Sự sống động của vỉa hè thành phố hồ chí minh và tác động của vỉa hè đến giá nhà trong những khu phố hỗn hợp

MINISTRY OF EDUCATION AND TRAINING UNIVERSITY OF ECONOMICS HO CHI MINH CITY NGUYEN THI HONG THU THE LIVELINESS OF SIDEWALKS IN HO CHI MINH CITY AND ITS IMPACT ON PROPERTY VALUES IN MIXED-USE NEIGHBORHOODS DOCTORAL THESIS IN ECONOMICS Ho Chi Minh – 2021 MINISTRY OF EDUCATION AND TRAINING UNIVERSITY OF ECONOMICS HO CHI MINH CITY NGUYEN THI HONG THU THE LIVELINESS OF SIDEWALKS IN HO CHI MINH CITY AND ITS IMPACT ON PROPERTY VALUES IN MIXED-USE NEIGHBORHOODS Major: Development Economic Major ID: 93.10.105 DOCTORAL THESIS IN ECONOMICS Supervisors: Dr Nguyen Luu Bao Doan Dr Truong Dang Thuy Ho Chi Minh – 2021 i ACKNOWLEDGES My name is Nguyen Thi Hong Thu, PhD student in the major of Development Economics at University of Economics Ho Chi Minh City I would like to confirm that the research results in this thesis is from my own works and has not been published The thesis does not contain documents extracted in whole or in part from a thesis presented to another qualification at University of Economics HCMC or in any other educational institution Nguyen Thi Hong Thu ii TABLE OF CONTENTS ACKNOWLEDGE ABBREVITATIONS LIST OF TABLES LIST OF FIGURES ABSTRACT CHAPTER INTRODUCTION 1.1 Problem statement 1.2 Research objectives 1.3 Research questions 1.4 Research methodology and scope 1.5 Research contributions 1.6 Thesis structure CHAPTER THE RESEARCH DESIGN 12 2.1 Research process 12 2.2 Definition of key terms 13 2.3 Construction of theoretical framework 19 2.4 Overview of sidewalk in HCMC 22 2.5 The housing market in HCMC 29 2.6 Data samples and data collections 32 2.7 Methodology 36 2.8 Conceptual framework 38 2.9 Summary 40 CHAPTER 41 ESSAY – THE LIVELINESS OF SIDEWALKS IN HO CHI MINH CITY 41 3.1 Introduction 41 3.2 Literature review 44 3.2.1 The theoritical reviews 44 3.2.2 The role of sidewalk as public space 46 3.2.3 Dimensions of public space 49 3.2.4 Empirical reviews of public space and sidewalk in HCMC 53 3.3 Methodology 60 3.3.1 Mixed-method research design 60 3.3.2 The study areas and data collection 63 iii 3.3.3 Data analysis methods 65 3.3.4 Calculating liveliness index 67 3.4 Findings and discussions 73 3.4.1 Descriptive statistics of physical characteristics of sidewalk 74 3.4.2 Behavioral mapping of people and activities 76 3.4.3 Calculating of liveliness index 82 3.4.4 Relationship between the physical characteristics of the sidewalk and liveliness index 85 3.5 Conclusions 89 CHAPTER 90 ESSAY - THE IMPACT OF SIDEWALKS ON PROPERTY VALUES IN MIXEDUSE NEIGHBORHOOD IN HO CHI MINH CITY 90 4.1 Introduction 90 4.2 Literature reviews 92 4.2.1 Theoretical reviews 92 4.2.2 Empirical reviews 98 4.3 Methodology 104 4.3.1 Data 104 4.3.2 Variables and definitions 105 4.3.3 Model construction 107 4.4 Results 111 4.4.1 Data descriptive analysis 112 4.4.2 Regression results 116 4.4.3 The discussion of results 119 4.5 Conclusions 125 CHAPTER 128 CONCLUSIONS, IMPLICATIONS, AND LIMITATIONS 128 5.1 Conclusions 128 5.2 Implication 131 5.3 Limitations 134 LIST OF AUTHOR’S PUBLISHED PAPERS 136 REFERENCES 137 APPENDIX 150 iv ABBREVITATIONS 1Q19: The first quarter 2019 CBD: Central Business District HCMC: Ho Chi Minh City HLM: Hierarchical Linear Modeling HN: Hanoi No.: Number TOD: Transit Oriented Development VIF: Variance Inflation Factors VN: Vietnam v LIST