INTRODUCTION
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 do 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 do 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)
Hanoi Metro System master plan consists 8 lines with 319 kilometers of total length with high capacity for carrying passengers for Hanoi city and contributes to Hanoi economic development
Commuters in Hanoi use motorbike, buses, taxis and bicycles Recently, private cars also have been raised in Hanoi because of enhanced living condition It is cause of worse congestion and serious pollution in Hanoi Metro is part of Vietnam Ministry of Transport Master Plan, the project aims to meet the demand half of local people travelling number by public transport, minimize urban pollution, and enhance the people’s living conditions It will support to ease traffic congestion and reduce green-house effects
Hanoi metro will change urban transport in Hanoi which has approximately 8 million people are living in Opening of metro can be change the travel behavior of people who are living in Hanoi by the change of built environment
Metro Hanoi also will promote the growth of areas along the route and create thousands of direct jobs as well as many other jobs involved in the process of construction investment and operation exploitation
Figure 2: The benefit of using metro (source: hanoimetro.net.vn)
After the development of railway system, the office for lease demand in these areas increases, thus lead to the boost of the rental price It is an example shows the impact of Hanoi urban railway to the office rental price along MRT 2A corridor
Therefore, this paper aims to analyze the impacts of Hanoi MRT 2A which is one line belong to Hanoi MRT system to office rental price in Hanoi, Viet Nam
Hanoi MRT 2A is the first operating line in Hanoi There are 12 stations from Cat Linh to Ha Dong and 13km of length The original cost estimate of $553 million but has been ballooned to more than $868 million, including $670 million in loans from China
According to plan, this line was already completed and can use in 2019 but in fact, this line still in trial It is an important project in Hanoi city currently
Figure 3: Hanoi Metro Line 2A (Cat Linh – Ha Dong) (source: hanoimetro.net.vn)
Hypothesis and objective
Assume that the Hanoi metro line 2A will be increase the price of office for rent along metro corridor
Estimate a property function with cross-sectional data by hedonic approach
Analyze Hanoi metro line 2A factor to property function
First, this research contributes to quantity several factors affect to office rental price in Hanoi city
Figure 4: Two Cat Linh - Ha Dong trains leave the station in Hanoi during a trial run in September 2018 (photo by Vietnamnet)
Second, study also analyses the impacts of Hanoi metro line 2A to office rental price.
Research framework
This research includes 6 chapters This chapter focused on overview and introduction as well as the main purpose and objectives of research
The following chapters are organized as follow:
Chapter 4: GIS-based database development
LITERATURE REVIEW
Studies about property price and rent price by hedonic approach
Many studies research about factors affect to house price or land price in the world
Public transportation affects strongly to property price When we considering factor affect to property price, several factors related
Public transportation investment increases property values (Cohen et al 2007) is a conclusion which prove the transportation effects to property
Olszewski (2019) study called hedonic analysis of office and retail rent and transaction prices in Poland – data sources, methodology and empirical results has been researched about rent price in Poland by hedonic approach
The list of variables that describe the building includes such features as: sum of parking spaces, modernization, year of the modernization, total rentable area, common space, share of vacant space, number of storeys, average size of the rented premises, location in or outside of the Central Business District, share of retail space, office building class A, B and C, age of the building, dummy for the age of the building in the ranges of 5 to 10 years, from 10 to 15 and above 15 years They apply logarithms of the rent and all other continuous numerical variables, which allows them to capture the elasticity of rents relative to renting factors By hedonic method with the list of variables above this study has found a negative relationship between the size of the single premise on its rent, which can be interpreted as economy of scale A similar relationship can be found between house prices and their size
Fang (2018), Subway Opening, Traffic Accessibility, and Housing Prices: A Quantile Hedonic Analysis in Hangzhou, China found that:
The average housing price within 2 km of the station is 2.1% to 6.1% higher than those outside We also find that the impacts of the subway differ significantly across the distribution of housing prices, wherein the absolute value of estimated coefficients increased from 0.023 for the 15th quantile to 0.086 for the 95th quantile
The subway opening strengthens the capitalization effect of traffic accessibility The absolute value of price elasticity increases from 0.044 to 0.053, and the range of influence is expanded from 1500 m to 2000 m
Zhang (2016) studied about “Do Urban Rail Transit Facilities Affect Housing Prices? Evidence from China” determined the Urban Rail Transit facilities has critical role in citizen’s social activities and also markedly evaluate real estate price in Chengdu, China
Quantitatively, each 1% increase in rail transit mileage improves housing prices by 0.0233% Effects are smaller than those of some other variables such as per capita GDP, land price, real estate investment and population growth, which are recognized as fundamental determinants of housing price
Same case study in Chengdu in China, Gaolu Zou (2015) found that house price decreased with distance from Central Business District and walking distance to the nearest underground station that mean house had further distance from center and public transportation system was cheaper That means the house closer to central district and public transportation system was more expensive than the house further
By hedonic approach, this study analyzed 11 features affected to housing price in Chengdu and the results from analysis could suggest to government impose property tax better
As viewing study Kato (2018), “Impacts of urban rail investment on regional economies: Evidences from Tokyo using spatial difference-in-difference analysis”, it has shown that the urban railway investment significantly positively influenced the land price in Tokyo The land price was positively influenced by the population and employment densities The railway introduction influences the land price through anticipation of expected future development, but no indirect effect through the uplift of population density and employment density caused by the railway introduction.
