Impact of infrastructure investment on land values: A case study in Hanoi, Vietnam

IMPACT OF INFRASTRUCTURE INVESTMENT ON LAND

VALUES: A CASE STUDY IN HANOI, VIETNAM

MSc. Nguyen Thanh Lan

nguyenthanhlan@neu.edu.vn

Faculty of Real estate and Resources Economics,

National Economics Universities, Hanoi, Vietnam

Abstract

This study is to examine an impact of infrastructure investment on change of land

values, particularly public road transportation in urban areas. Based on principles of the

land rent theory, developed by Alonso (1964) and Muth (1969), this paper carried out a

surveyof landusers who use plots of land or ownresidential property inseveralnewurban

areas in Hanoi -Vietnam, as a case study. Our findings reveal that Centre distance,

Mobility timing and Development opportunity have positive impact on land values, while

Land use changes and Accessible amenity have no influence.

Keywords: infrastructure investment, land use change, land value, urban land.

1. Introduction

Infrastructure investment has been a subject of many studies for some time,

like urban & regional planning, transport economics, land economics or public

management and so on (Button, 1998; Gramlich, 1994). Especially, transport

infrastructure is not only considered as one of the key elements for the economic

growth and development, but also a crucial role in achieving the objectives job

creation. The transport infrastructure has an impact on socioeconomic development

(Helling, 1997). For the long-term economic benefits of investments in

transportation, it might be organized into six groups, depended on some the types of

benefit being measured, namely: output; productivity; production costs; income,

property values, employment, and real wages; rate of return; and noncommercial

travel time, according to Bhatta and Drennan (2003). However, positive impact of

infrastructure investment on land values is a heart of this study.

In general, there was a bulky study to impacts of infrastructure investment on

land values up to date. Mulley and et. al (2016) argued that studies related to the

influences of infrastructures on land values being often qualitative research in earlier

times, from 1970s. The majority of papers that assess the relationship between some

kinds of transport infrastructure, a type of technical infrastructure, such as rail transit

road etc., and land value, finding an increase in land value, while others have focus

on impacts of social infrastructures likely parks, walked areas or green areas etc. on

land values.

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In recent years, after over the year 2000, there were a number of quantitative

studies aimed to identify and measure changing value of land or properties, which had

accessibility to different destinations around new investment infrastructures (Nguyen

Thanh Lan, 2018). In addition, there are some papers using meta-analysis methodology

to study relationship between infrastructure investment and land value, which are

summarized in table 1 Specifically, (RICS, 2002), Debrezion et al (2007) and Jeffery J

Smith and Gihring (2006) together provide major reviews of over 100 international

studies on the influence of public transport on property (land and housing) values.

Table 1: Some papers using meta-analysis methodology to study impact of

infrastructure investment on land value

Sample size

No

Authors

Year

Findings

(papers)

Accessibility to infrastructure had

decreased land value (about 6 -7%),

while changing land use purposes made

a gradual amount of land values.

1

Vessali (1996)

1996

37

- There has significant change in value

uplift of land and buildings

- A wide range of factors should study

impacts on land values such as

accessibility, land ownership regime

location, development density etc.

Infrastructure investment has positive

impacts on land value thanks to changing

of accessibility;

The impacts of rail system on value uplift

of residential and commercial properties

are different

2

RICS (2002)

2002

150

Jeffery J Smith and

Gihring (2006)

3

4

2006

2007

76

73

Debrezion et al.

(2007)

Mohammad,

Graham, Melo, and 2013

Anderson (2013)

Infrastructure investment made greater

value uplift of vacant land than real estate

(buildings)

5

102

Investment rail system contributed in land

value uplift and this paper focus on impacts

of TOD on land use and land value.

Higgins

Kanaroglou (2016)

and

6

7

2016

130

17

Saxe and Miller

(2016)

TOD and land use planning can make

greater effects on land value.

Infrastructure investment may bring

many prospects of economic benefits

that effects on land value uplift in

adjacent areas. However, land value may

increase or decrease.

Jeffery J. Smith,

Gihring, and Litman 2017 138

(2017)

8

Source: Nguyen Thanh Lan (2018)

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Generally, new transport infrastructure may increase land values due to

improved accessibility and possible agglomeration benefits; however, the findings

range significantly from place to place. It is noticeable that the findings may vary

depending on not only types of transport infrastructure but land use purposes, types

of real estate as well. Besides, the context and using methods used in some studies

can make variety of results.

In this research, we assess the impact of transport infrastructure investment on

land values in terms of land users’ preferences which has been largely ignored [Asadi

Bagloee and et al (2017); Iacono and et al (2008)]; and we use Hanoi city, Vietnam

as a case study. We see this as important for two core reasons. Firstly, has a strong

growth economy with being numerous transportation investment projects but is still

in many ways an emerging country, where there is not enough significant data about

land values or land prices (as proxy variable). Secondly, we have extensive evidence

on these impacts in the USA and Europe but little such evidence on developing

countries like Vietnam.