OF TABLES Table 3.1 Data requirement and methods .61 Table 3.2 Calculating of temporal diversity of use 71 Table 3.3 Calculating of diversity of activities .71 Table 3.4 Selected physical characteristics of the sidewalk environment 73 Table 3.5 Sidewalk width .74 Table 3.6 Sidewalk surface quality 75 Table 3.7 Sidewalk material 75 Table 3.8 Sidewalk furniture 76 Table 3.9 Number of people daytime and night-time .79 Table 3.10 Liveliness index for each of 270 sidewalk-segment .84 Table 3.11 Liveliness index for district level 85 Table 3.12 Correlation matrix of variables .85 Table 3.13 Descriptive statistics of variables in regression model 86 Table 3.14 Regression result of the relationship between physical characteristics of the sidewalk and Liveliness index 88 Table 4.1 The twenty characteristics appearing most often in previous hedonic pricing model studies 103 Table 4.2 Variables and their definitions 105 Table 4.3 Functional forms for the hedonic price function 108 Table 4.4 Correlation between dependent variable and independent variables 113 Table 4.5 Descriptive statistics .113 Table 4.6 Percent of observations for each district in HCMC 115 Table 4.7 Regression results 116 vi LIST OF FIGURES Figure 2.1 Research process diagram 13 Figure 2.2 Study area and data used 34 Figure 2.3 The exploratory research design 37 Figure 2.4 Conceptual framework 38 Figure 3.1 Sense of place model (Canter, 1977) 45 Figure 3.2 The coding process in inductive analysis 67 Figure 3.3 Categories of activities on sidewalk in HCMC 77 Figure 3.4 Day-time activities with sidewalk width 78 Figure 3.5 Night-time activities with sidewalk width 79 Figure 3.6 Number of people engaged in some type of activities on day-time and nighttime on 270 sidewalk-segments in 13 districts in HCMC 79 Figure 3.7 Day-time activities 81 Figure 3.8 Night-time activities 81 Figure 3.9 Number of activity on day-time and night-time on 270 sidewalk-segments in 13 districts in HCMC 82 Figure 3.10 Visual analysis 83 Figure 3.11 Scatter plot between liveliness index and sidewalk width 86 Figure 4.1 Scatter plot between price and lot size, CBD, sidewalk width, liveliness index 112 Figure 4.2 The eight-house group in mixed-use neighborhood 121 vii ABSTRACT In most countries around the world, sidewalks are usually for pedestrians for a long time However, it was said that HCMC’s sidewalks were not mingled with any urban cities in the world The HCMC’s sidewalks are possible to generate more liveliness, by commercial activities and social activities to occur in the sidewalks frontage of the house during day-time and night-time The first essay based on social perspectives This research applied the mixed-method research that is a combination to qualitative and quantitative methods to calculate the liveliness index The author uses the observation participant method blended with the visual method to collect data to the five activity categories including sidewalk vending, domestic use, communal, store spillover, transportation The results of the first essay show the estimated value of the liveliness index of 270 sidewalk-segment as a quality standard to consider sidewalk as public space in HCMC Most of the sidewalk-segments in District have a higher level of liveliness than others The second essay based on the home-owners to investigate the impact of sidewalks on property values in mixed-use neighborhoods