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

ABSTRACT 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 INTRODUCTION CHAPTER STUDY GOALS AND OBJECTIVES 2.1 Goal 2.2 Specific Objectives CHAPTER STUDY AREA AND RESEARCH METHODOLOGY 3.1 Study area 3.1.1 Bac Ninh Province 3.1.2 Tien Du District 3.2 Research methodology 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 Figure 3.2 The map of Tien Du District, Bac Ninh province Figure 3.3 Research framework 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 CHAPTER INTRODUCTION 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 STUDY GOALS AND OBJECTIVES 2.1 Goal Applying Multiple Linear Regression Model to predict the land price in Tien Du District, Bac Ninh Province 2.2 Specific Objectives  To analysis the different factors which affect to price of land in Tien Du District, Bac Ninh Province  To find out how impact of factors on determining price of land in Tien Du District, Bac Ninh Province  To build Multiple Linear Regression model for predicting land price in Tien Du District, Bac Ninh Province collection programs require organization, planning and close monitoring by distributing responsibilities, content and objectives of each type of information for each staff who directly collected; building a network to collect information in specific areas; implementing of crosschecking the information collected 5.2.3 Solution based on knowledge on multiple linear regression model on land pricing of appraisers To conduct mass appraisal, the training professional team and practicing the training is extremely important appraisers are not only deepen knowledge about basic principles and methods of valuation of land or real estate market but also having the skills and thorough grasp of methods of data collection, processing of information; technical knowledge of regression and proficient use of commonly used statistical software To save time and costs for the training of staff, can choose even those who have been formally trained in appraising and the specialized economic valuation of real estate and then trained-well about statistics and data analysis to help increase the valuation work in a professional manner In addition, the authorities should organize regular training courses on the mass appraisal for officials in charge of the departments, particularly the Department of natural resources and environment, finance departments Ongoing staff related tasks should be intensive training on the principles and methods of valuation of land, should be enhance the skill to identify the factors affecting the value of the parcel of land, master the process of mass appraisal and scope of use of appraisal results, and understand the appraisal model 37 CHAPTER 6: CONCLUSION Land is an important resource so determining land prices to calculate the value of land use rights and to state land use rights for objects using land is necessary that prevents the loss and waste national resources The multiple regression of the study shows that independent variables have strong correlation with land price and can reflect 56 % the change of regression model The important predictor variables reflect the factors affecting the price of the land, such as social infrastructure, distance to the CBD, location, width of facade, social infrastructure and security This results in order to serve the local authorities to find out the factors affecting land prices as a reference in the process of land valuation Combining the experience