Studies on land and property price in Vietnam
In Vietnam, there are several studies about house price which relate to housing development Kato and Nguyen (2009) is one of the first researchers analyzing the determinants of housing values in Hanoi by hedonic price analysis with more than 1,500 samples collected from Hanoi Authority of Finance which covered 10 urban- districts in Hanoi
Recently, study was written by Mai Chi (2018) about potential impacts of Built Environment on travel behavior and property price in Hanoi shown that density, diversity and transit accessibility had positive effect on bus ridership and property price
This research aims to providing empirical evidences about the association of built environment (BE) with travel behavior in Hanoi and investigates empirically associations of BE variables with property prices in Hanoi, particularly highlighting the associations of density, diversity and transit accessibility with property prices
Iida (2018), his study used Mai Chi research results and his data for analyzed the impacts of introducing Land Value Capturing for Mass Rapid Transit (MRTs) in Hanoi, Vietnam Iida’s research used case study in Hanoi metro line 2A
Iida (2018) found when population/employment density becomes 20-50% higher around MRT stations, capturing 2-5% of the total land price increment will bring the Internal Rate of Return (IRR) to positive By combining the office price function with a previously developed house price function, property prices after railway construction was estimated Railway revenue estimation was conducted by adopting a four step method to the origin-destination data A modal split model, a trip distribution model, and a trip generation and attraction model was developed in order to estimate the railway demand under different built environment
There are several studies analyzing the house price in Hanoi but the studies about office rental price by itself stills scanty.
Effects of Hanoi MRT to office rental price
Relationship between Built Environment (BE) and travel behavior (Mai Chi 2018)
The BE consisted of three characteristics, which called 3Ds of built environment, there are: Density, Diversity and Design in developed countries by Ewing and Cervero (2001)
Based on these three features of built environment, researches could be divided into four groups The first group’s studies focused on the impact of neighborhood design to travel behavior The second group’s studies, which focused on the relationship between land use patterns and travel demand variables, were believed to isolate the main influence factor to travel behavior The third group, which looked closer to accessibility dimension of the BE, investigated the influence between transportation network and people travel behavior And the fourth group is studies which focused mostly on a smaller scale of urban design features than the above mentioned three groups
In recent years, many researchers focused more on the impact of some specific BE variables such as development density, land use mixture (diversity) But almost studies analyzed in developed countries
The first research focus in analyzing BE in developing countries Cervero et al
(2009) studied about the influences of BE on walking and cycling in Bogotá, Colombia The BE variables are expressed 3Ds that plus two new additional Ds, which are Density, Diversity, Design, Distance to transit and Destination accessibility Unfortunately, this trend between Built Environment and travel behavior were not happen in Bogotá They found that street design was affected to walking and cycling behavior
In urban railway, 5Ds of BE are considered: Density, Diversity, Design, Distance to transit and Destination accessibility
Effects of Built Environment to property price
Effect of density on land price
Several studies have addressed the potential impact of density on housing prices A study concluded that increasing densities led to increased land value along Boston’s highway in US (Peterson 1974) but another study seen that high-density zoning associated with lower housing prices (Mullins 2001)
Study in UK found that rise of population density made high and rising levels of house prices (David Miles 2012)
Jonathan (2017) measured the residential density impacted to housing price in Singapore, this study had found increased in density causes non-trivial decreased in property values
Chang Doek Kang (2017), a study on condominium price in Seoul, the density had trivial impact on housing price