The sidewalk width could premium property approximately percent based on the primary data of 283 sidewalk segments and house prices in HCMC Besides, the rental property and the spill-over of neighboring houses also have a positive impact on property value Therefore, the government can perform sidewalk expansion or at least maintain a stable sidewalk width, creating a good space for those participating in activities on the sidewalk Keywords: Hedonic pricing model, Liveliness index, mixed-use neighborhoods, sidewalks, property value TÓM TẮT Ở hầu giới, vỉa hè thường nơi dành cho Tuy nhiên, số học giả cho vỉa hè TP HCM không trộn lẫn với vỉa hè thành phố giới Các vỉa hè TP HCM tạo sống động thơng qua chương trình kiện, hoạt động thương mại hoạt động xã hội diễn vỉa hè trước nhà mặt tiền vào ban ngày ban đêm Bài luận thứ tiếp cận viii dựa quan điểm xã hội Nghiên cứu áp dụng phương pháp nghiên cứu hỗn hợp, kết hợp phương pháp định tính định lượng việc thu thập liệu tính tốn số sống động Tác giả sử dụng phương pháp người tham gia quan sát (observation participant method) kết hợp với phương pháp trực quan (visual method) để thu thập liệu 270 phân đoạn vỉa hè, liệu thu thập năm danh mục hoạt động diễn vỉa hè bao gồm bán hàng rong, sử dụng hoạt động gia đình, sinh hoạt cộng đồng, hoạt động kinh doanh, giao thơng diễn vỉa hè Kết nghiên cứu trình bày giá trị ước tính số liveliness index 270 phân đoạn vỉa hè xem tiêu chuẩn chất lượng để xem xét vỉa hè không gian công cộng TP HCM Hầu hết phân đoạn vỉa hè Quận có mức sống động cao phân đoạn vỉa hè Quận lại Bài luận thứ hai tiếp cận dựa quan điểm chủ sở hữu nhà để xem xét tác động vỉa hè đến giá nhà khu phố hỗn hợp Độ rộng vỉa hè có tác động làm gia tăng giá trị nhà khoảng phần trăm Đối với nhà mặt tiền, nhà cho thuê hay nhà nằm khu tác động lan toả kinh doanh có tác động dương đến giá nhà Đây điểm khám phá nghiên cứu nhà riêng lẻ TP HCM Do đó, quyền thực mở rộng vỉa hè hay trì độ rộng vỉa hè ổn định, tạo không gian tốt cho người tham gia vào hoạt động vỉa hè, đặc biệt nhấn mạnh đến hoạt động hộ gia đình Thêm nữa, quyền thành phố thu thuế người mua nhà mặt tiền họ tham gia hoạt động kinh doanh vỉa hè trước nhà Từ khoá: Chỉ số sống động, Giá nhà, Khu phố hỗn hợp, Mô hình định giá Hedonic, Vỉa hè Song, Y., & Knaap, G.J (2004) Measuring the effects of mixed land uses on housing values Regional Science and Urban Economics, 34, 663– 680 Song, Y., & Zenou, Y (2012) Urban villages and housing values in China Regional Science and Urban Economics, 42(3), 495-505 Strassmann, W P (1986) Types of neighbourhood and home-based enterprises: evidence from Lima, Peru Urban Studies, 23(6), 485-500 Strassmann, W P (1987) Home-based enterprises in cities of developing countries Economic Development and Cultural Change, 36(1), 121-144 Talen, E (2015) Do-it-yourself urbanism: A history Journal of Planning History, 14(2), 135-148 Tashakkori, A., & Creswell, J W (2007) Exploring the nature of research questions in mixed methods research Thomas, M (1991) The demise of public space Town planning responses to city change, 209-224 Thompson, C W (2002) Urban open space in the 21st century Landscape and urban planning, 60(2), 59-72 Tiesdell, S., & Oc, T (1998) Beyond ‘fortress’ and ‘panoptic’cities—Towards a safer 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Springer Xiao, Y., Webster, C., & Orford, S (2016) Identifying house price