Luận án The liveliness of sidewalks in Ho Chi Minh city and its impact on property values in mixed - Use neighborhoods

Regression analysis related estimation approaches is common in Hedonic pricing models in real property as well. The functional form of the hedonic regression equation can either be linear, semi-log, or log-log form (Chin&Chau, 2002; Malpezzi, 2003; Sirman et al., 2005; Xiao, 2017). Most of the studies showed that semi-log specification has some advantages over the linear form (Follain&Malpezzi, 1980). Sirmans et al. (2005) confirmed three recognized advantages of log linear model, this model will reduce heterogeneity, the coefficients are the interpretable as elasticities and allow the change of prices according to the difference characteristics. The log linear model is shown in this study because they are the most common. However, model form appropriate should be explored and, during empirical phase, tests for heterogeneity and normality will be carried out before accepting the log linear model. This research is expressed semi-log function as Equation 1, which indicates three types of independent variables including structural, location and neighborhood characteristics. Dependent variables and many of independent variables are logs transformed.

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nt of guidelines, policies, laws or has the effect of changing the perception of society when considering sidewalks as a public space. Contribute to built infrastructure on the surface sidewalk for improving the quality of life, for example, pedestrian walking corridor, connectivity on public services, limited private vehicles, safety and comfort. Currently, although the legislation on sidewalk ownership is clearly defined, the actual use and ownership rights are not strictly controlled by the authorities in HCMC. One of the main reasons is that the quality of the sidewalk is poor, namely, the sidewalk width is very small, mostly under 3 meter. Along with traffic jams and vehicle users must run on the sidewalk. Moreover, the equally important reason is that this thesis proposes solutions and policies for management levels and subjects in urban infrastructure management, urban planning management, and necessary in the context of in HCMC today. 132 Currently, although the legislation on sidewalk ownership is well defined, the actual use and ownership rights are not strictly controlled by the authorities. One of the main reasons is poor urban street infrastructure leading to traffic congestion and the driver on the sidewalk. Moreover, the important reason is that sidewalk management is still fragmented and local. The use of sidewalks in coexistence with different functions creates the feeling that owning a sidewalk is no longer a public good. Therefore, the management of sidewalks is also difficult in this cultural and historical context. The urban living sidewalks account for the greatest proportion of the whole traffic system, and a liveliness sidewalks will enrich social and economics activities and contribute to better social life. The sidewalk is acting as a stage people actively live in. In conclusion, a liveliness sidewak depends on the primary factors as following the comfortable physical conditions and adjacent services. When the conditions of a sidewalks can meet the needs and requests of people, the social life will be enhanced, and bring in liveliness sidewalk space. Home-owner decisions This research results can provide recent information to people planning to buy a home. Research shows the benefits and usability of sidewalks in front of the house. The level of willingness to pay for home-buyer can depend on the benefits that the sidewalk brings. The empirical results in this study have shown that the wider sidewalk, the higher the house value. This result only shows the positive relationship between the two variables. Therefore, in order to reach a deeper conclusion on policy implications, should sidewalk expansion be considered? With this result, there is not enough basis to answer, according to the author, the study can analyze more in terms of cost and benefit in extending sidewalk to be conclusive. This can a suggestion for the next research direction. 