Keywords

1 Introduction

1.1 Effect of Metro Rail on Real Estate in Its Vicinity

Although results are somewhat heterogeneous across study areas, many researchers have identified a positive effect of metro or transit station proximity on housing price [11, 13]. While some results have shown nonsignificant results, no studies were found showing a negative impact of metro rail proximity to housing prices. In research assessing the effect of metro lines on housing prices, a wide range of proximity premiums were found: from as low as 0.3% in Seoul [4] to as high as 75% in London (Banister 2007).

In Bangalore, the Metro system was found to uplift property values by 10.7% within a 500 m catchment area [38]. Gadziski and Radzimski [15] examined the effect of a new transit line on three variables: travel behavior, housing choices, and property prices. They found a significant influence of the transit line on at least one of the three studied components in every examined region. Metro rail provides quicker and easier access to jobs and can increase housing demand, and thus prices, in a given region. In a Bangkok study, researchers found a significant impact of proximity to mass transit stations, along with proximity to other transportation infrastructure such as arterial roads, on housing prices [2]. Interestingly, it seems that even the announcement of a planned transit line can have an impact on rental prices [17].

Many articles call attention to the positive impacts of land value increase due to metro rail—e.g., opportunities for city governments to earn revenue through land value capture [38]. However, some authors have highlighted the possible negative effects of proximity to transit stations. For example, in the USA, new transit stations (particularly “walk and ride” transit stations) can lead to gentrification or displacement of residents by increasing rent in the catchment area of the station [26]. Additionally, proximity to transit can be a disamenity due to additional noise or crime risk [37]. However, generally speaking, results have indicated positive relationships between access to metro stations and land value.

While many articles have been published quantifying the effect of metro rail on housing prices in urban areas, the present research has much to add to the literature. First, as Mumbai currently has one operational metro line and multiple planned lines, the present study can test the effect of both operational and nonoperational metro lines on housing prices. Second, a majority of published articles assess metro systems in the global north. This article is one of few addressing a metro system in a rapidly developing nation, and one of even fewer addressing an Indian city. The nature of Mumbai, particularly its dynamic and rapidly expanding real estate market, makes this study extremely relevant. Third, this paper features several different data sources and two methodologies. This multiple-methods approach allows for internal validation of results.

Some of the studies [5] that focus on the large-scale operational and established urban rail/metro investments study the impact on property values. However small-scale mode like LRT/trams are not studied as much. There lies a research gap for these modes. Multiple studies [13, 14, 20] have confirmed similar positive effect of having urban rail transit system closer on the property prices. Although there are other studies that undermine the effect and call it marginal or only important when the supporting conditions are favorable. Out of 24 North American cities studied in 19 different studies, 13 tested positive correlation in property prices, while 4 were found to have no effect of LRT infrastructure. However, 7 cities showed signs of decline in property prices. The results are not ambiguous because the negative correlations have an explanation. The cities where increase in socioeconomic inequality was triggered by the LRT infrastructure, the benefits of accessibility and proximity to LRT are outweighed by negative externalities like rise in crime, noise levels.

The conclusions of these studies suggest that the significance of the positive effect of LRT on real estate can be guided by appropriate and supportive policies. This will require context-specific empirical studies because the generalized solution from the case studies of the Anglo-Saxon countries and Western Europe will not be suitable in India. The conclusion of these reviews hangs in the form of three primary questions:

  1. 1.

    How does the proximity to the new transportation infrastructure affect travel behavior in the residents of locality?

  2. 2.

    How does the transportation infrastructure affect the quality of life environment of the locality neighborhoods? How satisfied are the residents with their housing choices?

  3. 3.

    How does the proximity to public transportation network influence the property prices in the study area?

2 Hedonic Price Modeling

“Hedonics” comes from a Greek word hedonikos, which means pleasure, and in economics it refers to the satisfaction one gets from consumption of goods and services, i.e. utility [10]. HPM is extensively used in housing value and real estate research and even though the accuracy of the results may sometime be off the mark, but it continues to be valid for empirical research in the real estate market [10]. HPM analysis does economic analysis with following five assumptions:

  • Homogenous land/housing market.