effects of changes in urban street configuration: An empirical study in Nanjing, China Urban Studies, 53(1), 112-131 Yang, H., Song, J., & Choi, M (2016) Measuring the externality effects of commercial land use on residential land value: A case study of Seoul Sustainability, 8(5), 432 Yasmeen, G (1996) ‘Plastic-bag housewives’ and postmodern restaurants?: Public and private in Bangkok’s foodscape, Urban Geography, 17, pp 526–544 Zukin, S (1995) The Cultures of Cities Cambridge, MA: Blackwell APPENDIX Appendix A: Choosing observational scale and good view Fine scale: Gehl (1987) asserts that when we talk about social range in vision, 100 meters is a boundary, the point at which we can see people in motion and their body language in roughly, and 25 meters is another significant threshold, only under which we can precisely read facial expression and principal emotions A good view It is quite interesting to look at our city, glance at surroundings and people, whether when we are walking, standing or sitting A good view is essential in streets It should be noticed that our downward and upward sights are different We look down to figure out where we step on, about 70-80 degrees below the horizon, while look up, the angle of vision is limited to 50-55 degrees above the horizon (Figure 6-31) In addition, our head is usually inclined about 10 degrees downward during normal walking so that we can better assess the situation around By contrast, raising our head upwards is much more difficult (Tilley, 2002) Appendix B: Category and percentage of activities in HCMC Observed activity Sidewalk vendor attending to buyers - Breakfast or dinner food - Drink shop - Motorbike taxi drivers - shoes or clothing marker repairing a shoes or clothing Children washing plates in front of house Women cooking outside a house or a local food store People bathing in front of house Family eat lunch or dinner in front of house Family members sleep in front of house Women standing and chatting outside a store People chatting and drinking Young men chatting, drinking, smoking Children playing outside or playing football People seating and watching sidewalk activities under the shade Lady tapping on her phone Boy seating under the tree resting Man observing the sidewalk outside a cafe shop People excercise in front of house People participate in event on sidewalk Mechanic fixing a cars or motorbike Cars or motorbike washers, washing Store operator selling at a house Women attending to customers at a shop Shop operator siting outside the store Shop operator watching sidewalk activities Shop keeper talking on the phone A lady buying from the shop keeper Men drinking cafe at a cafe shop Shop owner display of goods Customer cars or motorbikes parking space Men at work on street construction Motorbike drivers negotiating with customers Man offloading from a vehicle Motorbike drivers waiting for their customer Standing and resting by a motorbike Pedestrians and walkers Motorbike drivers ride on sidewalk when traffic jam Analytical category Percentage Sidewalk vending 21% Domestic use 9% Communal 6% Store spillover 51% Sidewalk occupancy 13% of pedestrians and transportation means activities Appendix C: Some pictures describe activities on sidewalks Parking lots front shops/stores Food shops on the sidewalk Walking on the streets Vendors Household activities Physical characteristics