These results reflect that the effects of density on land price are inconsistent in different time and place
Effect of diversity on land price
Many researches were investigated the effect of diversity on land price The diversity was presented by land-use mix Mixed land use is defined as a mixture of commercial, residential and industrial land within a certain area (Koster et al 2012)
By presumed that mixing land uses yields socioeconomic benefits and therefore has a positive effect on housing values, Koster et al (2012) demonstrated that a diverse neighborhood is positively valued by households There were various land use types that have a positive impact on house prices, e.g., business services and leisure and there was substantial heterogeneity in willingness to pay for mixed land use
The previous study, Song and Knaap (2004) also investigated the effect of diversity on house prices in Portland in the US, found that mixing certain types of land uses with residential led to the premium in property values
But in the contrast, Li and Brown (1980) focused on the negative externalities caused by the appearance of commercial land inside residential area They concluded that housings, located near commercial areas, had lower price caused by noise, visual pollution and congestion Malaitham (2013) showed that the appearance of residential, industrial and road in land-use attributes decreased the land values, while commercial, education, vacant land and side walk would lead to price premium, case study in Thailand
Effect of accessibility on land price
Central Business District (CBD) is the most typical characteristic in accessibility
Gaolu Zou (2015) found that CBD had effected to housing price in Chengdu, China
The house located closer, priced higher
Similarly, the shorter the distance to the CBD, the higher the land values (Bartholomew, K., 2011) Study had found the positive impacts of distance of CBD on land price This trend has been seen in both developed and developing countries
Effect of distance to transit on land price
Commercial property prices increased when they located within a quarter mile of a station (Cervero and Duncan 2002) depended on type of transit And another study also concluded the shorter distance to station, the more valuable properties are (Malaitham 2013) Fang (2018) concluded that the average housing price within 2 km of the station was higher than those outside
But other study found that housings located too close to the railway tracks were suffered to price reduction
These studies said the inconsistent results about the impacts of distance to transit to property price over time and location among different kind of transit technologies and its service
Eda (2003) concluded that locational characteristics explain the spatial rent variations of the office property in Ankara to a large extent There were 3 groups in this studies included physical characteristics, lease characteristics and location of building
The negative relationship between the size of the single premise on its rent in Poland (Olszewski 2019) Study focus on rent by two characteristics which were office characteristics (city size, location, building class, age, leasable area, parking spaces, time dummy) and retail characteristics (city size, location, retail type, age, size of an average store, parking lots, time dummy)
The concept of Transit Oriented Development (TOD) is introducing for an effective urban development in line Based on the “Hanoi Capital Construction Master Plan up to 2030 with vision 2050” the TOD plan was first introduced in
2011 The MRT is believed to restructuring the Central Business District (CBD), more competitive urban development along MRT corridor, connect urban areas and sub urban areas in Hanoi Improvements of access environments to MRT stations are the keys for a successful TOD introduction The field survey was conducted in
2018 has shown the development of both commercial and residential buildings could be seen along MRT construction areas (Iida 2018)
Density characteristic is an important factor in BE analysis, population density and employment density are typical features
Population density is number of residents in the specific area Urban railway purpose to carrying passenger place to place in big city where has high population density
Hanoi MRT 2A line has been constructed from Cat Linh to Ha Dong run through high population density areas to lower population density areas
Employment density was affected by Hanoi MRT line Because of BE change, employment density inside change
