Abstract

Urban economic theory suggests that improved accessibility through transportation investments have the potential to drive up the bids for lands. A number of studies have investigated the impact of rail transit on home sales but produced mixed results. Further, few studies have explored how bus transit influences the lease rate of apartments. This question is more relevant than the relationship between rail transit and home prices because the scale of bus transit is regional as opposed to a narrow corridor of rail transit, and apartment dwellers are more likely to be influenced by transit accessibility than home owners. Using about 400 apartment dwellers in Fargo, this study developed a hedonic price model to determine implicit price of proximity to bus routes. The study found a negative impact of bus transit on apartment rent after controlling for other factors, however. We speculated that this negative relationship could represent spurious relationships from other causal factors as well as nuisance effects of bus transit itself.

Introduction

Transportation systems provide travel options for people to move among spatially-segregated activities such as working, shopping, and entertainment. Therefore, transportation investments that ease movement from one location to another presumably have important impacts on the achievement of social objectives such as reducing congestion and improving the environment (Giuliano 2004; Wegener and Fürst 1999). How the enhanced accessibility affects land value is also important because transit investments are often justified by promoting economic development (e.g., Mackett and Edwards 1998).

The American Public Transportation Association states that "Across the country, dial-a-ride, bus, rail and commuter rail services are providing enhanced travel options and expanding access, often in dramatic ways. Better access means rising market value for adjacent properties and buildings" (APTA undated, p.2). According to urban economics, the relative increase in accessibility provided by transit facilities may increase property value because the larger demand for highly-accessible locations drives up the bid for lands in those locations (Mills and Hamilton, 1994). However, previous studies provide mixed results on how transit infrastructure influences property value. First, some studies found that proximity to rail transit has a positive impact on residential property value (Gatzlaff and Smith 1993; Haider and Miller 2000; Lewis-Workman and Brod 1997; Voith 1991). However, it is known the enhanced accessibility itself is not sufficient to stimulate urban development and increase property value; the positive impact of accessibility greatly depends on other factors such as economic situations, land use policies, and development subsidies (Cervero 1996; Gatzlaff and Smith 1993; Giuliano 2004). On the other hand, transit infrastructure may bring about nuisance effects due to noise and crime. For example, Nelson (1992) found that proximity to transit stations is positively associated with property value in lowerincome neighborhoods but has a negative influence on property value in higher-income neighborhoods, although both neighborhoods are served by the same rail transit. This suggests that nuisance effects of the rail transit exceed accessibility effects in higher-income neighborhoods. Chen et al. (1998) found that property values decrease and then increase as the distance to transit stations increases, an interaction of a positive accessibility effect and a negative nuisance effect.

These studies intensively focused on the impact of rail transit (including heavy rail, light rail, and commuter rail) on home sales. We should also pay attention to the relationship between bus transit and values of rental properties. Although a single rail transit represents a huge amount of investment, bus transit has a much larger network in the region and carries the majority of transit passengers (Pucher 2004). In other words, the impact of rail transit on property value tends to be limited to the local corridor, while the influence of bus transit is likely to be regional due to its extensive network. Therefore, bus transit's influence on property value merits investigation. Generally, transit attracts patrons from people living in the urban core, transit-captives, and some choice users. Therefore, transit investments tend to have a limited impact on individuals' accessibility compared to highway investments. Given that many apartment dwellers are transportation-disadvantaged people, transit access and the level of service may have a larger impact on apartment dwellers than home owners. Further, apartment dwellers tend to value the importance of transportation factors in their residential choices, compared to home owners (Bina et al. 2006a, 2006b; Cao 2007). Therefore, transit infrastructure is more likely to affect lease rates than home prices.

Several studies have pointed to bus transit and/or rent value. Using real estate sales data collected a few years before and after introducing a new bus system in Denver in 1971, Koutsopoulos (1977) found that single-family houses close to bus routes tend to have higher values than those away from the bus system. Bina et al. (2006b) also found the number of bus stops per square mile is positively associated with home prices. Further, Benjamin and Sirmans (1996) showed that proximity to rail stations positively affects the lease rate of apartments in Washington, DC. Cervero (1996) revealed that the distance to BART stations has a negative impact on apartment rent in some neighborhoods but has no influence in other examined neighborhoods in the San Francisco Bay area. Bina et al. (2006a) is one of few studies investigating the influence of bus transit on lease rates. The study found the density of bus stops is negatively associated with apartment rents in Austin, TX. The researchers speculated that the noise of buses and the spread of bus services in lower-income neighborhoods might contribute to this negative association. The opposite impacts of the bus system on sale prices and lease rates may also arise from different sampling methods used in these two studies (Bina et al., 2006a; 2006b): choice-based sample vs. random sample. Bina et al. (2006a) pointed out the drawback of a choice-based sampling method and highly recommended a random sampling approach.