133 In addition, based on research data and research results that can show that when the sidewalk is extended by 1 meter, the house price will increase by 5 percent. In fact, if urban planning expand sidewalks, it can only expand in the direction of reducing the area of the house, because the expansion of sidewalks reduces the width of the the street is impossible. Thus, when expanding the sidewalk by 1 meter, the length of the house is reduced by 1 meter, so how much will the lot area be reduced? Based on the survey data, the author found that the common house width is from 4 to 5 meters, accounting for 45 percent. If the sidewalk is extended by 1 meter, the house area in this group decreases about 7 percent. Are home-buyers willing to accept dwindled home sizes? Home-buyers benefit from a 1 meter sidewalk extension that increase 5 percent in property value but decline 7 percent in lot size. To answer this question, it depends on the willingness to accept of home-buyers to consider the benefits and costs of this change. The liveliness index and property value Moreover, the research results also show that the higher the Liveliness index the negative impact on property value. Accordingly, in the more lively sidewalk- segments, the value of property decrease. However, the urban planners need to consider maintaining the liveliness in these sidewalks or at least should not remove it. Because lively sidewalks do not only bring benefit home-owners, particularly those used as shophouse owners, but can also help some other people earn additional income on these sidewalk-segments, among them are sidewalk vendors. In particular, sidewalks in HCMC contribute greatly and can help increase the benefit from the informal sector. If these sidewalk-segments managed well, the government can gains a large of revenue from the sidewalk. The encourage activities on sidewalks It can be seen that the meaning of activities taking place on sidewalk in HCMC is very different between day-time and night-time. This is a unique feature in HCMC 134 that is not mixed into any other place in the world. There are the large number of sidewalk-segment in mixed-use neighborhood showed marked differences. Most of activities took place in day-time, but these activities seemed to disappear in night- time, and sidewalk become quiet when houses were closed and there were no people participating in any activity. However, there are sidewalks that have the opposite effect, during the day-time, sidewalk only has a few people participate in sidewalk vending, store spillover activities, but people will spill out and fill these sidewalk with all activities in night-time as sidewalk vending, store spillover, communal, pedestrians and transportation means activities. In society, when the government does not meet the needs of the people, the private sector regulates itself. It also means that if there is no public space to take place, people can create them. In the context of an integrated planning and urban infrastructure planning that has not focused enough on household economic activities and the informal economic sector, the study provides a basis for policy recommendations in urban management. Research provides new knowledge, potentially applicable to similar economies and similar cultures. The study is based on previous studies on the role of sidewalks and public spaces in Vietnam. The study allows us to open up new research directions in Vietnam in the issue of urban infrastructure affecting economic activities. Especially, the planning of sidewalk usage and the research housing price that adjacent sidewalks. 