This characteristic belongs to land use mixed index According to development around Hanoi MRT, commercial and residential buildings surround MRT has developed It due to change the land use mixed
MRT designs, for example route design or station design, one characteristic of BE
However, this research does not focus designs of MRT as much
METHODOLOGY
Data
Figure 5: Samples in 11 urban districts in Hanoi by GIS (created by writer)
Data collect about office rental price in Hanoi city From reputable Vietnam Real Estate website batdongsan.com.vn and some other real estate website, I already collected 578 samples from this source and some other website relate to office for lease in Hanoi
Office rental price was collected from eleven districts in Hanoi in 2019:
Ba Dinh Tay Ho Hai Ba Trung Cau Giay Dong Da Hoang Mai Thanh Xuan
Ha Dong Bac Tu Liem Nam Tu Liem
Information about collected from real estate news batdongsan.com.vn:
- Purpose for rent by the owner
Figure 6: Data samples (created by writer)
Several features of collected data (created by writer)
Figure 7: Type of building Figure 8: Age of building
Figure 9: Rent purpose Figure 10: Facilities of building
The GIS data of Hanoi used was developed by Mai Chi (2018) Based on the DWG files created by the Hanoi Urban Planning Institute, she developed a GIS-based database All geographical analysis was conducted using ArcGIS
Population density was calculated by the formula in ward-scale:
• 𝑋 𝑛𝑃𝐷,𝑛 : the population density in a buffering zone (R) for the individual n
• 𝑍 𝑃𝐷𝑘 : the average population density in a ward k
• 𝐴 𝑛𝑅 : the area of the buffering zone (R) for the individual n
Employment density Employment density is measured following in the same formulation as population:
• 𝑋 𝑛𝑃𝐷,𝑛 : the employment density in a buffering zone (R) for the individual n
• 𝑍 𝑃𝐷𝑘 : the average employment density in a ward k
• 𝐴 𝑛𝑅 : the area of the buffering zone (R) for the individual n
The entropy index measures the heterogeneity of the spatial unit, and thus a higher index value relates to a decrease in travel
• 𝑋 𝑛 𝐸𝐼,𝑅 : the entropy index of the buffering zone (R) for the individual n
• 𝑞 𝑛𝑙,𝑅 : a share of land-use category l in the buffering zone (R) for the individual n
• 𝑚 𝑛,𝑅 : the number of land-use categories observed in the buffering zone (R) for the individual n
The current public spaces are extracted from the source maps about current land use.
Method
In this study, land price was determined by hedonic method Hedonic regression use to study the impact of factors that affect housing prices Office Rental Price estimate by total of variables which affect to Office Rental Price
D is dummy variable value 0 or 1
X is independent variable ε is error term
There are two groups of variable: office characteristics and area characteristics
Leasable area Number of floors for lease Number of floors of building Age of building (old or new) Type of building (house or shop or office building)
Facilities of building (parking area, elevator…)
Rent purpose (business or office)
Population Density Employment Density Entropy Index (land use mixed) Number of bus frequency Number of schools
Number of public buildings Distance to water space Outside embankment or not Inside Old Quarter or not Distance to Central Business District (CBD)
Inside Metro Line 2A corridor or not Distance to Metro Line 2A station
Independent variables of office characteristics:
Leasable area is area for lease available, in my research it is not the area of building Because the area for lease depend on the available of building (unit: square meter)
Number of floors for lease is total of number floors of the building for rent
From this value, we can calculate total areas for lease
Number of floors of building is total floors of the building High-rise or low- rise building
Age of building is determined the use level of building By information of real estate news, determining the age of building relatively, old or new
Type of building indicates kind of building, the owner has property for lease, shop or house or office building
Variable of facilities of building is available of parking lot and elevator inside of building, available or not
Rent purpose is purpose for the leasable area Shop usually rent for business purpose, office building usually rent for office activities and house can rent for both of these purpose
Independent variables of area characteristics:
Population Density Population density is number of people in buffer zone per area unit (unit: people/hectare)
