system (BOŽIĆ, MILIĆEVIĆ et al 2013) Multiple regression analysis is just one of the methods for this purpose

Regression has been widely used for many years in economic analysis especially it is useful for many appraisal purposes and for evaluating factors that impact the price of land Wilson, et al (2014) used regression to estimate the value of different types of cropland, which is valuable for appraisal work when puritan sales are difficult to find Especially, Postier et al (1992) showed the use of regression for evaluating factors that affect the price of land Regression analysis is defined as a statistical tool for the investigation of relationships between variables In real estate appraisal the price of property depends on the location and the question is what the relationship between them is and how to quantify it The statistical significance of the relationship estimated and degree of confidence of the conclusions made are of great importance for decision making process

Multiple regression allows more factors to enter the analysis separately and to estimate effect of each In this study, Area, location, security and social infrastructure are entered in multiple regression to show the influence of each to the price of land in market

3.2.2 Framework of factors influencing factors the price of land in market

A wide range of factors that influence the price of land are identified in the literature review These factors are grouped into those that relate to characteristics specific to land; area, location, Security, Surrounding that are discussed below (see figure 3.4)

Figure 3.4: Factors influences the price of land

1/ Area

Many studies showed that the floor area have a positive relationship to the price of the house (Nelson, 2003; Limsombunchai et al, 2004; Kim, 2001) This is similar to the price of land This is because buyers are willing to pay more for a larger space, especially the functional space The land with an area larger than meet the needs of families with many members and those who can afford to pay for a better standard of living For example, Limsombunchai et al (2004) studied in the housing market in New Zealand found that adding more area to increase the value of a land is about 0.08% Bajari and Kahn (2007) reported that large land area related to the price of land

2/ Location

Location factors to be considered in most studies Factors related to the location identified in relation to the entire metropolitan area Location factors easiest and most common implementation is to measure position distance from the house to the center which significantly impacted on land pricing which had been proven by researchers such as Follain

of district

- Land plot located in commune or town

Security

-The level of security in the land located (social evils, the rate of crime)

Surrounding

-Near or far social infrastructures such as school, hospital, market, park, etc

Price of land

et al, 1985; So et al,1996; Bajari and Kahn, 2007; Frew and Jud, 2003; Limsombunchai et al, 2004; Keng, 2000 In addition, the price of land also has influence to the location (commune

or town) has convenient transportation Buyers tend to trade-off between the cost of housing

or land to build house to the cost of travel Positive impact of public transport services on land prices have been examined empirically So et al (1996) study in Hong Kong about the convenience of transportation, as measured by the distance to the station nearest public transport (rail, bus) showed land prices depend on the means public transportation in the territory Therefore, buyers are willing to pay more for the property with easy access to the workplace such as in town where has more convenient transportation

3/ Security

The safety of the area in which the land as located or crime rate also plays an important role in determining land value If the area is one that is crime riddled then the value will be lower (Gregory Akerman, 2009) Babawale and Adewunmi (2011) also indicated that the outside factors such as security, parking- lot, the distance from apartments to church… impacts to the price of real estate It is important to the explanation of variations in land prices are variables derived from urban theory, such as distance to the CBD, and from the amenity literature, such as a community's crime rate, arts, and recreational opportunities (Haurin and Brasington 1996) Austin Troy and J Morgan Grove (2008) using hedonic analysis of property data in Baltimore, MD, they attempted to determine whether crime rate mediates how parks are valued by the housing market The results showed that parked proximity is positively valued by the land market where the combined robbery and rape rates for a neighborhood are below a certain threshold rate but negatively valued where above that threshold