5.3 Limitations Nevertheless, this thesis has some limitations. In terms of its data characteristics, the main sources of data used for this thesis were obtained from walk-by observation, direct observation, and survey with 283 observations. This is only a temporary acceptable level and research should be conducted at a larger sample size. In addition, during the data collection process, the study did not conduct interviews with a number of people directly involved in activities at the sidewalk-segments. Besides, this study 135 has not considered the aspects of sidewalk ownership, the purpose of using sidewalks to contribute to the official or informal economic activities in HCMC. In this study, the author has calculated the liveliness index of the sidewalk-segments based on the mixed-method. The limitation of this study is that the author has not classified in detail as the characteristics of the people participating in the activities such as age, gender, level of education, job, level of income. The level of education or income is very important because it affects the level of perception and decision of each person’s actions when using public spaces. Besides, the investment in equipment and tools during the recording process is also very limited. Researcher and participants may face many dangers when the survey during at night, so partly collected data is somewhat incomplete. LIST OF AUTHOR’S PUBLISHED PAPERS 1. Thu Nguyen (2019). 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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 Analytical category Percentage Sidewalk vendor attending to buyers - Breakfast or dinner food - Drink shop - Motorbike taxi drivers - shoes or clothing marker repairing a shoes or clothing Sidewalk vending 21% Children washing plates in front of house Domestic use 9% 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 Communal 6% 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 Store spillover 51% 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 Sidewalk occupancy of pedestrians and transportation means activities 13% 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 Appendix C: Some pictures describe activities on sidewalks Parking lots front shops/stores Walking on the streets Food shops on the sidewalk Vendors Physical characteristics Household activities Appendix D: Frequency and Percent of dummy variables Corner variable Corner | Freq. Percent Cum. ------------+----------------------------------- 0 | 257 90.81 90.81 1 | 26 9.19 100.00 ------------+----------------------------------- Total | 283 100.00 Rental variable Rental | Freq. Percent Cum. ------------+----------------------------------- 0 | 192 67.84 67.84 1 | 91 32.16 100.00 ------------+----------------------------------- Total | 283 100.00 Mixed-use variable Mixed_use | Freq. Percent Cum. ------------+----------------------------------- 0 | 171 60.42 60.42 1 | 112 39.58 100.00 ------------+----------------------------------- Total | 283 100.00 SW_qual variable SW_qual | Freq. Percent Cum. ------------+----------------------------------- 0 | 73 25.80 25.80 1 | 210 74.20 100.00 ------------+----------------------------------- Total | 283 100.00 SW_material variable SW_material | Freq. Percent Cum. ------------+----------------------------------- 0 | 23 8.13 8.13 1 | 34 12.01 20.14 2 | 140 49.47 69.61 3 | 86 30.39 100.00 ------------+----------------------------------- Total | 283 100.00 SW_fur variable SW_fur | Freq. Percent Cum. ------------+----------------------------------- 0 | 154 54.42 54.42 1 | 129 45.58 100.00 ------------+----------------------------------- Total | 283 100.00 Street variable Street | Freq. Percent Cum. ------------+----------------------------------- 0 | 239 84.45 84.45 1 | 44 15.55 100.00 ------------+----------------------------------- Total | 283 100.00 Oneway variable Oneway | Freq. Percent Cum. ------------+----------------------------------- 0 | 269 95.05 95.05 1 | 10 3.53 98.59 2 | 4 1.41 100.00 ------------+----------------------------------- Total | 283 100.00 Twoway variable Twoway | Freq. Percent Cum. ------------+----------------------------------- 0 | 235 83.04 83.04 1 | 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 1 Model 2 Model 3 Model 4 Coef. Coef. Coef. Coef. Constant -1.609*** (0.191) -1.577*** (0.199) -1.655*** (0.195) -1.552*** (0.197) Structural characteristics Lot size (square meter) 0.644*** (0.057) 0.636*** (0.056) 0.659*** (0.055) 0.643*** (0.058) Floor size (square meter) 