Sage Journals: Discover world-class research

Abstract

Transport infrastructure development projects play a vital role in improving accessibility, which has dynamic effects on the regional economy as well as increases property values. This research aims to assess the land price variability in control and catchment areas before and after the Dasuria-to-Dhalarchar railway development project in Pabna district, Bangladesh. A set of independent variables was considered to measure the correlation between development project and land price through a questionnaire survey and focus group discussion. The impact of a railway project on land price was determined by the ordinary least squares method, after which the method was justified through Cook’s distance, P-P plot and scatter plot. The data analysis reveals a strong association between land price and distance to a central business district. The data shows that the land price increased significantly after implementation of the railway project with different intensities, as functional characteristics vary among selected stations.

Keywords

Land price development project control area catchment area ordinary least square method

Introduction

A transportation infrastructure development project is carried out to increase regional or urban connectivity and alleviate traffic congestion (Levkovich et al., 2015). Improvements to transportation infrastructure and services will boost the overall economic output by lowering transportation costs and raising accessibility, directly lowering input costs, increasing private investment, promoting trade, creating employment and indirectly improving labour productivity (Deng, 2013). Rapid population growth and regional employment opportunities put pressure on transport infrastructure development (Cervero, 2009). Rail transit is a viable alternative to driving because it reduces the number of cars on the road and encourages compact growth trends by bringing people closer to rail stations (Bowes & Ihlanfeldt, 2001). Bangladesh’s rail network, mostly British-built, connects most of the country’s major cities. The economic growth of lower-middle income countries like Bangladesh depends a lot on their transportation infrastructure. Over the past decade, governments in Bangladesh have invested massive amounts of money in developing mega transportation projects to increase the transit connectivity of the country, including road and rail line construction.

The impact of rail transit becomes apparent at the regional level as regional economic growth or productivity increases as a result of rail transit development (Pan, 2013). At the local level, there is improved accessibility in the vicinity of stations (Bollinger & Ihlanfeldt, 1997; Pan, 2013). Over the past several decades, numerous studies have been carried out on the effect of transportation infrastructure construction on the regional economic growth, with the primary goal of examining the financial returns of transportation investments to determine an appropriate investment pattern (Aschauer, 1989; Canning & Bennathan, 2013; Munnell, 1990; Ozbay et al., 2003; Yu et al., 2013). With its traditional externalities, transport infrastructure investment is an important government responsibility and a key tool for controlling the economy and encouraging economic development (Xueliang, 2013). Rail transit investment has several public interest benefits, including increased physical activity, reduced traffic congestion, reduced emissions and increased customer amenities (Berawi et al., 2020; Bhattacharjee & Goetz, 2012; Freeland et al., 2013; Rahman et al., 2018; Sharma et al., 2014; Zheng et al., 2016). New mass transit infrastructure such as rail lines and railway stations enhance accessibility for both existing and new users, and improvements in mobility also translate into higher land value, a phenomenon known as land value uplift (Mulley, 2013).

Earlier studies investigated the impact of a transport development project on the livelihood pattern at the local and regional levels (Canning & Bennathan, 2013; Yu et al., 2013). Other studies have been conducted on large and medium-sized transport development projects to monitor land price variability, mainly in large urban areas (Arum & Fukuda, 2020; Mulley, 2013). It is important to note that most previous studies have considered only statistical tools and techniques to assess the effects of new transport development projects on local and regional economies. This study focused on the newly constructed Dasuria-to-Dhalarchar railway project to monitor the impact of the railway development project on land price variability. Also, the study considered the station area as the control area and the surrounding areas as the catchment area to analyse the impact of a newly developed railway project on relative land values. No previous article has yet analysed the comparative benefits of the control area and catchment area due to a transport construction project. This study examines land price variability in the control and catchment areas at a regional level for the first time.

Various models have been applied to examine the impact of rail transit on land price (Pan, 2013). The most widely used one is the hedonic price model, which estimates the impact of the rail line and rail stations on property values (Bowes & Ihlanfeldt, 2001; Nelson & McCleskey, 1990; Pan, 2013). Hedonic methods estimate transit value to station area residents (Workman & Brod, 1997). The recently invented ordinary least square (OLS) model is a much more effective method of identifying the impact of transportation infrastructure on property value. The estimate will include transportation and any non-use benefits of transit derived from a neighbourhood and the impact on general livability (Workman & Brod, 1997). The possible outcome of the OLS model would be identifying property values using the linear regression equation. A geographic information system (GIS) is a valuable geospatial tool commonly used to assess land cover characteristics (Bhuiyan et al., 2020; Jahan et al., 2021). Secondary sources of Google map data have been used for land cover scenario analysis (Nagne et al., 2018). The objective of the article is to measure the variability of land price before and after the rail infrastructure development project through the OLS model particularly in the control and catchment areas in the study region.

