Rail Transit's Effect on Property Values
A fair amount of the academic literature dealing with modern streetcar and light rail systems is geared towards understanding and documenting the economic development potential of these forms of transportation. One of the most common forms these studies take is what is known as a hedonic model of residential and/or commercial property values.
Essentially, a hedonic model attempts to compare values of residential and/or commercial properties that are situated near transit lines or stations (a buffer distance of about a quarter mile, or 1,320 feet, is common) before and after the implementation of a new transit project to determine the effect the new transit project has had on those property values. Hedonic models also control for the potential influence of other variables on a given property's value (for example: how many bedrooms or bathrooms a house has, how large a house's lot is, what neighborhood a house is in).
A more sophisticated variant of the basic hedonic model differentiates between properties that are adjacent to a transit line or station and those that are further away, but still within the buffer zone. Often, properties closest to stations have higher premiums for their property values than those further away, but still within the defined buffer zone. Another variant defines the buffer zone by accounting for actual on the ground walking distances from transit lines or stations, rather then defining them by using a simple linear amount of space from the transit station or line. In this variant, spaces with more sidewalks and streets that intersect with one another will generally create larger buffer zones than spaces without these attributes.
Essentially, a hedonic model attempts to compare values of residential and/or commercial properties that are situated near transit lines or stations (a buffer distance of about a quarter mile, or 1,320 feet, is common) before and after the implementation of a new transit project to determine the effect the new transit project has had on those property values. Hedonic models also control for the potential influence of other variables on a given property's value (for example: how many bedrooms or bathrooms a house has, how large a house's lot is, what neighborhood a house is in).
A more sophisticated variant of the basic hedonic model differentiates between properties that are adjacent to a transit line or station and those that are further away, but still within the buffer zone. Often, properties closest to stations have higher premiums for their property values than those further away, but still within the defined buffer zone. Another variant defines the buffer zone by accounting for actual on the ground walking distances from transit lines or stations, rather then defining them by using a simple linear amount of space from the transit station or line. In this variant, spaces with more sidewalks and streets that intersect with one another will generally create larger buffer zones than spaces without these attributes.
Documenting potential increases in property values is especially important in our case, since one of the major justifications for developing the streetcar line in downtown Cincinnati was the prospect of economic development, the main component of which was calculated gains in property values (HDR 2007). Increases in property values based on proximity to transit enriches both the property owner, who has more valuable property, and the relevant government entity, which can collect more taxes based on the assessed market rate of properties in their jurisdiction.
The section immediately below looks at how streetcar transit could potentially affect property values in Northern Kentucky (n.b.: the study in the section below does not use hedonic modeling). The Additional Literature section towards the bottom of this page provides an overview of recent academic literature on how proximity to rail transit affects property values in other cities around the US.
The section immediately below looks at how streetcar transit could potentially affect property values in Northern Kentucky (n.b.: the study in the section below does not use hedonic modeling). The Additional Literature section towards the bottom of this page provides an overview of recent academic literature on how proximity to rail transit affects property values in other cities around the US.
Rail Transit's Potential Effect on Property Values in Northern Kentucky
We borrow key ideas from the hedonic modeling concept described above to
understand how implementing a streetcar in Northern Kentucky would
affect property values in Covington and Newport. It's important to note that what follows is not a hedonic model, since they can only be formally constructed after a project has actually been implemented.
The line chosen for analysis is the current Southbank
Shuttle route, which is a logical route to situate a streetcar on,
given the overall success the service has enjoyed and the relatively
high densities of the areas in which it operates.
The study uses 2012 American Community Survey data for median value of house prices at the most disaggregated level available (for this specific data, the level of the census block group). We used census block groups for our level of analysis since accessed property values for individual properties in Kenton Country are not accessible to the public as large tables suitable for easy analysis.
