Impacts of transportation investment to property price in ha noi viet nam

VIETNAM NATIONAL UNIVERSITY, HANOI VIETNAM JAPAN UNIVERSITY NGUYEN THI THU HA IMPACTS OF TRANSPORTATION INVESTMENT TO PROPERTY PRICE IN HANOI, VIETNAM MASTER’S THESIS Hanoi, 2019 HANOI NATIONAL UNIVERSITY VIETNAM JAPAN UNIVERSITY NGUYEN THI THU HA IMPACTS OF TRANSPORTATION INVESTMENT TO PROPERTY PRICE IN HANOI, VIETNAM MAJOR: MASTER OF INFRASTRUCTURE ENGINEERING RESEARCH SUPERVISORS: Prof Hironori Kato Dr Phan Le Binh Hanoi, 2019 CONTENTS ABSTRACT .5 ACKNOWLEDGEMENT CHAPTER 1: INTRODUCTION .7 1.1 Introduction 1.2 Hypothesis and objective 11 1.3 Research framework 12 CHAPTER 2: LITERATURE REVIEW 13 2.1 Studies about property price and rent price by hedonic approach 13 2.2 Studies on land and property price in Vietnam 15 2.3 Effects of Hanoi MRT to office rental price 16 CHAPTER 3: METHODOLOGY 21 3.1 Data 21 3.2 Method 26 CHAPTER 4: GIS-BASED DATABASE DEVELOPMENT 30 4.1 Study area 30 4.2 GIS-based database 31 CHAPTER 5: DATA ANALYSIS 35 5.1 Data Characteristics 35 5.2 Estimation Results 42 CHAPTER 6: FINDING AND CONCLUSION .49 6.1 Finding and conclusion 49 REFERENCES .51 LIST OF FIGURES Figure 1: Hanoi MRT Master Plan 2030, 2050 vision…………………………….8 Figure 2: The benefit of using metro………………………………………………9 Figure 3: Hanoi Metro Line 2A (Cat Linh – Ha Dong)………………………… 10 Figure 4: Two Cat Linh - Ha Dong trains leave the station in Hanoi during a trial run in September 2018…………………………………………………………… 11 Figure 5: Samples in 11 urban districts in Hanoi by GIS………………………….21 Figure 6: Data samples………………………………………………………… 23 Figure 7: Type of building……………………………………………………… 24 Figure 8: Age of building………………………………………………………….24 Figure 9: Rent purpose…………………………………………………………….24 Figure 10: Facilities of building………………………………………………… 24 Figure 11: Population density of samples with buffer scale 500 meters………… 31 Figure 12: Employment density of samples with buffer scale 500 meters……… 31 Figure 13: Population density of samples with buffer scale 1000 meters…………32 Figure 14: Employment density of samples with buffer scale 1000 meters……….32 Figure 15: Number of samples inside MRT corridor in buffer 500 meter and 1000 meter……………………………………………………………………………….33 Figure 16: Observed office rental price and estimated office rental prices……… 48 LIST OF TABLES Table 1: Catalogue of variables……………………………………………………27 Table 2: Descriptive statics of variables in buffer 500 meters used in property price functions………………………………………………………………………… 37 Table 3: Descriptive statics of variables in buffer 1000 meters used in property price functions………………………………………………………………………… 38 Table 4: Correlations between potential explanatory variables with buffer scale 500 meters………………………………………………………………………………40 Table 5: Correlations between potential explanatory variables with buffer scale 1000 meters……………………………………………………………………… 41 LIST OF ABBREVIATIONS MRT Mass Transit Rapid MRT 2A Hanoi Mass Transit Rapid Line 2A BE Built Environment TOD Transit Oriented Development IRR Internal Rate Return CBD Central Business District GIS Geographic Information System ABSTRACT Viet Nam is a developing country which is similar to many developing countries are experiencing rapid growth Along with these developments, there are many existed problems such as traffic congestion, water pollution, air pollution, greenhouse gas emissions, energy…etc Transportation in Vietnam is improving rapidly both quantity and quality Road traffic is growing fast but the major roads are dangerous and slow to travel on due to outdated design and an