Methodology

The Dasuria-to-Dhalarchar railway project of Pabna district in Bangladesh is considered as the study region, on which construction started in 2010 and was completed in 2018. Before the project, only Ishwardi Upazila, Chatmohar Upazila, Bhangura Upazila, Bera Upazila and Faridpur Upazila of Pabna district were connected with the rail network. The 78.8 km new line and the 7.9 km loop line, along with 533 m small bridges and 952 m big bridges, connect the Pabna district with other regions of Bangladesh (Rahman, 2018). The Dasuria, Pabna and Dhalarchar stations are selected as the sample study area to analyse the impact of a railway project on land value in both control and catchment areas (Figure 1).

Figure 1.

Study Area Map.

The railway project has multiple sub-stations and in most cases is located far from human settlement. Therefore, it’s quite difficult to fix any specific mathematical equation to identify the sample size. Also, the target population of this development project has not yet been identified. Calculating the sample size without identifying the target population is difficult. Considering the limitation, we fixed 100 questionnaire surveys on each of the three railway stations. So, a total of 300 questionnaire surveys were done for the research. A questionnaire survey was conducted to collect data on the various effects caused by transportation development in the study area. It was administered to local stakeholders and residents impacted by the railway development project. Also, six focus group discussions (FGDs) were conducted in the study region, with two FGDs in each station. These discussions provided an understanding of the residents’ opinions on the project region and the risks and effects that the project poses to them. In addition, government publications, websites, books, journal papers and internal records were used as secondary data sources for the study.

Impact on Property Value (Ordinary Least Square Method)

The OLS method is often utilized (Pavelescu & Marius, 2004) to determine the impact of railway development projects on land price. The connection between the dependent and independent variables determines how the model is generally specified (Berawi et al., 2020). Property values may be calculated using multiple regression analysis based on structural qualities, neighbourhood characteristics, accessibility and land use types, among other variables (Berawi et al., 2020). The model designates the following formula.

Y = α + β 1. x 1 + β 2. x 2 + β 3. x 3 + β 4. x 4 + \dots + e

where

Y = Dependent variable (property prices)

α = Regression constant

β = Regression coefficient

x = Independent variables

e = Error value (5%)

This study employs two data sources to derive values for each identified variable in the OLS models. The initial data source was gathered through data mining from a field survey conducted at the selected stations. A mix of Google maps and GIS was used to acquire secondary data such as distance, buildings and position. The variable used in the research is given in Table 1.

Table 1.

Variable Measurements.

Variable Type	Variables	Variable Quantification	Data Source
Dependent variable	Land price	Taka/Sotangso	Field survey
Independent variables	Structural attributes
	Distance from station	Distance between the property and the railway station	GIS
	Physical structure of the properties	Existing property types, structure	Field survey
	Local employment	Local employment opportunities	Field survey
	Neighbourhood attributes
	Surrounding shops or commercial activities	Accessibility of nearby commercial activities	Field survey
	Access to CBD	Transportation facilities on the way to nearby CBD	GIS
	School within 1 km	If there is a school within 1 km, the variable is 1; otherwise 0	GIS, field survey
	Hospital within 1 km	If there is a hospital within 1 km, the variable is 1; otherwise 0	GIS, field survey

Note: CBD - central business district.

Control Area and Catchment Area Impact Analysis

The concept of control and catchment area has been introduced in the study. Control area is the station area, and catchment area is the nearby local bazaar or growth centre to the railway station. An analysis of the impact on land values in the control and catchment areas can be identified through the geographic information system (GIS), questionnaires and FGD surveys.

Data Analysis and Findings

Dasuria Station

Property Value Status and Land Use Characteristics

Dasuria station is located very near the Dasuria bazaar, and it is one of the critical intersection points of the Pabna district transportation system. Construction of the station was started in 2014 and completed in 2018. Changes in property values and land use types are noticeable before and after the station construction (Table 2).

Land values increased considerably within 0–100 m of Dasuria station area from 2012 to 2022. A similar increase was found within the 100 to 200 m buffer zone of Dasuria station. It is important to note that land prices have become almost double within the buffer zone of 200 to 300 m and 300 to 500 m because the major highway is situated over this buffer distance. Land use characteristics have also changed over time due to implementation of the project.

Measuring the Variability of Land Prices Through the OLS Model After the Project Was Completed

First, a correlation test was used to establish the degree of connection between the independent and the dependent variable (Berawi et al., 2020). The descriptive statistics for the dependent and independent variables are presented in Table 3. The dependent variable’s overall features and characteristics are presented by group (as determined by the independent factors) (Berawi et al., 2020). Table 3 describes the mean and standard deviation of the dependent and independent variables of the OLS method.

Table 2.

Comparative Property Values and Land Uses vis-à-vis Distance from Dasuria Station.

Distance from the Station (m)	Land Use Characteristics		Land Values/in Sotangso (Taka)
Distance from the Station (m)	2012 2022		2012 2022
0–100	Agriculture	Residential	30,000	5,00,000
100–200	Agriculture	Vacant land	30,000	3,00,000
200–300	Agriculture	Residential	5,00,000	10,00,000
300–500	Residential	Residential and commercial	15,00,000	25,00,000

Note: Sotangso is a local land-measuring unit, where 1 sotangso is equal to 0.605 katha or 1 decimal.