of the valuation of valuers and the results of the model analysis, will be constructed with high accuracy, objectivity and science The better knowledge of how factors affect the land price is very important role for both increasing land value and efficient management Also, this study has got practical implications for individual and decision makers in organizations to decide suitable strategies in marketing or investment in factors that affect to land price to increase the value of land If want to this It is essential to build land information system to serve for the land management through analyzing data on land use right transfer on real estate market Furthermore, using and training the staff has skill about multiple regression method to predict land price is important These made the multiple regression method become useful tool in land management and evaluating the true value of land that creates increasing in budget from using land also decreasing conflicts, lodge a complaint about land Making land is used according to the best and most efficiency 38 CHAPTER 7: REFERENCES Reference BOŽIĆ, B., et al (2013) The use of multiple linear regression in property valuation, Geonauka Dunkerley, H B (1983) Urban land policy: Issues and opportunities, The World Bank Follain, J R and E Jimenez (1985) "Estimating the demand for housing characteristics: a survey and critique." Regional science and urban economics 15(1): 77-107 Fraley, C R (2009) An Econometric Estimate of Hedonic Price Functions: The Case of Vintage Electric Guitars, ProQuest Furtado, B A (2009) "Modeling social heterogeneity, neighborhoods and local influences on urban real estate prices: spatial dynamic analyses in the Belo Horizonte metropolitan area, Brazil." Netherlands Geographical Studies 385 Furtado, B A (2009) "Modeling social heterogeneity, neighborhoods and local influences on urban real estate prices: spatial dynamic analyses in the Belo Horizonte metropolitan area, Brazil." Netherlands Geographical Studies 385 Grimes, A and Y Liang (2009) "Spatial determinants of land prices: Does Auckland’s metropolitan urban limit have an effect?" Applied Spatial Analysis and Policy 2(1): 23-45 Hardie, I W., et al (2001) "The joint influence of agricultural and nonfarm factors on real estate values: An application to the Mid-Atlantic region." American Journal of Agricultural Economics 83(1): 120-132 Haurin, D R and D Brasington (1996) "School quality and real house prices: Inter-and intrametropolitan effects." Journal of Housing Economics 5(4): 351-368 kê, T c t (2014) " Quyết định số 1467/QĐ-BTNMT ngày 21 tháng năm 2014 Bộ trưởng Bộ Tài nguyên Môi trường." 39 Malpezzi, S (2003) "Hedonic pricing models: a selective and applied review." Section in Housing Economics and Public Policy: Essays in Honor of Duncan Maclennan Postier, K., et al (1992) "The impact of quality characteristics on the price of land: A case study of the Kansas farmland market." Journal of the American Society of Farm Managers and Rural Appraisers 56: 53-57 Stockburger, D W (1998) Multivariate statistics: Concepts, models, and applications, David W Stockburger Troy, A and J M Grove (2008) "Property values, parks, and crime: A hedonic analysis in Baltimore, MD." Landscape and urban planning 87(3): 233-245 Verheye, W (2007) "The Value and Price of Land." Land use, land cover and soil sciences Wen, H.