inappropriate traffic mix Construction of expressways has accelerated Air travel is also important for long distance travel in Viet Nam Metro systems are under construction in the two metropolises of Vietnam, one in Hanoi and another in Ho Chi Minh City Metro in Hanoi is a solution to solving traffic issues However, it still has some financial problems according from financial analysis of this project By estimating the change of office rental price, this research aims to analyze the impacts of Hanoi metro line 2A to property price in Hanoi by hedonic method This study has found public transportation as bus frequency has positive significant impact to office rental price in Hanoi But Hanoi metro line 2A has negative significant impact at the moment There are several reasons Such as, Hanoi metro line 2A has not operated yet at the moment, the noise has bad influenced and reputation of project has not influenced to surrounded property Hanoi is the city which has high motorbike dependent, currently number of private car has been increased and bus system are the most choices to travel for people in Hanoi now And large area, office building, office in Old Quarter (Central Business District) and high employment density have positive impacts to office rental price in Hanoi city, Vietnam ACKNOWLEDGEMENT Firstly, I would like thank to my lecturers in Master of Infrastructure Engineering Program in Viet Nam Japan University for their inspiring in studying I have had meaningful studying time for two years with my master program I would like to give my special thank and deep attitude to my supervisors, Professor Hironori Kato and Dr Phan Le Binh for their advices and support in research work I also would like to thank Ms Nguyen Thi Mai Chi for her guidance for my research I would like thank all of my classmates for helping in studying This is my big support in my master program I have much helps from them during my master program I sincerely thank to all members in Urban Planning Laboratory in Kanazawa University for one semester as exchange student I have learnt from many people from your acknowledge and their research work Thanks for Viet Nam Japan University for all supports of my study for two years of my master program I had learnt much academic knowledge and took part in many seminars which held by Viet Nam Japan University, also I had joined many school’s activities which were good memories with me My deep thank to my Japanese and Vietnamese lecturers in Viet Nam Japan University for all time in the school Thanks for Kanazawa University for one semester-exchange program that I had done Thanks for my Japanese teacher and international students I had in this semester in Kanazawa University Finally, I would like to send my love and gratitude to my family who always love, support and encourage me every time CHAPTER 1: INTRODUCTION 1.1 Introduction In developing countries, transportation systems are directly affected to economic growth Because of affected infrastructure support for trading, it leads to developing the economy Railway is one kind of infrastructure which contributes to develop national economic, such kind of social benefit it brings to, many people will have better traveling With many developed countries, for example like Japan, many company’s offices usually change the location near to metro stations according to its convenient when urban railway has been used It is the cause of the increase in office rental prices in the area around the metro line But with a developing country like Vietnam which high motorbike dependent, people