Table 3.

Sample Descriptive Statistics of the OLS Method for Dasuria Station.

Variable	Mean	Std. Deviation
Land price	258,250.0000	150,693.79025
Distance from station	2.5000	1.14708
Physical structure	2.0500	0.99868
Local employment	2.3500	0.93330
Surrounding shops/ commercial activities	1.8000	0.83351
Access to CBD	2.4500	0.82558
School within 1 km	1.4500	0.51042
Hospital within 1 km	1.8500	0.36635
CBD within 1 km	2.0000	0.00000

Note: CBD - central business district.

The correlation coefficient can range between −1 and 1. A number near −1 or +1 denotes a strong association between the two variables, while a value near 0 denotes a poor relationship (Berawi et al., 2020). From Table 4 it can be seen that the dependent variable land price strongly relies on access to central business district (CBD; 0.920) and distance from the station (–0.771). In this case, the land price positively relates to access to CBD. In contrast, the land prices decrease with the distance from the railway station. Other variables are not significant for increasing land value. Positive (+) and negative (−) signs, on the other hand, indicate how the two variables are related (Berawi et al., 2020). These are directly related if the value is positive.

Table 4.

OLS Method Comprising Dependent and Independent Variables for Dasuria Station.

Variables	Land Price	Distance from Station	Physical Structure	Local Employment	Commercial Activities	Access to CBD	School Within 1 km	Hospital Within 1 km	CBD Within 1 km
Land price	1.000	−0.771	−0.529	.063	−0.043	0.920	0.497	−0.405	—
Distance from station	−0.771	1.000	0.528	−0.025	0.165	0.417	−0.494	0.438	—
Physical structure	−0.529	0.528	1.000	−0.359	0.076	0.227	0.160	0.165	—
Local employment	.063	−0.025	−0.359	1.000	0.162	−0.215	−0.238	0.162	—
Surrounding shops/commercial activities	−0.043	0.165	0.076	0.162	1.000	−0.168	0.099	0.069	—
Access to CBD	0.920	0.417	0.227	−0.215	−0.168	1.000	−0.131	0.061	—
School within 1 km	0.497	−0.494	0.160	−0.238	0.099	−0.131	1.000	−0.183	—
Hospital within 1 km	−0.405	0.438	0.165	0.162	0.069	0.061	−0.183	1.000	—
CBD within 1 km	—	—	—	—	—	—	—	—	1.000

A total of three assumptions are used to justify the method for checking the accuracy of the OLS method. These are: Cook’s distance from the residuals statistics summaries table, the P-P plot and the scatter plot. If a case of Cook’s distance is more than one, it may be an unduly influential case that should be excluded from consideration (National Centre for Research Methods (NCRM), 2011). According to the formula, Table 5 shows that the maximum Cook’s distance is 1.057, which is standard. On the other hand, the P-P plot is a bit more detailed. Between the observed cumulative probability of 0.2 and 0.6, there appears to be some departure from normalcy, although it appears minimal. Figure 2(a) shows the P-P plot, which seems like a linear inline that explains the relationships among the dependent and independent variables. The residuals do not follow any pattern with the projected values, as shown in this scatter plot (NCRM, 2011). This indicates that our model satisfies the homoscedasticity assumption. Figure 2(b) shows the scatter plot required to justify the model.

Table 5.

Residuals Statistics of Dasuria Station.

	Minimum	Maximum	Mean	Std. Deviation
Predicted value	61,274.3555	497,231.4688	258,250.0000	148,179.77687
Standard predicted value	–1.329	1.613	0.000	1.000
Standard error of predicted value	15,823.906	27,819.938	21,478.541	3,912.368
Adjusted predicted value	47,323.1133	495,370.4063	255,303.8071	144,697.20510
Residual	–48,316.39453	44,910.75781	0.00000	27,411.16830
Standard residual	–1.401	1.302	0.000	0.795
Studied residual	–1.757	2.174	0.028	1.088
Deleted residual	–86,571.70312	125,227.15625	2,946.19292	53,152.38683
Studied deleted residual	–1.952	2.674	0.040	1.195
Mahalanobis distance	3.049	11.411	6.650	2.731
Cook’s distance	0.000	1.057	0.142	0.257
Centred leverage value	0.160	0.601	0.350	0.144
Dependent variable: land price

Figure 2.

Dasuria Station: (a) P-P Plot of Regression Standardized Residual Dependent Variable; (b) Scatter Plot of Dependent Variable.

Changes in the Control and Catchment Areas

The comparative scenario of the control and catchment areas of Dasuria station is shown in Table 6. Most of the agricultural land in the control area has been converted into residential areas, where the land prices have increased by 19 times of the previous prices. The land use characteristics remain unchanged, but the prices have increased by 1,000,000 taka per sotangso in the catchment area. This clearly indicates that transport development projects are strongly associated with catchment areas.