Geographic information systems (GIS) software was used to determine the number of properties within one-quarter mile (1,320 feet) of the prospective streetcar system's lines, since this is typically the distance used in academic literature (please see the Additional Literature section below for an overview of recent academic literature on how proximity to rail transit affects property values in other US cities). To be clear, we use a simple linear measurement from the line to define our one-quarter mile buffer zone. Studies in the academic literature available typically focus on property value changes within one-quarter mile of stations (not lines) for the purposes of analysis, since stations are where users access public transit. Still, because streetcars typically have stops every few blocks, looking at properties within one-quarter mile of a streetcar line is reasonable.
Data from NKAPC was used to associate properties with land use type (for example, residential, commercial). The data available for Covington (Kenton County) was more fine-grained than data for Newport (Campbell County); for example, information on whether or not a property was vacant was available for Kenton County, but not for Campbell County.
Using this data to calculate projected increases in property values is fairly simple. We determine the number of properties within one-quarter mile of the prospective streetcar route by block group and land use type; for our purposes, we're only interested in residential properties. We then multiply the number of residential properties by the block group's median home value. The resulting figure is then multiplied by the percent range of potential value increases (2.5, 5, and 10 percent). The rationale for selecting this range of potential value increases is described below.
Provided in the table below is a range of calculated potential increases in property values in dollars (the percent increase range being 2.5, 5, and 10 percent). To be clear, we are borrowing our percent range of potential property value increases from other studies. The most conservative estimate (a 2.5% increase) is roughly equivalent to the lowest value increase in the examined literature (Buffalo's light rail system only boosted property values there between 2-5%) (Hess and Almeida 2007). The percentage increases in HDR's report for Cincinnati's streetcar are generally around 5%; in its report, HDR suggests that this is a conservative number, and can be expected to be fulfilled, or exceeded, with a very high degree of confidence (2007). A 10% increase in housing values may seem like an overly-optimistic prediction, but such value increases are not that uncommon in the examined academic literature. We are being purposefully conservative in the range of figures presented so as to increase our general confidence that this range represents probable outcomes that may be exceeded. A somewhat extreme example is the case of St. Louis, which experienced gains in property values of about 32% for residential properties adjacent to its light rail stations studied (Garret 21).
The study uses 2012 American Community Survey data for median value of house prices at the most disaggregated level available (for this specific data, the level of the census block group). We used census block groups for our level of analysis since accessed property values for individual properties in Kenton Country are not accessible to the public as large tables suitable for easy analysis.
Geographic information systems (GIS) software was used to determine the number of properties within one-quarter mile (1,320 feet) of the prospective streetcar system's lines, since this is typically the distance used in academic literature (please see the Additional Literature section below for an overview of recent academic literature on how proximity to rail transit affects property values in other US cities). To be clear, we use a simple linear measurement from the line to define our one-quarter mile buffer zone. Studies in the academic literature available typically focus on property value changes within one-quarter mile of stations (not lines) for the purposes of analysis, since stations are where users access public transit. Still, because streetcars typically have stops every few blocks, looking at properties within one-quarter mile of a streetcar line is reasonable.
Data from NKAPC was used to associate properties with land use type (for example, residential, commercial). The data available for Covington (Kenton County) was more fine-grained than data for Newport (Campbell County); for example, information on whether or not a property was vacant was available for Kenton County, but not for Campbell County.
Using this data to calculate projected increases in property values is fairly simple. We determine the number of properties within one-quarter mile of the prospective streetcar route by block group and land use type; for our purposes, we're only interested in residential properties. We then multiply the number of residential properties by the block group's median home value. The resulting figure is then multiplied by the percent range of potential value increases (2.5, 5, and 10 percent). The rationale for selecting this range of potential value increases is described below.
Provided in the table below is a range of calculated potential increases in property values in dollars (the percent increase range being 2.5, 5, and 10 percent). To be clear, we are borrowing our percent range of potential property value increases from other studies. The most conservative estimate (a 2.5% increase) is roughly equivalent to the lowest value increase in the examined literature (Buffalo's light rail system only boosted property values there between 2-5%) (Hess and Almeida 2007). The percentage increases in HDR's report for Cincinnati's streetcar are generally around 5%; in its report, HDR suggests that this is a conservative number, and can be expected to be fulfilled, or exceeded, with a very high degree of confidence (2007). A 10% increase in housing values may seem like an overly-optimistic prediction, but such value increases are not that uncommon in the examined academic literature. We are being purposefully conservative in the range of figures presented so as to increase our general confidence that this range represents probable outcomes that may be exceeded. A somewhat extreme example is the case of St. Louis, which experienced gains in property values of about 32% for residential properties adjacent to its light rail stations studied (Garret 21).