Appendix D: Frequency and Percent of dummy variables Corner variable Corner | Freq Percent Cum + | 257 90.81 90.81 | 26 9.19 100.00 + Total | 283 100.00 Rental variable Rental | Freq Percent Cum + | 192 67.84 67.84 | 91 32.16 100.00 + Total | 283 100.00 Mixed-use variable Mixed_use | Freq Percent Cum + | 171 60.42 60.42 | 112 39.58 100.00 + Total | 283 100.00 SW_qual variable SW_qual | Freq Percent Cum + | 73 25.80 25.80 | 210 74.20 100.00 + Total | 283 100.00 SW_material variable SW_material | Freq Percent Cum + | 23 8.13 8.13 | 34 12.01 20.14 | 140 49.47 69.61 | 86 30.39 100.00 + Total | 283 100.00 SW_fur variable SW_fur | Freq Percent Cum + | 154 54.42 54.42 | 129 45.58 100.00 + Total | 283 100.00 Street variable Street | Freq Percent Cum + | 239 84.45 84.45 | 44 15.55 100.00 + Total | 283 100.00 Oneway variable Oneway | Freq Percent Cum + | 269 95.05 95.05 | 10 3.53 98.59 | 1.41 100.00 + Total | 283 100.00 Twoway variable Twoway | Freq Percent Cum + | 235 83.04 83.04 | 48 16.96 100.00 + Total | 283 100.00 Appendix E: Scatter histogram Appendix F: Regression models F.1 Correlation matrix between price and day-time and night-time activities | price d_vend~g d_dome~c d_comm~l d_store d_tran~t n_vend~g n_dome~c n_comm~l n_store n_tran~t -+ price | 1.0000 d_vending | -0.0126 1.0000 d_domestic | -0.0766 -0.0492 1.0000 d_communal | -0.0232 0.0932 0.0640 1.0000 d_store | -0.0153 0.2064 0.0359 0.1580 1.0000 d_transport | 0.0486 0.2824 -0.0265 0.1653 0.1414 1.0000 n_vending | 0.0148 0.3643 0.0279 -0.0322 0.1450 0.2173 1.0000 n_domestic | -0.0970 -0.0343 0.2257 0.0925 0.1463 -0.0514 -0.0627 1.0000 n_communal | 0.0201 0.2292 0.0136 0.3623 0.1580 0.1263 0.0512 0.0546 1.0000 n_store | 0.0121 0.0502 -0.0303 0.0584 0.3359 0.1252 0.0434 0.0260 0.0739 1.0000 n_transport | 0.0768 0.1023 -0.1343 -0.0443 0.0038 0.2412 0.0498 -0.0642 0.0512 -0.0147 1.0000 F.2 Regression results (includes districts) Variables Model Model Coef Coef Constant -1.609*** -1.577*** (0.191) (0.199) Structural characteristics Lot size (square meter) 0.644*** 0.636*** (0.057) (0.056) Floor size (square meter) 0.201*** 0.200*** (0.033) (0.033) Corner (1=corner) -0.086 -0.083 (0.063) (0.062) Width (meter) 0.006 0.008 (0.011) (0.011) Property use conditions Rental property (1 = rental 0.112** 0.120*** property) (0.047) (0.046) Mixed-use property (1 = using -0.106** -0.112** house to business and shelter) (0.046) (0.046) Shophouse neighborhood (the 0.047*** 0.049*** number of houses) (0.007) (0.007) Location characteristics Distance to CBD (kilometer) -0.058*** -0.058*** (0.008) (0.009) Distance to School (kilometer) -0.062 -0.069* (0.038) (0.040) Distance to School_squared 0.016*** 0.017*** (square kilometer) (0.005) (0.005) Distance to Hospital (kilometer) 0.181*** 0.198*** (0.055) (0.057) Distance to Hospital_squared -0.037*** -0.039*** (square kilometer) (0.011) (0.011) Distance to Market (kilometer) 0.039 0.019 (0.049) (0.051) Model Coef -1.655*** (0.195) Model Coef -1.552*** (0.197) 0.659*** (0.055) 0.192*** (0.033) -0.062 (0.064) 0.004 (0.010) 0.643*** (0.058) 0.199*** (0.034) -0.077 (0.067) 0.005 (0.011) 0.137*** (0.045) -0.124** (0.048) 0.051*** (0.008) 0.110** (0.046) -0.104** (0.047) 0.050*** (0.007) -0.053*** (0.009) -0.047 (0.038) 0.013*** (0.005) 0.176*** (0.054) -0.036*** (0.010) 0.026 (0.049) -0.056*** (0.009) -0.069* (0.040) 0.016*** (0.005) 0.181*** (0.057) -0.036*** (0.011) 