This paper adopts the land rent theory, developed by Alonso (1964) and Muth

(1969), the theoretical framework for the relationship between accessibility and land

values. These theories purport that land rent (and therefore the underlying land

values) reflects accessibility gradients with higher values of rent reflecting higher

accessibility to goods/services after having transport infrastructure. The study also

adapts conceptual framework for Gwamna and Yusoff (2016), so that the diagram for

the conceptual framework is shown below.

Centre distance

Mobility timing

Land use

Land values

changes

Development

opportunity

Accessible amenity

Figure 1: Conceptual Framework for the Study

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Drawing from the conceptual framework and basing on the literature, several

hypotheses were formulated for this study. They are as follows:

H1: Centre distance has significant impact on Land use changes.

H2: Mobility timing has significant influence on Land use changes.

H3: Development opportunity has significant influence on Land use changes.

H4: Accessible amenity has significant influence on Land use changes.

H5: Land use changes have significant effect on Land values.

H6: Centre distance has significant influence on Land values.

H7: Mobility timing has significant effect on Land values.

H8: Development opportunity has significant effect on Land values.

H9: Accessible amenity has significant effect on Land values.

2. Method

The survey research approach was adopted for this study. A comprehensive list

of land value determinants thanks to investing public transports was generated from

previous studies conducted in the study area. Scale development was also performed

following the suggestions of literature review; and the questionnaire was designed with

6 main constructs and other variables.

The study instrument only employed closed-ended questions. For each

proposed dimension, a related set of variables was utilized. The variables were

measured on a bipolar 5-point semantic differential Likert type scale where 1 = strongly

disagree and 5 = strongly agree. Face to face questionnaires are conducted by

interviewers from December 2018 to February 2019. A total of 225 responses were

collected evenly across some new urban areas in several districts of Hanoi city but,

following a review of data quality and missing response elements, 212 samples were

ﬁnally selected.

Item generation began with theory development and a literature review. Items

were evaluated through interviews with practitioners. For the development and

exploratory evaluation of the measurement scales for the exploratory factor analysis

(EFA) on entire set and reliability estimation Cronbach’s Alpha, which is one of the

most widely used metrics for reliability evaluation (Koufteros, 1999). EFA was then

used to determine how many latent variables underlie the complete set of items.

Based on EFA results, the AMOS (Analysis of Moment Structures) Graphic

was used to model and analyze the inter-relationship between and among the latent

constructs in this study effectively, accurately and efficiently (Hoyle, 1995). The

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Confirmatory Factor Analysis (CFA) was performed for the measurement model of

the latent constructs. Especially, the overall fit of a hypothesized model can be tested

by using the maximum likelihood Chi-square statistic provided in the Amos output

and their fit indices such as the ratio of Chi-square to degrees of freedom, goodness-

of-fit index (GFI), the root mean square error of approximation (RMSEA),

comparative fit index (CFI), normed fit index (NFI). After that, a hypothetical

construct accounts for the inter-correlations of the observed variables that define that

construct (Bollen & Lennox, 1991).

3. Results

31. Descriptive Statistics

There were 87 (41.0%) male and 125 (59.0%) female respondents. In terms of

the respondents’ occupation, about 28.8% of the interviewers was an official in public

sector, 43.4% participants worked in private sector, and the other did part-time job.

Across ranges of age, the dominant age group of the respondents was more than 50

years old (41.0%) and 31-40 (32.1%) that follows; 31 (14.6%) were aged less than

30 years, only 26 (12.3%) participants were 41-50 years old. Nearly 39.2% (83/212)

of the interviewers lived in Ha Dong district. In terms of the respondents’ living areas,

it was distributed: South Tu Liem 36.3%, Thanh Xuan 23.1%, and others 1.4%.

Regarding participants’ level of education, 50% of the interviewers had high school

diploma, while these figures of respondents having the degree of bachelor and master

were 42.9% and 7.1% respectively.

3.2. Exploratory measurement results

This study used EFA to determine how many latent variables underlie the

complete set of items. An EFA was used to reduce these items to a smaller, more

manageable set of underlying factors, which is helpful for detecting the presence of

meaningful patterns among the original variables and for extracting the main factors,

according to Hair et al. (2013).

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Table 2: The factor loadings

Component

1

2

3

4

5

6

LUC6

.863

.836

.879

.818

LUC7

LUC8

LUC9

OPT1

.795

.736

.884

.841

OPT3

OPT4

OPT5

DISTANCE1

DISTANCE2

DISTANCE3

TIME1

TIME2

TIME3

LV1

.861

.843

.687

.859

.863

.793

.685

.806

.800

LV2

LV3

ACCESS1

ACCESS2

.867

.842

Eigenvalue: 4.662

Cumulative %24.536

3.587

1.961

1.681

1.250

1.129

75.105

43.417

53.736

62.581

69.162

Sig. = 0.000; KMO = 0.783

Depending on the result of EFA, there were six factors with new items and

new names, which were checked against Cronbach’s alpha and Corrected Item-Total

Correlation. Cronbach’s alpha is one of the most widely used to measure for

evaluating reliability. The summary result of Cronbach’s alpha value for each

measure is shown at Table 2. The reliability for each construct was significantly high

as above the value of .685, and KMO is 0.783, which is considered satisfactory for

basic research.