As shown in the above table, the conservative 5% increase in residential values adds $11,580,175 of value for 1,486 properties. The more optimistic 10% figure yields $23,160,350, while the 'worst-case' 2.5% figure yields just $5,790,088. Please see the study appendix for more detailed tables arranged by block group and land use type.
It's important to note that our study only looks at increases in value for residential properties (not commercial properties). This is due to the aforementioned issues relating to the availability of property value data in Kenton County for individual properties. Still, situating a streetcar in Northern Kentucky would almost certainly have an effect on commercial property values too, as is supported by HDR's economic analysis for the Cincinnati streetcar project and other available academic literature (HDR 2007, 28-31). This is significant for our case, since about 33% (801 of the 2,413) properties within the quarter mile buffer zone analyzed are commercial properties. Also, our study does not account for the growth of the number of available units (both residential and commercial) as HDR's study does for Cincinnati (HDR 2007, 34).
Data sources:
NKAPC. GIS data taken from University of Cincinnati, College of DAAP 'R drive'. 2010. Accessed February 4, 2014.
U.S. Bureau of the Census. Data taken from Summary File Retrieval Tool, data for Kentucky by block group, "Table: B25077 - Median Value (Dollars)." 2012. Accessed March 31, 2014. http://www.census.gov/acs/www/data_documentation/summary_file/
N.b., to access the data, you must first download the Summary File Retrieval Tool.
It's important to note that our study only looks at increases in value for residential properties (not commercial properties). This is due to the aforementioned issues relating to the availability of property value data in Kenton County for individual properties. Still, situating a streetcar in Northern Kentucky would almost certainly have an effect on commercial property values too, as is supported by HDR's economic analysis for the Cincinnati streetcar project and other available academic literature (HDR 2007, 28-31). This is significant for our case, since about 33% (801 of the 2,413) properties within the quarter mile buffer zone analyzed are commercial properties. Also, our study does not account for the growth of the number of available units (both residential and commercial) as HDR's study does for Cincinnati (HDR 2007, 34).
Data sources:
NKAPC. GIS data taken from University of Cincinnati, College of DAAP 'R drive'. 2010. Accessed February 4, 2014.
U.S. Bureau of the Census. Data taken from Summary File Retrieval Tool, data for Kentucky by block group, "Table: B25077 - Median Value (Dollars)." 2012. Accessed March 31, 2014. http://www.census.gov/acs/www/data_documentation/summary_file/
N.b., to access the data, you must first download the Summary File Retrieval Tool.
Additional Literature
![Picture](/uploads/2/6/0/6/26062495/3497166.jpg?342)
There have been numerous studies on the effect proximity to public transit has on property values. Although the changes in value are almost always positive, there have been a broad range of percent increases observed. Although there are exceptions, growing cities with robust economies (for example, San Francisco) usually realize higher gains in values than shrinking and economically struggling cities (like Buffalo).
In a larger study from 1993 which looked at how accessibility to Philadelphia’s CBD affects suburban residential property values, it was found that houses with easy access to light rail service enjoy a 5% premium over houses that do not have this advantage (Voith 1993). The study also found that the effect of the value of this accessibility was positively correlated with the economic well-being of the CBD over time; put more simply, it was found that when the CBD was strong economically, ease of access to rail for public transportation was valued more highly than when the CBD was slumping (Voith 1993).
A 2004 study looking at the effect of proximity to light rail stations in St. Louis found a very high positive correlation, a 31.25-32.72% increase in value for properties within 1,460 feet (a little over one-quarter mile) of light rail stations studied (Garret 21). Still, the author notes that these huge jumps in value likely have as much to do with the initially very low property values for homes in the neighborhoods studied as they do with the real value of rail station proximity (Garret 21). For example, a 30% increase on a $40,000 house is $12,000, whereas a $12,000 increase on a $300,000 house is only a jump in value of 4%; the percentages can be misleadingly positive in the former, since the base value is comparatively lower.