Employment Density Employment density is number of employee in buffer zone per area unit (unit: people/hectare)
Entropy Index Evaluate the land-use mixed
Number of bus frequency Bus frequency is calculated by base on Hanoi Transerco bus schedule
Number of schools Number of schools is the number of schools in buffer zone
Number of public buildings Number of public buildings is calculated by counting total number of public buildings such as hospital, department store, market…within a buffer area
Distance to water space Distance to the nearest water source (unit: meter)
Outside embankment Determine the sample inside or outside the embankment of Hong River
Inside Old Quarter or not Determine the sample inside or outside Old Quarter area
Distance to city center Distance from sample to city center which is Guom Lake in Hoan Kiem district (unit: meter)
Inside metro line 2A or not Determine the sample inside or outside metro line 2A, which counted 500 meters and 1000 meters from the core line to the sides
Distance to metro 2A station Distance from the sample to the nearest metro 2A station (unit: meter) Dependent variable:
Office rental price per month (unit: million VND)
Or office rental price per square meter (unit: VND)
GIS-BASED DATABASE DEVELOPMENT
Study area
In Vietnam, there are three levels standard zones: city/province, district/town and ward Hanoi has twelve urban districts, seventeen rural districts and one town Each urban district contains around fourteen wards
According to Hanoi’s Master Plan to 2050, Hanoi is divided into thirty subdivision urban areas Each subdivision urban area has its own plan and must follow the Hanoi’s Master Plan, known as “Hanoi Capital Construction Master Plan up to
2030 with vision 2050” These subdivision urban areas are divided based on major road’s systems in Hanoi or natural conditions One subdivision urban area contains about twenty wards and bigger than an administrative district For aggregation scale, subdivision urban area is bigger than ward and district/town and smaller than city/province
The area use to analyzing in this study as listed in dataset, eleven urban-center districts which are Hoan Kiem, Ba Dinh, Hai Ba Trung, Cau Giay, Dong Da, Hoang Mai, Thanh Xuan, Ha Dong, Tay Ho, Bac Tu Liem and Nam Tu Liem Hoan Kiem is Central Business District and Old Quarter is center MRT line 2A inside these districts
This area comprises more than 4 million people and be considered as the most developed area in Hanoi This area consists of diverse types of urban designs such as old-quarter area; French-quarter area; old handicraft villages and so on
Another reason for choosing this area as the study area is it has a wide range of land use, also this area contain new urban district Tu Liem which had large numbers of road constructions has been constructed Hoang Mai, Ha Dong, Bac Tu Liem and Nam Tu Liem are new urban districts by Hanoi expansion Therefore, infrastructure system in new area has been grown rapidly that support to economic development.
GIS-based database
Figure 11: Population density of samples with buffer scale 500 meters
GIS data with buffer 1000 meters (created by writer)
Figure 14: Employment density of samples with buffer scale 1000 meters Figure 13: Population density of samples with buffer scale 1000 meters
Number of samples inside MRT corridor in buffer 500 meter and 1000 meter by GIS
In GIS, both MRT 2A and sample use buffer zone from core to surrounded circle area with buffer 500 meter and 1000 meter
Samples inside MRT corridor in buffer 500 meter and 1000 meter were recognized by GIS
Population Density, Employment Density, Entropy Index calculated by GIS in buffer 500 meter and 1000 meter and the formula has been shown above
Number of bus, schools and public buildings counted by GIS in buffer 500 meter and 1000 meter
Distance to water space, city center and MRT 2A stations computed by GIS
BE variables Figure 15: Number of samples inside MRT corridor in buffer 500 meter and
Based on the GIS-databased, two dimensions of the BE measures are examined: density, diversity, distance to transit and destination accessibility Each dimension is measured in two different buffering distances (500m and 1000m)
Density includes population density and employment density Diversity is represented by the entropy index of land use mix Distance to transit is distance to Hanoi MRT 2A station Destination accessibility is measured by distance to Central Business District (CBD).