not much rely on public transportation So the trend of change office location near to metro line in Hanoi will have a little different with a developed country like Japan But it still affect to the rental price along metro line corridor Urban infrastructure system Hanoi City is a whole and covers many areas of both economic infrastructure and social infrastructure Within the scope of the study public transportation, railway network in Hanoi brings to economic profit and social profit for citizens who are living in the city In general, in recent years, the urban infrastructure system of Hanoi City has changed markedly, gradually changing the urban appearance towards synchronous, modern and increasingly important role in the city's socio-economic development However, the infrastructure system still has many weaknesses and shortcomings that not meet the needs of the people, the socio-economic development requirements and requirements of a civilized and modern city with stature Hanoi Urban Metro is a rail system has been developed in Hanoi, Vietnam This project is part of an integrated development program for urban transport in Hanoi Figure 1: Hanoi MRT Master Plan 2030, 2050 vision (source: hanoimetro.net.vn) Figure Seventh, the average of number of bus frequency is 29.94 and the average of number of schools is 28.11, this numbers reflect property in our dataset have accessibility to bus service and near several schools even though unbalancing between different property Eighth, the average of distance to water is 650.04 means that local people prefer working near lakes in Hanoi Ninth, distance to CBD varies from 159.26 m to 18548.36 m, with the average of 5109.84 m The maximum distance about 18 km means that the urbanized area is a little bit compact in Hanoi city Tenth, the average of distance to MRT 2A is 2485.09 m which is from 94.30 m to 10086.49 The minimum distance about 0.1 km means that properties has been located close to MRT 2A Correlations between variables Because of exist relationships between variables, we need to check correlation between variables and avoid the correlated independent variables A code has been run by R software could not use 39 correlated independent variables Table 4: Correlations between potential explanatory variables with buffer scale 500 meters No AREA FLs_LEASE FLs_BUILDING AGE_BUILDING FACILITIES RENT_PP TYPE_BUILDING PRICE_MONTH PRICE_SQM BANKMENT OLD_QUATER MRT_2A_500 PD_500 ED_500 Entropy_Index_500 No_pb_500 No_bus_500 No_school_500 DIS_WATER DIS_CBD DIS_2A AREA FLs_LEASE FLs_BUILDING AGE_BUILDINGFACILITIES RENT_PP -0.151116054 0.371228792 0.308078421 0.208403318 0.033239257 0.299216442 0.72115409 0.01215093 -0.015725572 -0.066962032 0.005222457 -0.13934726 -0.033762517 -0.082986202 -0.09930474 -0.05205795 -0.138631416 0.059102289 0.128066359 0.074232175 -0.322702146 -0.245922961 -0.34685564 -0.042906254 -0.50686446 0.147609884 -0.327329266 0.025675807 0.015568381 -0.015891621 -0.058774855 -0.064969642 -0.009338406 -0.057215073 -0.1263049 -0.0254558 0.069790499 0.086092743 0.021000502 0.532234416 0.47443835 0.220974252 0.62679725 0.199042324 0.096031743 -0.070711188 -0.154198214 -0.02029605 -0.179529818 -0.109991671 -0.020262772 -0.210128555 -0.023057788 -0.243147507 0.056581847 0.180949748 0.001857139 0.421348892 0.213663356 0.534849851 0.218952731 0.066885999 0.004408669 -0.134250265 -0.008886409 -0.118799171 -0.068218614 0.023330287 -0.197963356 -0.073176695 -0.249645425 0.030076989 0.146692973 -0.016326749 0.343339098 0.614140492 0.073101036 0.039214561 -0.08701437 -0.18649024 0.002718944 -0.06017234 -0.04688668 0.018314172 -0.15725865 0.020164497 -0.14483502 0.003099308 0.098573283 -0.03397613 TYPE_BUILDINGPRICE_MONTHPRICE_SQM BANKMENT OLD_QUATERMRT_2A_500 PD_500 0.29108421 -0.10144609 0.111450863 -0.30278068 0.109267827 0.278608206 -0.05218802 -0.070362972 0.054261494 -0.30769145 -0.154794747 0.10739988 0.0598162 0.019085879 -0.052927289 -0.15641827 -0.044477028 -0.006181595 -0.12879465 0.025325526 0.205381712 0.01248682 0.061295284 0.045232361 -0.32489056 -0.093561066 0.186745391 -0.08010886 0.115766361 0.076027463 -0.24966202 -0.1247585 0.052154664 0.01931241 -0.011383408 -0.03195826 0.22248974 0.047287265 -0.110477877 0.00012879 -0.024089887 0.015852961 -0.001504431 0.421453228 -0.100727076 0.257157204 0.396925849 0.153386157 0.500115086 0.274213118 0.328062204 -0.173618524 -0.421878157 -0.078163963 0.050405979 -0.04050486 -0.0217377 -0.06305071 -0.01250553 0.013860751 -0.05008419 -0.06564357 0.017621721 -0.1133825 0.110164116 -0.08098342 0.260148318 0.34953342 0.077134012 0.558989813 0.141788062 0.379157266 -0.06094221 -0.37096982 -0.00324368 ED_500 0.182957215 0.09753622 0.545580407 0.086339556 0.307853068 0.40595967 -0.10056609 0.459488074 0.65583683 0.00693687 0.351052958 0.59680482 0.045844467 0.662925272 0.48959681 0.06702959 -0.3214692 -0.24313565 0.034413972 -0.69118573 -0.72688167 -0.44411781 -0.44444162 -0.4722419 No_pb_500 No_bus_500 No_school_500DIS_WATER DIS_CBD DIS_2A Entropy_Index_500 0.26896757 0.41033455 0.50288127 0.21761191 0.718688813 -0.36715188 -0.24495751 -0.37083562 -0.74018698 -0.26305817 -0.08087658 0.368712269 -0.23008337 -0.3140926 -0.5233683 -0.69788397 0.53731283 -0.26328618 -0.19437687 0.05172743 0.294313 *correlation in range -0.4 to 0.4 means there is no relationship between two variables *correlation less than -0.4 and bigger than 0.4 means there exists a relationship between two variables 40 Table 5: Correlations between potential explanatory variables with buffer scale 1000 meters No AREA FLs_LEASEFLs_BUILDING AGE_BUILDING FACILITIESRENT_PP TYPE_BUILDING PRICE_MONTH PRICE_SQMBANKMENTOLD_QUATER MRT_2A_1000 PD_1000 ED_1000 Entropy_Index_1000 No_pb_1000 No_bus_1000 No_school_1000 DIS_WATERDIS_CBD DIS_2A AREA FLs_LEASE -0.15112 FLs_BUILDING 0.371229 -0.3227 AGE_BUILDING 0.308078 -0.24592 0.532234 FACILITIES 0.208403 -0.34686 0.474438 0.421349 RENT_PP 0.033239 -0.04291 0.220974 0.213663 0.343339 TYPE_BUILDING 0.299216 -0.50686 0.626797 0.53485 0.61414 0.291084 PRICE_MONTH 0.721154 0.14761 0.199042 0.218953 0.073101 -0.10145 0.111451 PRICE_SQM 0.012151 -0.32733 0.096032 0.066886 0.039215 -0.30278 0.109268 0.278608 BANKMENT -0.01573 0.025676 -0.07071 0.004409 -0.08701 -0.05219 -0.07036 0.054261 -0.0015 OLD_QUATER -0.06696 0.015568 -0.1542 -0.13425 -0.18649 -0.30769 -0.15479 0.1074 0.421453 0.050406 MRT_2A_1000 -0.01032 0.028314 -0.04331 0.050035 0.06882 0.110439 -0.01068 -0.03123 -0.10443 -0.06598 -0.13191 PD_1000 -0.13668 -0.06419 -0.16319 -0.09879 -0.0296 -0.11648 -0.02163 0.01046 0.251544 -0.03326 0.170857 0.303002 ED_1000 -0.08506 -0.08207 -0.12247 -0.09214 -0.04844 -0.15627 0.019767 0.144182 0.404429 -0.02277 0.354075 0.215043 0.702437 Entropy_Index_1000 -0.07532 -0.0599 -0.00954 -0.00781 0.076389 -0.00837 0.108686 0.039732 0.183806 -0.12238 0.047648 0.189694 0.529647 0.564615 No_pb_1000 -0.08588 -0.0653 -0.19172 -0.19508 -0.15166 -0.30195 -0.06997 0.189691 0.490472 0.061488 0.520825 -0.15514 0.460438 0.760073 0.35713 No_bus_1000 -0.09797 -0.07479 -0.08113 -0.0692 0.009167 -0.0763 0.076889 0.088094 0.287767 -0.05756 0.144324 0.067904 0.604223 0.823322 