Table 6.

Dhalarchar Station: Comparison of Property Values and Land Use Characteristics in the Control and Catchment Areas.

Zone	Land Use Characteristics		Average Land Value/Sotangso (Taka)
Zone	2012	2022	2012	2022
Control area	Agriculture	Residential	25,000	475,000
Catchment area	Residential and commercial	Residential and commercial	1,300,000	2,300,000

Pabna Station

Property Value Status and Land Use Characteristics

The Rajshahi–Pabna highway is located within 100 to 200 m from the Pabna railway station, and the Pabna bus terminal is located 700 m from the station. Therefore, the station has a complex impact on the local land prices. Agricultural land within 0 to 100 m has been converted into a residential area, and the water body within 300 to 500 m has also been converted into a residential area after the implementation of the project. The land values within 100 to 200 m and 200 to 300 m increased rapidly as the population increased in this region significantly. Table 7 shows the comparative scenario of property values with land use characteristics before and after the project was built.

Table 7.

Comparison of Property Values and Land Use Characteristics vis-à-vis Distance from Pabna Station.

Distance from the Station (m)	Land Use characteristics		Land Values/Sotangso (Taka)
Distance from the Station (m)	2012	2022	2012	2022
0–100	Agriculture	Residential	100,000	100,000
100–200	Residential	Residential	800,000	1,500,000
200–300	Residential	Commercial	1,000,000	1,800,000
300–500	Water body	Residential	350,000	1,200,000

Measuring the Variability of Land Prices Through the OLS Model After the Project Was Completed

Table 8 describes the sample descriptive statistics of the OLS method, including the mean and standard deviation. In Table 9, the OLS model indicates where the dependent variable and land prices relate to several independent variables such as distance from the station, employment in the area, access to CBD and shops/commercial activities around the station. Access to CBD (0.949) or the main road is responsible for increasing the land prices. Employment opportunities (0.734) and closeness to the station (0.898) also have an impact on increasing the land values of the area.'

Table 8.

Sample Descriptive Statistics of the OLS Method for Pabna Station.

Variable	Mean	Std. Deviation
Land price	802,750.0000	189,093.92681
Distance from station	2.5000	1.14708
Physical structure	1.4000	0.68056
Local employment	1.7000	0.97872
Surrounding shops/commercial activities	2.6000	0.82078
Access to CBD	1.1000	0.30779
School within 1 km	1.9000	0.30779
Hospital within 1 km	1.0000	0.00000
CBD within 1 km	2.7000	0.73270

Note: CBD - central business district.

Cook’s distance from the residuals statistics of Pabna station is 1.102, which is close to the standard Cook’s distance of 1. So, this assumption is almost standard (Table 10). The P-P plot of regression shows its standard nature in Figure 3(a); also, the scatter plot in Figure 3(b) shows its usual results. So, we can say there are strong relationships between dependent and independent variables.

Table 9.

Results of OLS Method Comprising Dependent and Independent Variables for Pabna Sadar Station.

Variable	Land price	Distance from Station	Physical Structure	Local Employment	Commercial Activities	Access to CBD	School Within 1 km	Hospital Within 1 km	CBD Within 1 km
Land price	1.000	0.898	0.077	0.734	0.949	0.467	−0.357	—	−0.851
Distance from station	0.898	1.000	−0.405	−0.234	−0.112	0.298	0.149	—	0.188
Physical structure	0.077	−0.405	1.000	0.190	0.302	−0.201	−0.050	—	0.253
Local employment	0.734	−0.234	0.190	1.000	−0.157	−0.245	0.245	—	0.308
Surrounding shops/Commercial activities	0.949	−0.112	0.302	−0.157	1.000	0.167	−0.167	—	0.140
Access to CBD	0.467	0.298	−0.201	−0.245	0.167	1.000	−0.444	—	−0.327
School within 1 km	−0.357	0.149	−0.050	0.245	−0.167	−0.444	1.000	—	0.327
Hospital within 1 km	—	—	—	—	—	—	—	1.000	—
CBD within 1 km	−0.851	0.188	0.253	0.308	0.140	−0.327	0.327	—	1.000

Table 10.

Residuals Statistics for Pabna Station.

	Minimum	Maximum	Mean	Std. Deviation
Predicted value	603,584.5000	1,075,279.3750	802,750.0000	132,530.72985
Standard predicted value	−1.503	2.056	0.000	1.000
Standard error of predicted value	65,347.168	150,006.656	104,119.617	26,769.594
Adjusted predicted value	547,899.7500	1,370,035.8750	825,365.4258	206,950.16508
Residual	−215,576.98437	207,648.56250	0.00000	134,878.16281
Standard residual	−1.270	1.223	0.000	0.795
Studied residual	−1.571	1.571	−0.041	1.093
Deleted residual	−570,035.87500	570,035.87500	−22,615.42584	292,844.18912
Studied deleted residual	−1.688	1.688	−0.038	1.148
Mahalanobis distance	1.867	13.893	6.650	3.900
Cook’s distance	0.000	1.102	0.214	0.388
Centred leverage value	0.098	0.731	0.350	0.205
Dependent variable: Land price

Figure 3.