4/ Social infrastructure

The price of land also depends on how far social infrastructure (schools, hospitals, supermarkets, parks etc.) from the land Closing to shopping area or shopping center showed

the impact on the value of surrounding residential properties Chin and Chau (2002) noted that there is a shopping center within 2km radius making the price of land will increase by around 0.11% in Penang, Malaysia Besides that, External benefits, including beautiful scenery, quiet atmosphere and the presence of urban green space has been studied experimentally by Sander and Polasky (2008) used data in the city of Ramsey, United States Results showed that people appreciated residential areas with green space and access to the recreation area with trees The impact of the quality of environment to price of apartments in Brazil by hedonic method The results showed that the apartments, are located near sewage treatment factory, has low value While near the public service establishment has positive impact to the apartment’s price (Furtado 2009)

3.2.3 Data collection method:

In this study, 100 real estates were selected for survey The survey covered an

“interview” component The “interview” component comprised of a questionnaire designed to collect data on general about land characteristic, factors influencing the price of land A copy

of the questionnaire is available on request

Table 3.1: Sampling design in Tien Du district Commune/ Tower Total surveyed about parcel land

& Fidell, 2007): 50 + (8xm) = 50+ (8x5) = 90 Where m is the number of independent factors

of the model Thus, we have sample size should be 100 because the number of independent factors is seven Two representative communes are Noi Due (40 real estates) and Phu Lam commune (30 real estates); one town is Lim (30 real estates) have been selected to conduct the interview The interview design was followed by a stratified sampling approach to obtain representative strata

Using table questionnaires will be conducted in the research This method will be easy

to analysis, data entry and tabulation for nearly all surveys can be easily done with many computer software packages It is useful for predicting land price by Multiple Linear Regression model in SPSS software Face to face methods also will be used to get information about the land pricing methods were practiced by the government to evaluate the efficiency applying Multiple Linear Regression Methods The survey was conducted from 15th August

2016 to 28th August 2016

Secondary data collection

The data for that question was obtained from official government records, academic publication of different agencies such as Vietnam Statistic, Ministry of Agriculture and Rural Development

3.2.4 Data analysis method:

After data collection, the first step would be data preparation with editing, coding, and data entry to ensure accuracy of data from raw data and detect errors or omission to correct

Next, IBM SPSS Statistic 16 was used for data analysis To examine the research question, a multiple linear regression will be conducted to assess if the independent variables predict the dependent variable (criterion) A multiple linear regression assesses the relationship among a set of dichotomous, or ordinal, or interval/ratio predictor variables on an

distance of the parcel of land to the central; area, the width of façade; shape; social infrastructures, and security and the dependent variable is price of land (See table 4.1) for a full list of factors included in the analysis) The following regression equation will be used:

Y( LAND_PRICE)= β0+ β1( DISTANCE_CBD)+ β2(AREA)+

β3(SHAPE)+β4(WIDTH_FACADE)+ β5(SOCIAL_INFRASTRUCTURE) +β6(SECURITY) +β7 (LOCATION) + εi

Explanation for the variances:

X2 (AREA): is the variable shows the area of plat

This is quantitative variable, the unit is square meters, expected coefficient is (+) If the area of plat is larger, the ability to meet the daily needs of people will be higher In addition to, the capacity to invest and develop is bigger leading to the price of land increases

X3(SHAPE): is the variable shows the shape of plat

This is qualitative variable When applying the multiple linear regression model, this variance will be coded with the values: the value is coded as “1” if the shape of land is rectangular and is coded as “0” if it has others shapes (square, parallelogram, trapezoid, reverse trapezoid etc.)