0.201*** (0.033) 0.200*** (0.033) 0.192*** (0.033) 0.199*** (0.034) Corner (1=corner) -0.086 (0.063) -0.083 (0.062) -0.062 (0.064) -0.077 (0.067) Width (meter) 0.006 (0.011) 0.008 (0.011) 0.004 (0.010) 0.005 (0.011) Property use conditions Rental property (1 = rental property) 0.112** (0.047) 0.120*** (0.046) 0.137*** (0.045) 0.110** (0.046) Mixed-use property (1 = using house to business and shelter) -0.106** (0.046) -0.112** (0.046) -0.124** (0.048) -0.104** (0.047) Shophouse neighborhood (the number of houses) 0.047*** (0.007) 0.049*** (0.007) 0.051*** (0.008) 0.050*** (0.007) Location characteristics Distance to CBD (kilometer) -0.058*** (0.008) -0.058*** (0.009) -0.053*** (0.009) -0.056*** (0.009) Distance to School (kilometer) -0.062 (0.038) -0.069* (0.040) -0.047 (0.038) -0.069* (0.040) Distance to School_squared (square kilometer) 0.016*** (0.005) 0.017*** (0.005) 0.013*** (0.005) 0.016*** (0.005) Distance to Hospital (kilometer) 0.181*** (0.055) 0.198*** (0.057) 0.176*** (0.054) 0.181*** (0.057) Distance to Hospital_squared (square kilometer) -0.037*** (0.011) -0.039*** (0.011) -0.036*** (0.010) -0.036*** (0.011) Distance to Market (kilometer) 0.039 (0.049) 0.019 (0.051) 0.026 (0.049) 0.034 (0.049) Sidewalk Sidewalk width (meter) 0.047** (0.021) 0.061** (0.025) 0.047* (0.027) 0.047* (0.026) Distance to sidewalk (meter) -0.002*** (0.000) -0.002*** (0.000) -0.002*** (0.000) -0.002*** (0.000) Sidewalk surface (1 = paved sidewalk) 0.104* (0.062) 0.115* (0.062) 0.116* (0.066) 0.120* (0.066) Sidewalk width*surface -0.037 (0.027) -0.042 (0.028) -0.036 (0.029) -0.040 (0.031) Sidewalk furniture (1 = sidewalk has furniture) 0.139*** (0.037) 0.137*** (0.036) 0.132*** (0.035) 0.138*** (0.037) Street width (meter) 0.086 (0.052) 0.096* (0.053) 0.074 (0.055) 0.082 (0.055) Liveliness index Liveliness index (number) -0.029* (0.015) Day-time activities D sidewalk vending (people) -0.003 (0.017) D domestic use (people) -0.056* (0.031) D communal (people) -0.059*** (0.012) D spillover store (people) 0.014 (0.013) D transportation (people) -0.025 (0.026) SW width*D sidewalk vending -0.002 (0.006) SW width*D domestic use 0.030** (0.015) SW width*D communal 0.006 (0.004) SW width*D spillover store -0.008* (0.004) SW width*D transportation 0.015* (0.008) Night-time activities N sidewalk vending (people) 0.005 (0.014) N domestic use (people) -0.019 (0.024) N communal (people) -0.014 (0.033) N spillover store (people) -0.005 (0.004) N transportation (people) -0.043** (0.020) SW width*N sidewalk vending -0.001 (0.003) SW width*N domestic use 0.004 (0.010) SW width*N communal 0.001 (0.013) SW width*N spillover store 0.001 (0.000) SW width*N transportation 0.016* (0.009) Districts (Base: Binh Tan district) Distrist 1 0.340*** (0.091) 0.347*** (0.093) 0.365*** (0.090) 0.347*** (0.092) Distrist 3 0.357*** (0.105) 0.330*** (0.108) 0.391*** (0.103) 0.339*** (0.112) Distrist 5 0.299*** (0.089) 0.325*** (0.085) 0.402*** (0.079) 0.298*** (0.090) Distrist 10 0.291*** (0.074) 0.288*** (0.075) 0.323*** (0.075) 0.288*** (0.077) Distrist 11 0.249*** (0.073) 0.250*** (0.073) 0.254*** (0.072) 0.260*** (0.076) Phu Nhuan Distrist 0.331*** (0.068) 0.340*** (0.069) 0.335*** (0.072) 0.351*** (0.071) Tan Binh Distrist 0.244*** (0.060) 0.259*** (0.062) 0.261*** (0.059) 0.236*** (0.060) R_squared 0.8488 0.8518 0.8625 0.8523 F (Prob > F) 47.85 (0.000) 48.31 (0.000) 49.34 (0.000) 36.26 (0.000) Mean VIF 4.13 4.07 4.72 4.85 AIC 102.04 98.34 95.24 115.38 BIC 200.47 200.41 230.12 250.26 F3. F test Model 2 (1) livelinessindex = 0 F( 1, 255) = 3.59 Prob > F = 0.0592 Model 3 ( 1) d_vending = 0 ( 2) d_domestic = 0 ( 3) d_communal = 0 ( 4) d_store = 0 ( 5) d_transport = 0 ( 6) sw_dvending = 0 ( 7) sw_ddomestic = 0 ( 8) sw_dcommunal = 0 ( 9) sw_dstore = 0 (10) sw_dtransport = 0 F( 10, 246) = 6.96 Prob > F = 0.0000 Model 4 ( 1) n_vending = 0 ( 2) n_domestic = 0 ( 3) n_communal = 0 ( 4) n_store = 0 ( 5) n_transport = 0 ( 6) sw_nvending = 0 ( 7) sw_ndomestic = 0 ( 8) sw_ncommunal = 0 ( 9) sw_nstore = 0 (10) sw_ntransport = 0 F( 10, 246) = 5.06 Prob > F = 0.0379

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