Pabna Station: (a) P-P Plot of Regression Standardized Residual Dependent Variable; (b) Scatter Plot of Dependent Variable.

Changes in the Control Area and Catchment Area

The comparative scenario of the control and catchment areas of Pabna station is shown in Table 11; there is a considerable increase in land prices in the control area of Pabna station. In addition, agricultural land has changed to residential areas due to the higher demand for residential land after the project implementation. The land use characteristics have remained unchanged in the catchment area, but the land prices have increased by 1,000,000 taka per sotangso in the study region.

Table 11.

Dasuria Station: Comparison of Property Values and Land Use Characteristics in the Control and Catchment Areas.

Zone	Land Use Characteristics		Average Land Values/Sotangso (Taka)
Zone	2012	2022	2012	2022
Control area	Agriculture	Residential	100,000	900,000
Catchment area	Commercial	Commercial	800,000	1,800,000

Dhalarchar Station

Property Value Status and Land Use Scenario

The surrounding area of Dhalarchar station was almost completely covered by agricultural land before the project. However, the land use pattern has been changing as people have started living around the station. The land price was around 8,000 taka per sotangso in 2022 in the station area, but over the years, in 2022, the land value increased to 80,000 taka per sotangso (Table 12). So, the land value has increased significantly in both the station and catchment areas (Dhalarchar bazaar).

Table 12.

Comparison of Property Values and Land Use Characteristics vis-à-vis Dhalarchar Station.

Distance from the Station (m)	Land Use Characteristics		Land Value/Sotangso (Taka)
Distance from the Station (m)	2012	2022	2012	2022
0–100	Agriculture	Residential	8,000	80,000
100–200	Agriculture	Agriculture	8,000	50,000
200–300	Agriculture	Agriculture	8,000	30,000
300–500	Agriculture	Residential	15,000	40,000

Measuring the Variability of Land Price After the Project Through the OLS Model

Table 13 represents the mean and standard deviation of the selected variables. Table 14 shows that the values of the dependent variable (land price) and the independent variable (distance from the station) are highly related, which is 0.944. The land price increases with nearness to the station, and the land price decreases with distance from the station. Other independent variables have no significant impact on the land price of the area.

Table 13.

Sample Descriptive Statistics of the OLS Method for Dhalarchar Station.

Variable	Mean	Std. Deviation
Land price	38,100.0000	21,834.06320
Distance from station	2.5000	1.14708
Physical structure	2.6000	0.75394
Local employment	2.7000	0.73270
Surrounding shops/commercial activities	2.6000	0.82078
Access to CBD	2.0000	0.00000
School within 1 km	2.0000	0.00000
Hospital within 1 km	2.0000	0.00000
CBD within 1 km	2.5500	0.82558

Note: CBD - central business district.

Table 14.

Results of Ordinary Least Square Method Comprising Dependent and Independent Variables for Dhalarchar Station.

Variable	Land Price	Distance from Station	Physical Structure	Local Employment	Commercial Activities	Access to CBD	School Within 1 km	Hospital Within 1 km	CBD Within 1 km
Land price	1.000	0.944	−0.285	0.272	0.190	—	—	—	0.195
Distance from station	0.944	1.000	0.183	−0.188	−0.224	—	—	—	−0.083
Physical structure	−.285	0.183	1.000	0.152	−0.272	—	—	—	0.034
Local employment	0.272	−0.188	0.152	1.000	−0.210	—	—	—	0.809
Surrounding shops/ commercial activities	0.190	−0.224	−0.272	−0.210	1.000	—	—	—	0.031
Access to CBD	—	—	—	—	—	1.000	—	—	—
School within 1 km	—	—	—	—	—	—	1.000	—	—
Hospital within 1 km	—	—	—	—	—	—	—	1.000	—
CBD within 1 km	0.195	−0.083	0.034	0.809	0.031	—	—	—	1.000

Note: CBD - central business district.

Table 15.

Residuals Statistics for Dhalarchar Station.

	Minimum	Maximum	Mean	Std. Deviation
Predicted value	4,216.3462	71,633.2969	38,100.0000	20,961.27955
Standard predicted value	−1.616	1.600	0.000	1.000
Standard error of predicted value	2,348.896	6,475.372	3,754.952	1,079.748
Adjusted predicted value	−1,928.6031	69,798.6406	37,929.6920	21,082.29859
Residual	−13,107.11133	8,783.65430	0.00000	6,111.55262
Standard residual	−1.841	1.234	0.000	0.858
Studied residual	−2.348	1.608	0.005	1.049
Deleted residual	−21,325.96289	14,928.60352	170.30800	9,466.17186
Studied deleted residual	−2.906	1.717	−0.024	1.148
Mahalanobis distance	1.118	14.766	4.750	3.322
Cook’s distance	0.000	1.27	0.106	0.166
Centred leverage value	0.059	0.777	0.250	0.175
Dependent variable: Land price

Cook’s distance of residuals statistics of Dhalarchar station is 1.27, which is close to +1. So, Cook’s distance shows the standard value justifies the model. The P-P plot of regression and scatter plot (Figure 4(a) and 4(b)) of this analysis also show the standard form.