0.034 (0.049) Sidewalk Sidewalk width (meter) 0.047** 0.061** (0.021) (0.025) Distance to sidewalk (meter) -0.002*** -0.002*** (0.000) (0.000) Sidewalk surface (1 = paved 0.104* 0.115* sidewalk) (0.062) (0.062) Sidewalk width*surface -0.037 -0.042 (0.027) (0.028) Sidewalk furniture (1 = sidewalk 0.139*** 0.137*** (0.037) (0.036) has furniture) Street width (meter) 0.086 0.096* (0.052) (0.053) Liveliness index Liveliness index (number) -0.029* (0.015) Day-time activities D sidewalk vending (people) D domestic use (people) D communal (people) D spillover store (people) D transportation (people) SW width*D sidewalk vending SW width*D domestic use SW width*D communal SW width*D spillover store SW width*D transportation 0.047* (0.027) -0.002*** (0.000) 0.116* (0.066) -0.036 (0.029) 0.132*** (0.035) 0.074 (0.055) 0.047* (0.026) -0.002*** (0.000) 0.120* (0.066) -0.040 (0.031) 0.138*** (0.037) 0.082 (0.055) -0.003 (0.017) -0.056* (0.031) -0.059*** (0.012) 0.014 (0.013) -0.025 (0.026) -0.002 (0.006) 0.030** (0.015) 0.006 (0.004) -0.008* (0.004) 0.015* (0.008) Night-time activities N sidewalk vending (people) N domestic use (people) N communal (people) N spillover store (people) 0.005 (0.014) -0.019 (0.024) -0.014 (0.033) -0.005 (0.004) N transportation (people) -0.043** (0.020) -0.001 (0.003) 0.004 (0.010) 0.001 (0.013) 0.001 (0.000) 0.016* (0.009) SW width*N sidewalk vending SW width*N domestic use SW width*N communal SW width*N spillover store SW width*N transportation Districts (Base: Binh Tan district) Distrist 0.340*** (0.091) Distrist 0.357*** (0.105) Distrist 0.299*** (0.089) Distrist 10 0.291*** (0.074) Distrist 11 0.249*** (0.073) Phu Nhuan Distrist 0.331*** (0.068) Tan Binh Distrist 0.244*** (0.060) R_squared 0.8488 F (Prob > F) 47.85 (0.000) Mean VIF 4.13 AIC 102.04 BIC 200.47 F3 F test Model (1) livelinessindex = F( 1, 255) = Prob > F = 3.59 0.0592 Model ( 1) d_vending = ( 2) d_domestic = ( 3) d_communal = 0.347*** (0.093) 0.330*** (0.108) 0.325*** (0.085) 0.288*** (0.075) 0.250*** (0.073) 0.340*** (0.069) 0.259*** (0.062) 0.8518 48.31 (0.000) 4.07 98.34 200.41 0.365*** (0.090) 0.391*** (0.103) 0.402*** (0.079) 0.323*** (0.075) 0.254*** (0.072) 0.335*** (0.072) 0.261*** (0.059) 0.8625 49.34 (0.000) 4.72 95.24 230.12 0.347*** (0.092) 0.339*** (0.112) 0.298*** (0.090) 0.288*** (0.077) 0.260*** (0.076) 0.351*** (0.071) 0.236*** (0.060) 0.8523 36.26 (0.000) 4.85 115.38 250.26 ( 4) d_store = ( 5) d_transport = ( 6) sw_dvending = ( 7) sw_ddomestic = ( 8) sw_dcommunal = ( 9) sw_dstore = (10) sw_dtransport = F( 10, 246) = 6.96 Prob > F = 0.0000 Model ( 1) n_vending = ( 2) n_domestic = ( 3) n_communal = ( 4) n_store = ( 5) n_transport = ( 6) sw_nvending = ( 7) sw_ndomestic = ( 8) sw_ncommunal = ( 9) sw_nstore = (10) sw_ntransport = F( 10, 246) = 5.06 Prob > F = 0.0379 ... hữu nhà để xem xét tác động vỉa hè đến giá nhà khu phố hỗn hợp Độ rộng vỉa hè có tác động làm gia tăng giá trị nhà khoảng phần trăm Đối với nhà mặt tiền, nhà cho thuê hay nhà nằm khu tác động. .. động vỉa hè, đặc biệt nhấn mạnh đến hoạt động hộ gia đình Thêm nữa, quyền thành phố thu thuế người mua nhà mặt tiền họ tham gia hoạt động kinh doanh vỉa hè trước nhà Từ khoá: Chỉ số sống động, Giá. .. hầu giới, vỉa hè thường nơi dành cho Tuy nhiên, số học giả cho vỉa hè TP HCM không trộn lẫn với vỉa hè thành phố giới Các vỉa hè TP HCM tạo sống động thơng qua chương trình kiện, hoạt động thương