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3.3. Confirmatory factor analysis results

To refine the initial measures and test the internal consistency of the scale, a

combination of exploratory factor analysis, confirmatory analysis (each construct

individually) and item-to-total correlations were used. Depended on the results of

these analyses, those items that had low item-to-total correlations were eliminated, as

well as the items that had low factor loadings.

Chi-square= 307.843; df= 137;

P= .000; CMIN/DF = 2.247;

GFI = .863; AGFI = .810; TLI= .888; CFI=

.911; IFI= .912; RMSEA= .077

Figure 2: Initial Structural Model with Standardized estimates

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The model was assessed and shown in the Figure 2. An examination of the

overall fit statistics for the measurement model, indicated that the model provided

acceptable fit to the data, with CMIN/df = 2.247 (<3). Even though the value of GFI

(0.863), AGFI (0.810), TLI (0.888), and RMSEA (0.077) were quite low but CFI

(0.911), IFI (0.912) stand out to demonstrate that model is likely to fit data.

In order to further improve upon the values of the fitness indexes of the

Structural Model so as to have reliable results from the analysis, a pair of redundant

items were also set as free parameters to improve the model. Figure 3 shows the Final

Structural Model.

Chi-square= 285.655; df= 136;

P= .000; CMIN/DF = 2.100;

GFI = .875; AGFI = .825; TLI= .901; CFI=

.922; IFI= .923; RMSEA= .072

Figure 3: Final Structural Model with Standardized estimates

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The final model fit with the data was evaluated using common model

goodness-of- fit measures estimated by AMOS, which was than these of initial model.

The model exhibited a fit value exceeding or close to the commonly recommended

threshold for the respective indices values of 0.875, 0.825, 0.901, 0.922, 0.923 for the

GFI, AGFI, TLI, CFI, IFI are satisfactory with respect to the commonly

recommended value of equal to 1.0. RMSEA (0.072) which satisfied the threshold of

0.2. In brief, the final model is reasonably considered to fit with the data collected.

The assessment hypothesis is based on results in Table 3 where standardized

estimates and their significance level are provided. A positive sign of parameter

estimate indicates a positive direct effect.

Table 3: Results of hypothesis testing

Hypothesis

Estimate

S.E.

.026

.038

.039

.024

.039

.019

.028

.026

.028

C.R.

P

Result

LUC <--> OPT .020

.778

.436 Not Supported

*** Supported

LUC <--> DIS

.139^***

3.670

4.079

1.532

-.195

1.811

3.239

1.880

1.087

LUC <--> TIM .160^***

LVC <--> LUC .036

LUC <--> ACC -.008

LVC <--> OPT .035^*

*** Supported

.125 Not Supported

.845 Not Supported

.070 Supported

LVC <--> DIS

LVC <--> TIM .049^*

.090^***

.001 Supported

.060 Supported

LVC <--> ACC .031

.277 Not Supported

Notes:

*** Significant at 0.01 level

** Significant at 0.05 level

* Significant at 0.1 level

Based on the result of regression in this study, our following hypothesis for

land use changes: “Centre distance has significant impact on Land use changes”

(H1), and “Mobility timing has significant influence on Land use changes” (H2) are

supported by data. Similarly, there are evidences to support several hypotheses for

land value namely: “Centre distance has significant influence on Land values” (H6),

“Mobility timing has significant effect on Land values” (H7), and “Development

opportunity has significant effect on Land values” (H8). In contrast, the data does not

support the rest hypotheses, which is an unexpected result by virtue of the previous

literature review on the relationship between land use changes, accessible amenity

and land value.

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Especially, the results have shown that a positive relationship between Center

distance, Mobility timing and Land use change. In other words, if there are change of

land use thanks to public transportation investment, the Centre distance and Mobility

timing will be imprinted a positive impact being about 13.9% and 16.0% respectively.

Moreover, in the terms of land value increment, the finding confirms a positive

effect of Centre distance, mobility timing and development opportunity on land

values. It means that if the effect of changes in land value is determined due to

investment of public transport, the Centre distance, Mobility timing and Development

opportunity will be contributed to a numerical quantity being 9.0%, 4.9% and 3.5%

respectively. In particular, compared with other factors, the Centre distance had the

strongest influence on change of land values.

4. Discussion and Conclusion

This study attains to adopt a conceptual model that explains how investment

of public transport can affect land values in Hanoi city. The findings indicated that

some hypotheses were supported by the data like 1, 2, 6, 7, 8 while the opposite

(without supporting) is true for the rest hypotheses (3, 4,5, 9).

Generally, the major findings are that owing to investing public transport, the

Centre distance, Mobility timing and Development opportunity have positive

influence on land values, however there is no significant relationship between Land

use changes, Accessible amenity and land values in the scope of this study.

Some findings of this research work may have extended to the body of knowledge

of urban economics, real estate economics and land administration in general, and urban

land use and land values in particular especially as it relates to urban development in

developing countries. Based on understanding the relationships among factors it can be

provided more information for planners to manage urban development, property

investors to make a decision investment in property, policy makers to make policy, and

researchers to refine our understanding of urban system.

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