Figure 4.

Dhalachar Station: (a) P-P Plot of Regression Standardized Residual Dependent Variable; (b) Scatter Plot of Dependent Variable.

Changes in the Control and Catchment Areas

Table 16 shows that there were considerable changes in land prices before and after the project was completed. The land prices in the control area were only 7,000 taka per sotangso in 2012, which increased to 70,000 taka per sotangso in 2022. The catchment area also experienced remarkable land price shifts over the years. The land price was only 50,000 taka per sotangso in 2012 but exponentially increased to 6,00,000 taka in 2022. The land use characteristics in control and catchment areas shifted due to the active pressure of the railway construction project.

Table 16.

Dhalarchar Station: Comparison of Property Values and Land Use Types in the Control and Catchment Areas.

Zone	Land Use Characteristics		Average Land values/Sotangso (Taka)
Zone	2012	2022	2012	2022
Control area	Agriculture	Residential	7,000	70,000
Catchment area	Commercial	Residential and commercial	50,000	600,000

Discussion and Conclusion

Transport facilities shape the urban fabric and deeply impact the land prices (Wang et al., 2021). According to traditional urban economics theories, locations closer to railway stations tend to have higher development densities and more capital-intensive land uses. Hence, land prices there rise quickly (Pan & Zhang, 2008). The evidence has been collected from all over the world. China is the greatest example of this scenario; a 7%–9% increase in land prices has been observed after railroad development (Zhou et al., 2017). Zhou et al. showed how a high-speed railway, a new demand, has changed land use and, in some cases, increased land prices by 30%. This research has also identified the steep increase in land prices in three control areas of railroad development in Bangladesh but different incremental ratios. Several reasons have been given according to the findings of FGDs. In Bangladesh, agricultural land prices are less than residential land prices. Land owners can charge more if they sell their land as residential plots. As a result, within a decade, land prices have increased at different rates at different stations. Moreover, the resettlement procedure and rules are very complex, which has created an opportunity for price hike. Rail authorities are optimistic about increasing passenger coaches and opportunities to deliver raw materials supporting local industries on this rail route. Different local firms and export–import companies have started to bring wheat, corn, fly ash, etc., which has led to employment opportunities. This has been a chance to build godowns as the railway authorities have strict rules and regulations about the collection period of goods. Firms ordering the goods and products must collect these within a fixed time; otherwise, the railway authorities will dump the products. Moreover, significant penalties are charged for the delay. Even near Pabna station, rail authorities have built godowns and given them out for rent. The relation between rail transport and warehouse godowns for food storage has been observed in India as well (Kumar et al., 2021). Moreover, there is a plan to build a servicing industry of rail and oil distribution in this region, centring around the railway network. As the railroad starts to be built, local people have become optimistic about these opportunities, and land prices have peaked. In Dasuria station, as a result, within a 100–200 m buffer area, agricultural land has become vacant.

Dasuria railway station is closest to the Ruppur Nuclear Power Plant (about 10 km), Ishwardi Export Zone and several agro-food industries and just 700 m away from the Dasuria bus station, a major catchment area. Land prices and residential demand are already very high in Ruppur and Ishwardi. Housing rents are similar to those in Dhaka, the capital city of Bangladesh. Dasuria railway station has become another opportunity for landowners to develop the rural agricultural area as residential. There has been an approximately 17 times increase within a 100 m range and 10 times increase in the 100–200 m range. As an area at a distance of more than 200 m from the railway station was already in the Dasuria bus stand’s vicinity, the price was already higher, and the change had little effect. This is why the control area has faced a significant change (19% increase in price), and the catchment area not so significant. A similar impact has been observed by Zheng and Gu (2010) in Beijing, where the influence of rail transit is waning as nearby urban transportation networks grow.

Pabna station is the closest to the district-level CBD. The land price at 100 m from the station has increased 10 times in a decade, and a major conversion from agriculture to residential use has been observed. The project has been associated with a pucca road development from the station to the Pabna–Sirajganj highway. This connection has steered a 9 times increase in prices and residential development. The control area was more low land, not suitable for residential development as water used to get trapped. The development has increased land values and properties as well. Even the water bodies 300–500 m away from the station have been filled up and prepared as residential land. The decrease in transport costs led by railroad construction also increases surrounding land prices (Coffman & Gregson, 1998). On the other hand, the catchment area is already developed under full-functioning commercial use. This is why it has fuelled the control area land prices more. The result is proved in the OLS analysis section showing the relationship between different parameters—local employment, surrounding shops and commercial activities, etc.