X4 (WIDTH_FACADE): is the variable represents the size of façade

This is quantitative variable, the unit is meter, expected coefficient is (+) The size of façade is larger, the more convenient for the commercial such as constructing building to do business, advertise, etc This factor also can affect the price of land

X5 (SOCIAL_INFRASTRUCTURE): is the variable shows the social infrastructure around

the plat

This is qualitative variable so it is also coded If the location of plat is surrounded by the school, hospital, market or super market, the value is coded as “1” and if it is far away from these places, the surrounding would be coded with a 0, expected coefficient is (+)

X6 (SECURITY): is the variable, presents for the security of the plat

This is qualitative variable Security has 2 levels 1= good security and 0= bad security

X7 (LOCATION): is the qualitative variable, presents for kind of location of land This has 2

kinds The first one is “1 = land belongs to commune unit” and the other is “2= land belongs

to town unit”

εi: is the random error

β0 = a constant, the value of Y when all X values are zero

β1 = the slope of the regression surface (the β represents the regression coefficient associated with each X1)

Besides the factors we mentioned above, the others factors such as the legality of land, psychological factors, the polluted environment (noisy) also cause the increasing- decreasing

the price of land However, in research area, they had no effect to price of land so can’t apply

in regression

Then, standard multiple linear regression- the enter method- will be used The standard method enters all independent variables (predictors) simultaneously into the model Unless theory sufficiently supports a different method, enter is standard method of variable entry Variable will be evaluated by what they add to the prediction of the dependent variable which

is different from the predictability afforded by the other predictors in the model Analyzing correlation (including Pearson Correlation and Sig (2-tailed) value) is used for showing the strength of the linear relationship between each factor and land price also explaining what single and double asterisks signify Test the null hypothesis to express there is linear correlation present (H0: p=0; H1: p#0) The null hypothesis of no linear correlation present in population against the alternative that there is linear correlation present Using guiding that Evans (1996) suggests for the absolute value of r (Pearson correlation) 00-.19 “very weak”, 20-.39 “weak”, 40-.59 “moderate”, 60-.79 “strong” and 80-1.0 “very strong” to evaluate correlation between two variables

The F- test (independent sample test) will be used to assess whether the set of independent variables namely social infrastructure, security, shape and location collectively predicts the dependent variable It tells whether or not we can reject the assumption (the “null hypothesis”) that the independent variables (jointly) are significant R-Squared- the multiple correlation coefficient or determination- will be reported and used to determine how much variance in the dependent variable can be accounted for by the set of independent variables R- Squared must be between zero and one, with a higher number indicating more explanatory power by the independent variables The other relevant statistic “t- test” will be used to determine the significance of each predictor and beta standardized coefficients will be used to determine the magnitude of prediction for each independent variable For significant

predictors, every one unit increase in the predictor, the dependent variable will increase or decrease by the number of unstandardized beta coefficient We also calculated the Durbin- Watson statistic for regression model This is test used to find the presence of autocorrelation

in the residuals which could invalidate the standard error measurements The test returns a value between zero and four If it lie between the interval, the result can be acceptable

Before conducting standard multiple regressions, preliminary analyses were conducted

to ensure no violation of the assumption of multicollinearity, autocorrelation and homoscedasticity among the variables The absence of multicollinearity assumes that predictor variables are not too related and will be assessed using Variance Inflation Factors (VIF) The autocorrelation is tested by the Durbin- Watson value (the value should be lie in 1 and 4) and the homoscedasticity is test by histogram of regression standardize residual and in the Normal Probability Plot (P-P) of the regression Standardized Regression

CHAPTER 4: RESULT 4.1 Preparation data

4.1.1 Editing

After collection 100 samples from respondents, all cases were checked first All cases did not have any missing data for contend of independent variables and dependent variable

4.1.2 Coding

Table 4.1: Codebook of questionnaire items

No Factor Code description Variable name Coding/ creating Dummy

2 The width of facade Width_facade Record numbers

The shape of land plot

5 Security The rate of social

evils and crime

Security Creating dummy variables

4.2 Description data

4.2.1 Descriptive statistic quantitative variables on surveyed in Tien Du District

Table 4.2: Description of quantitative variables

Statistic Statistic Statistic Statistic Std Error Statistic Land_price 100 2.400 20.000 7.41027 333524 3.335243

4.2.2 Descriptive statistic qualitative variables on land price survey in Tien Du District

Table 4.3 Description of qualitative variables