0.68795 0.619813 No_school_1000 -0.13457 -0.05559 -0.21652 -0.20464 -0.11915 -0.23453 -0.09492 0.085074 0.389051 0.002595 0.348821 0.023539 0.76355 0.703046 0.378865 0.788161 0.603678 DIS_WATER 0.059102 0.06979 0.056582 0.030077 0.003099 0.019312 -0.01138 -0.03196 -0.17362 0.017622 -0.06094 0.074672 -0.3676 -0.27018 -0.3669 -0.26927 -0.27151 -0.33958 DIS_CBD 0.128066 0.086093 0.18095 0.146693 0.098573 0.22249 0.047287 -0.11048 -0.42188 -0.11338 -0.37097 0.053456 -0.74772 -0.79718 -0.43334 -0.7774 -0.6419 -0.81836 0.537313 DIS_2A 0.074232 0.021001 0.001857 -0.01633 -0.03398 0.000129 -0.02409 0.015853 -0.07816 0.110164 -0.00324 -0.64569 -0.52703 -0.54321 -0.42288 -0.11464 -0.36434 -0.2731 0.051727 0.294313 *correlation in range -0.4 to 0.4 means there is no relationship between two variables *correlation less than -0.4 an bigger than 0.4 means there exists a relationship between two variables 41 “Type of building” has positive correlation with “Floors of building”, “Age of building” and “Facilities” but negative correlation with “Floors for lease” “Population density” and “Employment density” have highly positive correlation each other “Population density” has correlation with “Entropy Index” in buffer 1000 meters but no correlation in buffer 500 meters “Distance to CBD” has negative correlations with “Population density” and “Employment density” 5.2 Estimation Results First model: Assume: PRICE_MONTH ~ AREA + TYPE_BUILDING + PD_500 DIS_WATER + MRT_2A_500 + OLD_QUATER + BANKMENT We have: Residuals: Min 1Q Median -205.65 -17.31 -6.17 3Q Max 6.77 393.66 Coefficients: Estimate Std Error t value Pr(>|t|) (Intercept) -0.154047 6.510615 -0.024 0.981131 AREA 0.297412 0.011175 26.614 < 2e-16 *** TYPE_BUILDING -14.250406 3.890928 -3.662 0.000273 *** PD_500 0.018210 0.018522 0.983 0.325951 No_bus_500 1.565949 0.488085 3.208 0.001410 ** DIS_WATER -0.004335 0.003412 -1.271 0.204392 MRT_2A_500 -9.690856 5.812228 -1.667 0.096002 OLD_QUATER 34.703516 9.073087 3.825 0.000145 *** BANKMENT 34.504813 16.571527 2.082 0.037774 * Signif Codes: ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ Residual standard error: 43.35 on 568 degrees of freedom Multiple R-squared: 0.5719, Adjusted R-squared: 0.5659 F-statistic: 94.85 on and 568 DF, p-value: < 2.2e-16 42 + No_bus_500 + Significant variables in first model: Leasable area, type of building, inside Old Quarter or not are the most significant Number of bus frequency, samples in MRT 2A corridor buffer 500 meters inside embankment or not R-square of this model is 0.5659 The first model is reliable Second model: Assume: PRICE_MONTH ~ AREA + RENT_PP + TYPE_BUILDING + ED_500 + No_school_5 00 + MRT_2A_500 + OLD_QUATER + BANKMENT We have: Residuals: Min 1Q Median -191.98 -16.69 -4.49 3Q Max 8.72 375.10 Coefficients: Estimate Std Error t value Pr(>|t|) (Intercept) 0.04635 6.38638 0.007 0.9942 AREA 0.29420 0.01076 27.350 < 2e-16 *** RENT_PP -9.22544 5.74288 -1.606 0.1087 TYPE_BUILDING -12.48283 3.86220 -3.232 0.0013 ** ED_500 0.11469 0.01685 6.808 2.52e-11 *** No_school_500 0.22424 0.30670 0.731 0.4650 MRT_2A_500 -13.97605 5.47009 -2.555 0.0109 * OLD_QUATER 11.46750 9.47621 1.210 0.2267 BANKMENT 40.59166 16.03992 2.531 0.0117 * Signif Codes: ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ Residual standard error: 41.76 on 568 degrees of freedom Multiple R-squared: 0.6026, Adjusted R-squared: 0.5971 F-statistic: 107.7 on and 568 DF, p-value: < 2.2e-16 43 Significant variables in second model: Leasable area and employment density in buffering zone 500 meters are the most significant variables