-Z., et al (2005) "Hedonic price analysis of urban housing: An empirical research on Hangzhou, China." Journal of Zhejiang University(Science) 6(8): 907-914 Wilson, B and J Frew (2009) "Apartment rents and locations in Portland, Oregon: 19922002." Journal of Real Estate Research Wilson, B., et al (2014) "Regression Estimates of Different Land Type Prices and Time Adjustments." Journal of the ASFMRA 40 CHAPTER 8: APPENDICES LAND SURVEY QUESTIONNARE (Tien Du district- Bac Ninh Province) Name of commune: Name of people using land: - Address: Time for land transfer: - Price for land transfer: MillionVN/m² The information about piece of land - Area: m² - Location: - The size of facade: - The shape of land: Rectangle + Reverse trapezoid Parallelogram + Polygon Square + L shape Trapezoid - The length of land: - The distance from land to: + The main axis traffic of residential area: m; + Commune central: m; + Inter- village road: m; + Highway district: km; + Highway province: km; + Highway nation: km; - The using purpose according to plan: - Social infrastructure ( near hospital, schools, waste treatment system, markets, electronic system: 41 - Security (good or bad): The information about properties relating with land: - House: Kind of house: Structure: year of building: - Area for building: Number of floor: Floor area: m² - Building permit: Yes ( ) ; No ( ) - Other property: - The value of building: Ngày…, tháng…, năm 20… Confirm of people’s committee (Signature and put one’s seal) Investigator Land user Signature Signature 42 RESULT OF LAND SURVEY STT Land_owner Address Location Distance_CBD Area Shape With_facade Social_Infrastructure Security Land_price Chu Thị Tâm Thơn Đình Cả Nội Duệ 10.3 160 7.000 Nguyễn Hữu Luận Thơn Đình Cả Nội Duệ 7.05 86.40 12.000 Nguyễn Thị Loan Thôn Duệ Nam Nội Duệ 152 10 6.024 Nguyễn Thị Lệ Thơn Đình Cả Nội Duệ 8.50 129 4.657 Trần Thị Chúc Thơn Đình Cả Nội Duệ 8.50 50 4.500 Nguyễn Thị Tỵ Thôn Duệ Nam Nội Duệ 8.20 60 18 1 4.657 Chu Thị Toàn Thơn Đình Cả Nội Duệ 8.40 145 13 1 5.652 Hà Thanh Cường Thơn Đình Cả Nội Duệ 8.50 144 12 1 6.100 Nguyễn Văn Quảng Thôn Duệ Khánh Nội Duệ 8.50 170 24 5.192 10 Nguyễn Hữu Chiến Thơn Đình Cả Nội Duệ 8.50 166 12 5.267 11 Nguyễn Văn Vấn Thôn Duệ Nam Nội Duệ 8.50 120 10 0 4.985 12 Nguyễn Văn Tùng Thơn Đình Cả Nội Duệ 110.80 5.9 1 10.367 13 Nguyễn Thị Thao Thôn Due Nam Noi Due 8.50 175 5.300 14 Nguyễn Đức Phú Lũng giang TT Lim 12 200 1 9.000 15 Nguyễn Văn Nhượng Lũng giang TT Lim 13.50 250 10 9.398 16 Nguyễn Văn Mạnh Lũng giang TT Lim 10.80 100 2.400 17 Nguyễn Văn Tiền Lũng giang TT Lim 167 15 1 8.000 18 Nguyễn Đức Đàm Thơn Đình Cả Nội Duệ 150 15 1 4.934 19 Lê Văn Lượng Thôn Tam Tảo Phú Lâm 12 96.25 5.438 20 Nguyễn Hữu Tục Thôn Tam Tảo Phú Lâm 10.90 100.75 11 4.944 21 Nguyễn Hữu Chiến Thôn Tam Tảo Phú Lâm 13 183 5.438 22 Đỗ Thiện Thể Thôn Tam Tảo Phú Lâm 16 187.50 0 4.278 23 Nguyễn Quang Sơn Thôn Tam Tảo Phú Lâm 11 183 5.438 24 Hồng Thị Canh Thơn Tam Tảo Phú Lâm 12.50 95 1 5.650 25 Hồng Thị Hiển Thơn Tam Tảo Phú Lâm 14.50 92.74 0 4.944 26 Nguyễn Hữu Đông Thôn Tam Tảo Phú Lâm 14 89.80 0 4.944 27 Nguyễn Hữu Thỏa Thôn Tam Tảo Phú Lâm 13.40 132.40 4.944 28 Nguyễn Tất Thái Thôn Tam Tảo Phú Lâm 12.80 118.47 1 5.044 29 Nguyễn Xuân Kỵ Thôn Tam Tảo Phú Lâm 10.80 137.23 1 5.857 30 Nguyễn Văn Việt Thơn Đình Cả Nội Duệ 8.20 60 7.769 31 Nguyễn Huy Hùng Lũng Giang TT Lim 8.50 250 1 7.000 32 Nguyễn Văn Đường Lũng Giang TT Lim 7.90 305 20 1 9.924 33 Hoàng Thị Canh Thôn Tam Đảo Phú Lâm 12.50 81.88 13 4.944 34 Hồng Thế Sửu Thơn Lộ Bao Noi Due 12.60 85.36 15 4.944 35 Ngô Hữu Vinh Thôn Tam Tao Phu Lam 15 183 10 7.500 36 Nguyễn Thị Vân Thôn Lộ Bao Nội Duệ 