DATA ANALYSIS
Data Characteristics
By encoding variables, we can run data in R software as well from original collected data Population density, employment density, entropy index, number of public buildings, number of bus frequency, number of schools and samples in MRT 2A computed in buffering zone 500 meter and 1000 meter
AREA: leasable area FLs_LEASE: Number of floors for lease FLs_BUILDING: Number of floors of building AGE_BUILDING: Age of building
FACILITIES: Facilities of building (parking area, elevator…) RENT_PP: Rent purpose (business or office)
TYPE_BUILDING: Type of building (house or shop or office building) PRICE_MONTH: office rental price per month
PRICE_SQM: office rental price per square meter per month BANKMENT: inside or outside embankment of Hong River OLD_QUATER: inside or outside Old Quarter
MRT_2A_500: inside or outside MRT 2A within buffer 500 meters MRT_2A_1000: inside or outside MRT 2A within buffer 1000 meters PD_500: population density in buffer 500 meters
PD_1000: population density in buffer 1000 meters ED_500: employment density in buffer 500 meters ED_1000: employment density in buffer 1000 meters Entropy_Index_500: entropy index in buffer 500 meters Entropy_Index_1000: entropy index in buffer 1000 meters No_pb_500: Number of public buildings in buffer 500 meters No_pb_1000: Number of public buildings in buffer 1000 meters No_bus_500: Number of bus frequency in buffer 500 meters No_bus_1000: Number of bus frequency in buffer 1000 meters No_school_500: Number of school in buffer 500 meters
No_school_1000: Number of school in buffer 1000 meters DIS_WATER: distance to the nearest water space
DIS_CBD: distance to Central Business District (CBD) DIS_2A: distance to MRT 2A station
Variables Type Mean Min Max Median SD
First, leasable area varies from 14 m² to 2000 m², which represents various areas for rent in Hanoi, an average is 126.98 m²
Second, number of floors of building average is 9 and from 1-storey to 72-storeys building This represents that building for rent is various from low to high building, a shop or top high-rise building like Keangnam Hanoi Landmark Tower
Third, office rental price per square meter per month around 11 thousand VND to
270 thousand VND The average is approximately 240 thousand VND represents the market office rental price in Hanoi is pretty high
Table 2: Descriptive statics of variables in buffer 500 meters used in property price functions
Fourth, in buffering zone 1000 meters, population density average is 228.15, vary in wide range from 19.03 to 465.23 people per hectare and employment density average is 175.31, also vary in wide range from 3.54 to 466.37 people per hectare
These number reflect the monocentric characteristic in Hanoi urban areas
Fifth, in buffering zone 1000 meters, the average of entropy index is 0.31 and the range from 0 to 0.36, which means the current land-use pattern in Hanoi is mixed not really high
Sixth, number public buildings vary from 0 to 277 in buffering zone 1000 meters, the average is 56.57 It means that unbalancing distribution of public buildings in Hanoi
Variables Type Mean Min Max Median SD
DIS_2A Continuous 2485.09 94.30 10086.49 2172.06 1691.21 Table 3: Descriptive statics of variables in buffer 1000 meters used in property price functions
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
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 correlated independent variables.
*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
No AREA FLs_LEASE FLs_BUILDING AGE_BUILDINGFACILITIES RENT_PP TYPE_BUILDINGPRICE_MONTHPRICE_SQM BANKMENT OLD_QUATERMRT_2A_500 PD_500 ED_500 Entropy_Index_500 No_pb_500 No_bus_500 No_school_500DIS_WATER DIS_CBD DIS_2A AREA
Table 4: Correlations between potential explanatory variables with buffer scale 500 meters
*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
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
Table 5: Correlations between potential explanatory variables with buffer scale 1000 meters
“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
Estimation Results
PRICE_MONTH ~ AREA + TYPE_BUILDING + PD_500 + No_bus_500 + DIS_WATER + MRT_2A_500 + OLD_QUATER + BANKMENT
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 ***
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 ***
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 8 and 568 DF, p-value: < 2.2e-16
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
PRICE_MONTH ~ AREA + RENT_PP + TYPE_BUILDING + ED_500 + No_school_5
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 ***
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 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 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 8 and 568 DF, p-value: < 2.2e-16
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
PRICE_MONTH ~ AREA + RENT_PP + TYPE_BUILDING + Entropy_Index_500 +DIS_CBD + OLD_QUATER + BANKMENT
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 ***
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 7 and 569 DF, p-value: < 2.2e-16
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
PRICE_MONTH ~ AREA + TYPE_BUILDING + PD_1000 + No_bus_1000 + DIS_W ATER + MRT_2A_1000 + BANKMENT
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 ***
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 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 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 7 and 569 DF, p-value: < 2.2e-16
Significant variables in fourth model:
Leasable area, type of building, number of bus frequency in buffering zone 1000 meters are the most significant
R-squared in second model is 0.5633 The fourth model is reliable
PRICE_MONTH ~ AREA + RENT_PP + TYPE_BUILDING + ED_1000 + DIS_WATER + MRT_2A_1000 + OLD_QUATER + BANKMENT
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 ***
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 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 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 8 and 568 DF, p-value: < 2.2e-16
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:
Figure 16: Observed office rental price and estimated office rental prices
FINDING AND CONCLUSION
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,
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
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