Type of building, samples in MRT 2A corridor buffer 500 meter, inside embankment or not R-squared in second model is 0.5971 The second model is high reliable Third model: Assume: PRICE_MONTH ~ AREA + RENT_PP + TYPE_BUILDING + Entropy_Index_500 +DIS_CBD + OLD_QUATER + BANKMENT We have: Residuals: Min 1Q Median -197.05 -17.85 -5.86 3Q Max 7.46 382.45 Coefficients: Estimate Std Error t value Pr(>|t|) (Intercept) -10.685398 26.835478 -0.398 0.69065 AREA 0.299233 0.011032 27.123 < 2e-16 *** RENT_PP -8.945543 5.867352 -1.525 0.12791 TYPE_BUILDING -12.027027 3.947063 -3.047 0.00242 ** Entropy_Index_500 256.506584 130.636327 1.964 0.05007 DIS_CBD -0.003554 0.000795 -4.471 9.42e-06 *** OLD_QUATER 22.366830 9.394807 2.381 0.01760 * BANKMENT 23.159213 16.399712 1.412 0.15845 Signif codes: ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ Residual standard error: 42.69 on 569 degrees of freedom Multiple R-squared: 0.5841, Adjusted R-squared: 0.579 F-statistic: 114.2 on and 569 DF, p-value: < 2.2e-16 44 Significant variables in third model: Leasable area and distance to Central Business District in buffering zone 500 meters are the most significant Type of building, entropy index, inside Old Quarter or not R-squared in second model is 0.579 The third model is reliable Fourth model Assume: PRICE_MONTH ~ AREA + TYPE_BUILDING + PD_1000 + No_bus_1000 + DIS_W ATER + MRT_2A_1000 + BANKMENT We have: Residuals: Min 1Q Median -206.07 -17.66 -5.67 3Q Max 7.18 384.87 Coefficients: Estimate Std Error t value Pr(>|t|) (Intercept) -10.124921 6.966905 -1.453 0.1467 AREA 0.300157 0.011219 26.755 < 2e-16 *** TYPE_BUILDING -17.185564 3.845243 -4.469 9.47e-06 *** PD_1000 0.013140 0.025979 0.506 0.6132 No_bus_1000 0.866188 0.189234 4.577 5.79e-06 *** DIS_WATER -0.003123 0.003502 -0.892 0.3728 MRT_2A_1000 -5.255396 4.476379 -1.174 0.2409 BANKMENT 39.320771 16.635924 2.364 0.0184 * Signif Codes: ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ Residual standard error: 43.48 on 569 degrees of freedom Multiple R-squared: 0.5686, Adjusted R-squared: 0.5633 F-statistic: 107.1 on and 569 DF, p-value: < 2.2e-16 45 Significant variables in fourth model: Leasable area, type of building, number of bus frequency in buffering zone 1000 meters are the most significant Inside embankment or not R-squared in second model is 0.5633 The fourth model is reliable Fifth model Assume: PRICE_MONTH ~ AREA + RENT_PP + TYPE_BUILDING + ED_1000 + DIS_WATER + MRT_2A_1000 + OLD_QUATER + BANKMENT We have: Residuals: Min 1Q Median -193.76 -17.40 -5.14 3Q Max 7.39 380.81 Coefficients: Estimate Std Error t value Pr(>|t|) (Intercept) 1.750903 7.207720 0.243 0.808155 AREA 0.297514 0.010914 27.259 < 2e-16 *** RENT_PP -8.492392 5.822075 -1.459 0.145213 TYPE_BUILDING -13.225671 3.944047 -3.353 0.000852 *** ED_1000 0.127262 0.020927 6.081 2.19e-09 *** DIS_WATER -0.002299 0.003269 -0.703 0.482117 MRT_2A_1000 -7.357432 4.319166 -1.703 0.089033 OLD_QUATER 15.986411 9.567900 1.671 0.095305 BANKMENT 33.653446 16.225543 2.074 0.038520 * Signif Codes: ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ Residual standard error: 42.42 on 568 degrees of freedom Multiple R-squared: 0.59, Adjusted R-squared: 0.5843 F-statistic: 102.2 on and 568 DF, p-value: < 2.2e-16 46 Significant variables in fifth model: Leasable area, type of building, employment density in buffering zone 1000 meters are the most significant Sample in MRT 2A corridor in buffering zone 1000 meters, inside Old Quarter or not and inside embankment or not R-squared in second model is 0.5843 The fifth model is high reliable By