107.60 1 5.438 37 Trần Văn Tiến Thôn Duệ Khánh Nội Duệ 6.50 103 1 5.984 38 Nguyễn Văn Đồng Thôn Lộ Bao Nội Duệ 89.50 1 7.500 39 Nguyễn Thị Liên Thôn Duệ Khánh Nội Duệ 90 4.500 40 Nguyễn Thị Thư Thôn Duệ Khánh Nội Duệ 17.40 81 3.489 41 Nguyễn Thị Vui Thôn Duệ Khánh Nội Duệ 7.80 178.20 10 1 7.000 42 Nguyễn Đình Hồng Lũng Giang TT Lim 165 11 9.000 43 Hoàng Ngọc Duy Lũng Giang TT Lim 56.70 10.909 44 Hoàng Ngọc Đệ Lũng Giang TT Lim 14 202.80 0 6.700 45 Hoàng Huy Dũng Lũng Giang TT Lim 16 273 17 0 5.600 46 Lê Văn Nguyên Lũng Giang TT Lim 240 10 1 13.000 47 Lê Hữu Độ Lũng Giang TT Lim 10 150 16 1 18.000 48 Nguyễn Hữu Thắng Lũng Giang TT Lim 12 200 1 8.000 49 Trần Tiến Thắng Lũng Giang TT Lim 7.50 91.60 1 8.650 50 Nguyễn Hữu Công Lũng Giang TT Lim 124.50 12 12.365 51 Nguyễn Thị Ngọc Anh Thôn Duệ Nam Nội Duệ 82 16.5 0 8.000 52 Nguyễn Thị Mai Hương Thôn Duệ Khánh Nội Duệ 7.3 123.8 13 1 10.000 53 Nguyễn Thị Ngân Thôn Duệ Khánh Nội Duệ 8.5 64 15 6.300 54 Nguyễn Thị Phương Thảo Thôn Duệ Nam Nội Duệ 8.1 132 0 5.500 55 Vũ Thị Minh Hòa Thôn Lộ Bao Nội Duệ 8.4 156.1 16 1 7.000 56 Vũ Đức Anh Tuấn Thôn Duệ Nam Nội Duệ 8.5 210 6.500 57 Lê Quang Huy Thôn Lộ Bao Nội Duệ 79.5 7.5 0 5.000 58 Hồng Huy Linh Thơn Duệ Khánh Nội Duệ 7.5 113 17 8.500 59 Trần Thị Trang Thôn Lộ Bao Nội Duệ 8.2 118 21 1 12.000 60 Nguyễn Thị Tuyết Lan Thôn Duệ Khánh Nội Duệ 6.5 76 14 7.500 61 Nguyễn Thanh Phong Thôn Lộ Bao Nội Duệ 7.5 90.5 16 0 4.500 62 Nguyễn Thanh Phương Thôn Duệ Khánh Nội Duệ 116.5 0 4.500 63 Lê Ngọc Trừ Thôn Duệ Nam Nội Duệ 7.2 172.3 1 4.657 64 Nguyễn Thị Yến Thôn Lộ Bao Nội Duệ 194 1 9.562 65 Đỗ Thiị Bích Thủy Thơn Duệ Nam Nội Duệ 10 72.4 7.5 1 8.655 66 Đặng Bá Tài Thôn Lộ Bao Nội Duệ 6.5 87.4 3.5 0 4.657 67 Nguyễn Thùy Dung Thôn Lộ Bao Nội Duệ 7.5 78.3 15 0 5.679 68 Đỗ Tiến Đạt Thôn Duệ Khánh Nội Duệ 58 0 5.000 69 Trần Thị Ngọc Hà Thôn Duệ Nam Nội Duệ 7.4 262.2 12 1 8.500 70 Nguyễn Thị Ngoan Thôn Lộ Bao Nội Duệ 7.3 150 14 0 8.500 71 Đỗ Ngọc Sáng Lũng giang TT Lim 131.3 0 9.050 72 Bùi Trung Kiên Lũng giang TT Lim 400 10.000 73 Dương Đức Long Lũng giang TT Lim 6.2 160 16 1 15.000 74 Phạm Thế Hiệp Lũng giang TT Lim 6.6 167 8.5 0 8.675 75 Trần Thị Mai Lũng giang TT Lim 87.2 19 0 12.000 76 Nguyễn Cao Minh Lũng giang TT Lim 7.5 344 18 1 17.250 77 Lê Văn Trung Lũng giang TT Lim 8.2 97.5 10 0 12.500 78 Nguyễn Thị Thu Hà Lũng giang TT Lim 10 86 8.500 79 Nguyễn Minh Tâm Lũng giang TT Lim 15.4 73 7.5 7.000 80 Nguyễn Minh Quốc Lũng giang TT Lim 5.4 125.5 15 1 15.000 81 Nguyễn Văn Trí Lũng giang TT Lim 6.5 145 20 14.850 82 Ngô Thị Kim Loan Lũng giang TT Lim 117.2 15 1 20.000 83 Nguyễn Huy Dương Lũng giang TT Lim 6.2 206 15 0 12.825 84 Nguyễn Chí Cường Lũng giang TT Lim 8.2 82.2 6.5 10.000 85 Nguyễn Duy Phương Lũng giang TT Lim 89.3 7.050 86 Phan Văn Hồng Thơn Tam Tảo Phu Lam 12 102 1 6.500 87 Nguyễn Văn Lợi Thôn Tam tảo Phu Lam 8.8 207.1 5.5 0 5.000 88 Nguyễn Văn Quang Thôn Tam tảo Phu Lam 14 124.7 1 8.000 89 Nguyễn Khánh Tồn Thơn Tam tảo Phu Lam 17 100 12 4.675 90 Nguyễn Hồng Anh Thơn Tam tảo Phu Lam 18 160.7 4.000 91 Bùi Tiến Đạt Thôn Tam Tảo Phú Lâm 10 106 0 5.500 92 Phạm Văn Thịnh Thôn Tam Tảo Phú Lâm 15.6 205.9 4.944 93 Nguyễn Văn Phước Thôn Tam Tảo Phú Lâm 17 93.6 3.5 4.090 94 Nguyễn Anh Tuấn Thôn Tam Tảo Phú Lâm 16.8 327.6 5.040 95 Nguyễn Văn Tú Thôn Tam Tảo Phú Lâm 15.5 80.5 1 5.587 96 Bùi Duy Đức Thôn Tam Tảo Phú Lâm 17 70.2 21 5.438 97 Trần Ngọc Tuấn Thôn Tam Tảo Phú Lâm 18.5 90 0 4.944 98 Trần Thị Thu Thôn Tam Tảo Phú Lâm 10.8 178 0 5.587 99 Lê Thị Huệ Thôn Tam Tảo Phú Lâm 18 303 12 4.944 