One of the most recent studies from 2007 looks at the economic effect of Buffalo’s underutilized light rail system. Despite the city’s declining population and the light rail systems weak network connectivity (one line with fourteen stations), a modest positive effect on residential property values can be attributed to light rail station proximity; in this case, homes located within one-quarter mile of stations enjoy a 2-5% premiums over similar homes outside these zones (Hess and Almeida 2007). However, this study also found that proximity to stations in low-income areas actually had a negative effect on property values (Hess and Almeida 2007). Also, other variables exerted more of an influence than light rail proximity in the study, including: the number of bathrooms in a home, the size of the parcel, and which side of town (East or West) the home was located in; the study also found that network walkability had a statistically significant impact on the effects of valuation (Hess and Almeida 2007). The take-away from this is that it is easy to draw a quarter mile circle around a station and call this a ‘transit-oriented development (TOD) area,’ but it’s important that this zone be practical to walk in for pedestrians to easily access the stations. This point is particularly salient when comparing growth patterns typical of dense urban areas and less dense suburbs.
In a larger study from 1993 which looked at how accessibility to Philadelphia’s CBD affects suburban residential property values, it was found that houses with easy access to light rail service enjoy a 5% premium over houses that do not have this advantage (Voith 1993). The study also found that the effect of the value of this accessibility was positively correlated with the economic well-being of the CBD over time; put more simply, it was found that when the CBD was strong economically, ease of access to rail for public transportation was valued more highly than when the CBD was slumping (Voith 1993).
A 2004 study looking at the effect of proximity to light rail stations in St. Louis found a very high positive correlation, a 31.25-32.72% increase in value for properties within 1,460 feet (a little over one-quarter mile) of light rail stations studied (Garret 21). Still, the author notes that these huge jumps in value likely have as much to do with the initially very low property values for homes in the neighborhoods studied as they do with the real value of rail station proximity (Garret 21). For example, a 30% increase on a $40,000 house is $12,000, whereas a $12,000 increase on a $300,000 house is only a jump in value of 4%; the percentages can be misleadingly positive in the former, since the base value is comparatively lower.
One of the most recent studies from 2007 looks at the economic effect of Buffalo’s underutilized light rail system. Despite the city’s declining population and the light rail systems weak network connectivity (one line with fourteen stations), a modest positive effect on residential property values can be attributed to light rail station proximity; in this case, homes located within one-quarter mile of stations enjoy a 2-5% premiums over similar homes outside these zones (Hess and Almeida 2007). However, this study also found that proximity to stations in low-income areas actually had a negative effect on property values (Hess and Almeida 2007). Also, other variables exerted more of an influence than light rail proximity in the study, including: the number of bathrooms in a home, the size of the parcel, and which side of town (East or West) the home was located in; the study also found that network walkability had a statistically significant impact on the effects of valuation (Hess and Almeida 2007). The take-away from this is that it is easy to draw a quarter mile circle around a station and call this a ‘transit-oriented development (TOD) area,’ but it’s important that this zone be practical to walk in for pedestrians to easily access the stations. This point is particularly salient when comparing growth patterns typical of dense urban areas and less dense suburbs.
Extreme variations of connectivity (Points A and B are the same distance apart in both images)
Image source: Kelly 2010, 24
Image source: Kelly 2010, 24
We have focused strategically on cities that have implemented rail transit and that also have population demographics roughly similar to Cincinnati’s. This represents a small component of the available literature. Other studies recently done on the rail transit's influence on property values include: a 2002 study looking at Dallas’ DART light rail system, two studies from 2002 and 2011 that look at San Diego’s trolley and light rail systems respectively, a 2012 study looking at light rail in Phoenix, and a 2012 study looking at light rail in Charlotte. Significantly, all of these studies reveal positive gains on property values. Key findings from these studies are summarized in the table below.