  • Perfect competition in the market.

  • Consumers and suppliers are free to enter and exit the market.

  • Consumers and suppliers are perfectly informed about the products and prices.

  • Market is at equilibrium and prices and attributes have no inter-relationship.

HPM applies least squares regression analysis, and there is a linear relationship between the dependent variable and explanatory variables. In case of housing/land market, the observed price (P) is explained using the following parametric land price equation.

$$ P_{i} = f\left( {X_{j} ;\beta_{j} } \right) + \varepsilon_{i} $$
(1)

  • \( P_{i} \) is the assessed land/residential property price of the ith observation,

  • \( X_{j} \) is a vector of quantitative and qualitative attributes of land/residential property,

  • \( \beta_{j} \) is the unknown hedonic hidden price, of the land/residential property for attribute j, and

  • \( \varepsilon_{i} \) is the stochastic error term.

2.1 Functional Form of Hedonic Price Models

As the association between the explanatory and dependent variables in HPM of Housing markets is mostly nonlinear, there are different configuration of HPM functional form for Housing, land and real-estate models to bypass this lack of linearity, which assume that explanatory variables are continuous, not binary in nature. Hannonen [19] proposes that the methodology choice of the correct functional form for the HPM decides the accuracy of estimation process, and an inappropriate choice can make the subsequent analysis invalid. In parametric research, it is essential to work on a variety of alternative model configuration to decide, which suits the land, housing market or its submarket being analyzed. An incorrect selection of functional form can result in unreliable estimates [6, 10]. And even though, it has been practiced for a long time, the theory lacks guideline on the decision of choosing correct functional form for varying application. Among the variety of hedonic price models, the mostly used ones are (i) Linear HPM, (ii) Log-linear HPM, (iii) Linear-Log HPM, and (iv) Double-Log HPM.

The following tables (Tables 1 and 2) summarize the different forms of hedonic price models used in the 21 projects around the world. They also tell us the degree of success these models had in describing the pricing of these housing projects. Tables 3, 4, 5 and 6 list down the respective independent variables as used in these models. The ID column in the latter tables relates with the Tables 1 and 2.

Table 1 Hedonic price models studying impact of BRT and LRT projects
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Table 2 Hedonic price models studying impact of metro & commuter rail projects
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Table 3 Explanatory variables in HPM models with BRT
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Table 4 Explanatory variables in HPM models with LRT
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Table 5 Explanatory variables in HPM models with metro rail
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Table 6 Explanatory variables in HPM models with commuter rail projects
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3 Conclusion and Identified Research Gaps for Further Study

Some of the studies [5] that focus on the large-scale operational and established urban rail/metro investments study the impact on property values. However small-scale modes like LRT/trams are not studied as much. There lies a research gap for these modes. Multiple studies [13, 14, 20) have confirmed similar positive effect of having urban rail transit system closer on the property prices. Although there are other studies that undermine the effect and call it marginal or only important when the supporting conditions are favorable. Out of 24 North American cities studied in 19 different studies, 13 tested positive correlation in property prices, while 4 were found to have no effect of LRT infrastructure. However, 7 cities showed signs of decline in property prices. The results are not ambiguous because the negative correlations have an explanation. The cities where increase in socio-economic inequality was triggered by the LRT infrastructure, the benefits of accessibility and proximity to LRT are outweighed by negative externalities like rise in crime, noise levels.

The conclusions of these studies suggest that the significance of the positive effect of LRT on real estate can be guided by appropriate and supportive policies. This will require context-specific empirical studies because the generalized solution from the case studies of the Anglo-Saxon countries and Western Europe will not be suitable in India. The conclusion of these reviews hangs in the form of three primary questions:

  1. (1)

    How does the proximity to the new transportation infrastructure affect travel behavior in the residents of locality?

  2. (2)

    How does the transportation infrastructure affect the quality of life environment of the locality neighborhoods? How satisfied are the residents with their housing choices?

  3. (3)

    How does the proximity to public transportation network influence the property prices in the study area?