100 Nguyễn Minh Hiếu Thôn Tam Tảo Phú Lâm 15.4 155.2 16 1 6.000 COEEFICIENT MATRIX Correlations Land_price Pearson Correlation Land_pric Social_Infrast e ructure Location Area 200* -.627** 188 -.449** 046 000 061 100 100 100 200* 1 Sig (2-tailed) N Social_Infr Pearson astructure Correlation Location Area Security 383** -.142 219* 000 000 159 028 100 100 100 100 100 -.066 167 -.026 143 -.123 170 515 097 797 157 222 091 046 N 100 100 100 100 100 100 100 100 -.627** -.066 -.275** 251* -.151 089 -.009 Sig (2-tailed) 000 515 006 012 135 378 931 N 100 100 100 100 100 100 100 100 Pearson Correlation 188 167 -.275** 099 094 -.059 037 Sig (2-tailed) 061 097 006 328 354 557 711 N 100 100 100 100 100 100 100 100 -.449** -.026 251* 099 -.208* 009 -.107 Sig (2-tailed) 000 797 012 328 038 931 289 N 100 100 100 100 100 100 100 100 383** 143 -.151 094 -.208* -.144 -.022 Sig (2-tailed) 000 157 135 354 038 154 831 N 100 100 100 100 100 100 100 100 Pearson Correlation -.142 -.123 089 -.059 009 -.144 140 Sig (2-tailed) 159 222 378 557 931 154 N 100 100 100 100 100 100 100 100 Pearson Correlation 219* 170 -.009 037 -.107 -.022 140 Sig (2-tailed) 028 091 931 711 289 831 165 N 100 100 100 100 100 100 100 Pearson Correlation Width_faca Pearson de Correlation Security Shape Sig (2-tailed) Distance_C Pearson BD Correlation Shape Distance_ Width_fac CBD ade * Correlation is significant at the 0.05 level (2-tailed) ** Correlation is significant at the 0.01 level (2-tailed) 165 100 RESULT OF REGRESSION MODEL Model Summaryb DurbinChange Statistics R Model R Squar Watson Adjust Std Error ed R of the R F Sig F e Square Estimate Square Chang df1 df2 Change Change e 594 19.235 771 594 563 2.204314 92 000 a a Predictors: (Constant), Security, Location, Width_facade, Shape, Social_Infrastructure, Area, Distance_CBD b Dependent Variable: Land_price Anova Sum of Mean Model df Squares F Sig 19.235 000a Square Regression 654.233 93.462 Residual 447.028 92 4.859 Total 1101.261 99 a Predictors: (Constant), Security, Location, Width_facade, Shape, Social_Infrastructure, Area, Distance_CBD 1.506 b Dependent Variable: Land_price Unstandardized Standardized Coefficients Coefficients Model t B Std Error (Constant) 10.057 1.081 Location -3.673 526 Distance_CBD -.236 Area 95% Confidence Collinearity Interval for B Statistics Sig Beta Lower Upper Toleranc Bound Bound e VIF 9.305 000 7.910 12.203 -.507 -6.988 000 -4.717 -2.629 838 1.194 067 -.253 -3.528 001 -.369 -.103 859 1.164 001 004 027 373 710 -.006 008 866 1.155 Shape -.515 464 -.076 -1.109 270 -1.437 407 939 1.065 Width_facade 147 044 233 3.345 001 060 234 908 1.101 538 464 081 1.161 249 -.383 1.459 913 1.095 1.261 460 189 2.739 007 346 2.175 930 1.075 Social_Infrastructu re Security a Dependent Variable: Land_price Residuals Statisticsa Minimum Predicted Value Maximum Mean Std Deviation N 2.65063 13.18404 7.41027 2.570683 100 -5.997738 7.200347 000000 2.124955 100 Std Predicted Value -1.852 2.246 000 1.000 100 Std Residual -2.721 3.266 000 964 100 Residual a Dependent Variable: Land_price ... factors on determining price of land in Tien Du District, Bac Ninh Province  To build Multiple Linear Regression model for predicting land price in Tien Du District, Bac Ninh Province CHAPTER STUDY... 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. .. predicting land price in Tien Du District, Bac Ninh Province? ?? to be my research CHAPTER STUDY GOALS AND OBJECTIVES 2.1 Goal Applying Multiple Linear Regression Model to predict the land price in Tien