the estimated coefficient results of five models above, we have estimation results about office rental price of all samples And combining with observation results which is office rental price of all samples collected in website batdongsan.com.vn, we illustrate the relationship two results by this figure: 47 Figure 16: Observed office rental price and estimated office rental prices (created by writer) 48 CHAPTER 6: FINDING AND CONCLUSION 6.1 Finding and conclusion First of all, the estimation results by R model show that leasable area has significant positive impact to office rental price while type of building has significant negative impact to office rental price in Hanoi That means larger area for rent, higher price and office building has better office rental price than house or shop for rent Normally, building was built for office for lease purpose has better price than some house for office for lease Secondly, property inside Old Quarter (CBD) has much high office rental price because variable Old Quarter has high positive impact to office rental price Distance to Central Business District (CBD) has negative impact that mean property has longer distance to CBD will have lower office rental price Third, outside embankment also have positive impact to office rental price, property outside embankment still has good office rental price Fourth, employment density has significant positive effect to office rental price which prove that office for lease in higher employment density has higher price Because usually property will locate in place where there are many employee Fifth, office rental price has effect by entropy index which represents the land-use mixed has positive impact to office rental price Property in place where is land-use mixed area has good price Sixth, number of bus frequency has significant positive impact to office rental price It means that public transportation has great effect to office rental price Because public transportation is way which employee use to travel from their own place to working place daily with reasonable price It proves that transportation has important role in people life, this is reason why transportation directly effect to property price 49 Finally, MRT 2A has negative impact to office rental price in Hanoi currently MRT 2A has not operated yet at the moment There are several reasons why MRT 2A has negative impact to office rental price In constructing time of MRT 2A, office avoid to locate near MRT 2A construction because of the noise And site view is not a good choice for office location At the moment, people who are living in Hanoi prefer to choose private vehicle like motorbike and car or using bus to traveling And not really good quality and reputation of MRT 2A is not due to increase the office rental price right now But when MRT 2A has been operated, this trend could change Because when MRT 2A is a choice to travel for people in Hanoi, the impact of MRT 2A to office rental price could change It raises a question for further research to figure out how MRT 2A impacts to office rental price when it was already operated 50 REFERENCES Cohen et al 2007 The impacts of transportation infrastructure on property values: A higher-order spatial econometrics approach pp.1-3 Koster et al 2012 The impact of mixed land use on residential property values Journal of Regional Science Vol 52 No.5 pp.733-761 Gaolu Zou 2015 The effect of central business district on house price in Chengdu Metropolitan Area: A hedonic Approach International 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