The purpose of this study is to explore the influence of transit facilities on the lease rate of apartments using the data randomly collected from apartment dwellers in Fargo, North Dakota. It aims to answer the following question: do transit services add value to adjacent apartments? The next section briefly reviews the hedonic price model. Section 3 describes the data and variables. Section 4 presents the results of correlation analysis and the hedonic model. The final section discusses the underlying reasons for the model results.

Hedonic Price Model

The hedonic price model is commonly used to determine the impact of transportation investments on property value. The model assumes that goods are characterized as a package of inherent attributes, and the observed prices of goods reflect the utility (or implicit prices) of these attributes (Rosen 1974). Therefore, the value of a residence is the summation of implicit prices for the characteristics associated with the residence. What constitutes the characteristics of a residential property? Previous research points to location, structure, and neighborhood attributes (e.g., Chin and Chau 2003; Lewis-Workman and Brod 1997). Some of these attributes are summarized in Table 1.

In mathematical form, the hedonic function of an apartment can be expressed as:

where Y stands for the dependent variable: rent of an apartment; L, S, and N denote location, structural, and neighborhood characteristics of the apartment, respectively. The partial derivative of the function with respect to an attribute represents the marginal implicit price (shadow price) of that attribute. For a linear regression model, the coefficient of an attribute is the shadow price of that attribute.

Data and Variables

The data used in this study comes from a self-administered telephone survey conducted in Fargo, North Dakota. Fargo, located in the Red River Valley region, is a typical small city in the Midwest (Figure 1). The city's land area is about 30 square miles, and the population was 90,599 in the 2000 census. Fargo is a major transportation hub for the surrounding regions: two interstate highways (I29 and I94) run across the city (Figure 2). Inside the metropolitan area, Metro Area Transit (MAT, http://www.matbus.com/) operates 18 routes to provide transit services for three adjacent cities: Fargo, West Fargo, and Moorhead. In 2006, Fargo MAT provided about 900,000 one-way passenger trips.

Figure 1. Geography of Fargo in the Region

Figure 2. Residential Locations of Respondents and Transit Routes.

Note: The dots are observations and the lines with arrows are bus routes.
A detailed route map is available at http://www.matbus.com/Documents/FargoBusRoutes.pdf.

Survey questions were developed from questionnaires used in previous research projects by the first author and Dr. Kara Kockelman. The survey was pre-tested on students and staff of North Dakota State University. Participants were asked first to complete the survey, then to discuss the survey questions with the researchers in one-on-one interviews. Based on these pretests, survey questions were modified and refined.

A database of apartment dwellers was purchased from AccuData America (http://www.accudata.com/). In May and June 2007, a contract interviewer from the National Agriculture Statistics Service phoned respondents randomly selected from the database. Since those who do not answer the phone may substantially differ from those answering the phone, a callback procedure was adopted. As an incentive to complete the survey, respondents were told they would be entered into a drawing to receive one of five $50 cash prizes. Ultimately, among 1,395 individuals who answered the phone, 415 no longer lived in apartments. The number of responses totaled 424, equivalent to a 43.2% response rate based on the valid respondents only. As shown by the dots in Figure 2, most of the respondents were gathered in several locations, which reflects the cluster of apartment buildings. Note that 26 respondents were removed from the analysis because they either lived in senior centers or subsidized apartments, and the nominal rent they reported does not reflect the true value of properties.

In the survey, respondents were asked to report their monthly rent (the total rent if they shared an apartment). Moreover, a series of questions asked attributes of the apartment (e.g., number of bedrooms and bathrooms as listed in Table 2). These attributes serve as control variables. As shown later in Table 3, respondents were also asked to indicate how true 20 attributes were for their current apartments and neighborhoods, on a four-point ordinal scale from "Not at all true" (1) to "Entirely true" (4). The characteristics as perceived by respondents reflect fundamental differences in attributes of residential environments.

Following the survey, several land use characteristics were calculated at the traffic analysis zone (TAZ) level. Using the data from regional travel demand forecasting model, the study first computed a few density measurements and vehicular travel time to the central business district (CBD) as shown in Table 2. Further, regional accessibility was computed using the following gravity-based measure:

A_i = O_jf(t_ij) = O_j(a * t_ij^b * e^c*t_ij),

where A_i is the accessibility of TAZ(i); O_j is the number of jobs in TAZ(j); ƒ(t_ij) is the friction function to travel between TAZ(i) and TAZ(j). Here, calculations adopted the HBW (home-based work) Gamma function coefficients for friction factors where a = 28507, b = -0.020, and c = -0.123. Using GIS, three dummy variables were created to indicate whether a respondent lived within 1/8, 1/4, or 1/2 mile (network distance) of transit routes, respectively. In Fargo, although there are designated bus stops, the MAT bus stops at a shelter location or any corner as long as it is safe traffic-wise. Therefore, the distance to the transit route is actually the network distance to the bus stop.