Dhalachar was an underdeveloped area in every sense. The land price in 2012 was only 7,000 taka per sotangso in the control area. The railway project has increased the prices significantly, although the final price is not as high as other study areas. Moreover, more the distance from the station, lower the price. Conversely, the catchment area has the highest increase in price (12 times) in a decade as the area is developing daily after the project completion.

Dasuria is a great place for transit-oriented development (TOD). Many people travel daily to attend Ruppur and Ishwardi EPZ as rents are sky high in those areas. Dasuria is also well connected with Ruppur and Ishwardi by road and rail. However, TOD has a complex and significant impact on residential land prices based on railway line differences and service quality (Arum & Fukuda, 2020). This is why pre-planning is vital to guide urban development and land prices. Pabna station is also a great place for TOD. Moreover, there is a public university and well-known industries nearby. There is a residential zone called Monsurabad residential area, not far from the control and catchment areas. A great opportunity for real estate companies is dormant in this area. Debrezion et al. (2010) have identified that real estate prices drop by 1% for every doubling of the distance between a property’s location and the nearby railroad station. This research has also found a significant increase in residential prices and a change in land use close to the stations. These findings constitute a base for concerned authorities to design a planned development outline for the study area.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

References

Arum

S. P.

, & Fukuda

(2020). The impact of railway networks on residential land values within transit-oriented development areas. Asian Transport Studies, 6, 100009. 10.1016/j.eastsj.2020.100009

Aschauer

D. A.

(1989). Is public expenditure productive? Journal of Monetary Economics, 23(2), 177–200. 10.1016/0304-3932(89)90047-0

Berawi

M. A.

, Miraj

, Saroji

, & Sari

(2020). Impact of rail transit station proximity to commercial property prices: Utilizing big data in urban real estate. Journal of Big Data, 7(1), 1–17. 10.1186/s40537-020-00348-z

Bhattacharjee

, & Goetz

A. R.

(2012). Impact of light rail on traffic congestion in Denver. Journal of Transport Geography, 22, 262–270. 10.1016/j.jtrangeo.2012.01.008

Bhuiyan

M. A. E.

, Witharana

, & Liljedahl

A. K.

(2020). Use of very high spatial resolution commercial satellite imagery and deep learning to automatically map ice-wedge polygons across Tundra vegetation types. Journal of Imaging, 6(12), 137. 10.3390/jimaging6120137

Bollinger

C. R.

, & Ihlanfeldt

K. R.

(1997). The impact of rapid rail transit on economic development: The case of Atlanta’s MARTA. Journal of Urban Economics, 42(2), 179–204. 10.1006/juec.1996.2020

Bowes

D. R.

, & Ihlanfeldt

K. R.

(2001). Identifying the impacts of rail transit stations on residential property values. Journal of Urban Economics, 50(1), 1–25. 10.1006/juec.2001.2214

Canning

, & Bennathan

(2013). The social rate of return on infrastructure investments (Policy Research Working Papers). 10.1596/1813-9450-2390

Cervero

(2009). Transport infrastructure and global competitiveness: Balancing mobility and livability. Annals of the American Academy of Political and Social Science, 626(1), 210–225. 10.1177/0002716209344171

10.

Coffman

, & Gregson

M. E.

(1998). Railroad development and land value. Journal of Real Estate Finance and Economics, 16, 191–204. 10.1023/A:1007707801970

11.

Debrezion

, Pels

, & Rietveld

(2011). The impact of rail transport on real estate prices: An empirical analysis of the Dutch housing market. Urban Studies, 48(5), 997–1015. 10.1177/0042098010371395

12.

Deng

(2013). Impacts of transport infrastructure on productivity and economic growth: Recent advances and research challenges. Transport Reviews: A Transnational Transdisciplinary Journal, 33(6), 686–699. 10.1080/01441647.2013.851745

13.

Freeland

A. L.

, Banerjee

S. N.

, Dannenberg

A. L.

, & Wende

A. M.

(2013). Walking associated with public transit: Moving toward increased physical activity in the United States. American Journal of Public Health, 103(3), 536–542. 10.2105/ajph.2012.300912

14.

Jahan

, Pradhanang

S. M.

, & Bhuiyan

M. A. E.

(2021). Surface runoff responses to suburban growth: An integration of remote sensing, GIS, and curve number. Land, 10(5), 452. 10.3390/land10050452

15.

Kumar

, Ram

, Jha

, & Vishwakarma

(2021). Warehouse storage management of wheat and their role in food security. Frontiers in Sustainable Food Systems, 5, 675626. 10.3389/fsufs.2021.675626

16.

Levkovich

, Rouwenda

, & Marwijk

R. V.

(2015). The effects of highway development on housing prices. Transportation, 43(2), 379–405. 10.1007/s11116-015-9580-7

17.

Mulley

(2013). Accessibility and residential land value uplift: identifying spatial variations in the accessibility impacts of a bus transit way. Urban Studies, 51(8), 1707–1724. 10.1177/0042098013499082

18.

Munnell

A. H.

(1990). How does public infrastructure affect regional economic performance? New England Economic Review, 34, 11–33.

19.

Nagne

A. D.

, Dhumal

R. K.

, Vibhute

A. D.

, Nalawade

B. D.

, Kale

K. V.

, & Mehrotra

S. C.

(2018). Advantages in land use classification of urban areas from hyperspectral data. International Journal of Engineering and Techniques, 4(1), 28–35.

20.

NCRM. (2011). ReStore. https://www.ncrm.ac.uk/

21.

Nelson

A. C.

, & McCleskey

S. J.

(1990). Improving the effects of elevated transit stations on neighborhoods. Transportation Research Board, 1266, 173–180.

22.

Ozbay

, Ozmen-Ertekin

, & Berechman

(2003). Empirical analysis of relationship between accessibility and economic development. Journal of Urban Planning and Development, 129(2), 97–119. 10.1061/(ASCE)0733-9488(2003)129:2(97)

23.

Pan

(2013). The impacts of an urban light rail system on residential property values: A case study of the Houston METRO Rail transit line. Transportation Planning and Technology, 36(2), 145–169. 10.1080/03081060.2012.739311

24.

Pan

, & Zhang

(2008). Rail transit impacts on land use: Evidence from Shanghai, China. Transportation Research Record, 2048(1), 16–25.

25.

Pavelescu

, & Marius

(2004). Features of the ordinary least square (OLS) method: Implications for the estimation methodology. Journal for Economic Forecasting, 1(2), 85–101.

26.

Rahman

M. S.

(2018). Implementation of development project. Independent Publications Limited. https://m.theindependentbd.com/printversion/details/169393

27.

Rahman

H. Z.

, Berawi

M. A.

, Susantono

, Miraj

, Petroceany

J. S.

, & Maya

(2018). Investigation of an operation and maintenance framework in the railway industry: A case study of the Makassar-Parepare. International Journal of Technology, 9(3), 549–557. 10.14716/ijtech.v9i3.813

28.

Sharma

, Singh

, Dhyani

, & Gaur

(2014). Emission reduction from MRTS projects—A case study of Delhi metro. Atmospheric Pollution Research, 5(4), 721–728. 10.5094/APR.2014.081

29.

Wang

, Han

, & Vries

(2021). Insights into the correspondence between land use pattern and spatial distribution of rail transit services. Applied Spatial Analysis, 14, 907–928. 10.1007/s12061-021-09385-4

30.

Workman

S. L.

, & Brod

(1997). Measuring the neighborhood benefits of rail transit accessibility. Transportation Research Record, 1576(1), 147–153.

31.

Xueliang

(2013). Has transport infrastructure promoted regional economic growth? With an analysis of the spatial spillover effects of transport infrastructure. Social Sciences in China, 34(2), 24–47. 10.1080/02529203.2013.787222

32.

, Jong

M. D.

, Storm

, & Mi

(2013). Spatial spillover effects of transport infrastructure: Evidence from Chinese regions. Journal of Transport Geography, 28, 56–66. 10.1016/j.jtrangeo.2012.10.009

33.

Zheng

, & Gu

(2010). The impacts of rail transit on property values and land development intensity: The case of No. 13 Line in Beijing. Acta Geographica Sinica, 65(2), 213–223.

34.

Zheng

, Hu

, Wang

, & Wang

(2016). Subways near the subway: Rail transit and neighborhood catering businesses in Beijing. Transport Policy, 51, 81–92. 10.1016/j.tranpol.2016.03.008

35.

Zhou

, Yang

, Hewings

, & Huang

(2017). Does high-speed rail increase the urban land price in China: Empirical evidence based on micro land transaction data. SSRN Electronic Journal, 10, 3082657. 10.2139/ssrn.3082657

Land Price Variability Assessment of a Transport Infrastructure Project in Pabna District,Bangladesh

Abstract

Keywords

Introduction

Methodology

Study Area Map.

Impact on Property Value (Ordinary Least Square Method)

Control Area and Catchment Area Impact Analysis

Data Analysis and Findings

Dasuria Station

Property Value Status and Land Use Characteristics

Measuring the Variability of Land Prices Through the OLS Model After the Project Was Completed

Dasuria Station: (a) P-P Plot of Regression Standardized Residual Dependent Variable; (b) Scatter Plot of Dependent Variable.

Changes in the Control and Catchment Areas

Pabna Station

Property Value Status and Land Use Characteristics

Measuring the Variability of Land Prices Through the OLS Model After the Project Was Completed

Pabna Station: (a) P-P Plot of Regression Standardized Residual Dependent Variable; (b) Scatter Plot of Dependent Variable.

Changes in the Control Area and Catchment Area

Dhalarchar Station

Property Value Status and Land Use Scenario

Measuring the Variability of Land Price After the Project Through the OLS Model

Dhalachar Station: (a) P-P Plot of Regression Standardized Residual Dependent Variable; (b) Scatter Plot of Dependent Variable.

Changes in the Control and Catchment Areas

Discussion and Conclusion

Footnotes

Declaration of Conflicting Interests

Funding

References