The Effect of the Light Rail on Home Prices in

Norfolk: A Difference-In-Difference Approach

Abstract:

This study looks to analyze the effect of light rail transit (The Tide) on residential

property values in Norfolk, Virginia. The Tide opened in 2011, and the study period

spans 2005-2013, using sales price of homes sold in Norfolk. We account for home

characteristics, as well as census block by month by year fixed effects. Unlike a majority

of the light rail literature, this study uses a Difference-In-Difference estimation to

measure if The Tide has had any effect on home prices. We fail to find a statistically

significant effect on home prices within 500m of The Tide.

Julia Martin

Budget and Policy Analyst

City of Norfolk

julia.martin@norfolk.gov

Gary A. Wagner

Regional Economic Advisor

Federal Reserve Bank of Philadelphia

Gary.Wagner@phil.frb.org

Timothy Komarek

Assistant Professor of Economics

Old Dominion University

tkomarek@odu.edu

1. Introduction

The modern light rail systems in American cities came about in the 2000’s as a

way to reduce traffic congestion, provide an affordable and sustainable mode of transport,

and to spur economic activity. Light rail systems differ from their other rail counter parts

(heavy and commuter) in that they are easily maneuverable, occasionally automatically

piloted, are much less invasive to construct, and in some cases can run on roadways

shared with cars.

Light rail presents an opportunity for city planners to significantly influence

transit-oriented development (TOD) through specifically chosen station locations. The

station locations are often chosen with increased accessibility and reduced transportation

costs to a central business district (CBD) or downtown in mind. It is a general proposition

of transportation economics that a reduction in transportation costs to a CBD or

downtown will be capitalized in a home’s property value (Garrett, 2004). Unfortunately,

when analysis has been conducted on proximity to light rail and its effect on property

values, the results have been rather ambiguous across all types of properties.

This study looks at the light rail system known as The Tide serving Norfolk,

Virginia. As a proxy for an individual’s willingness to pay for the increased accessibility

due to The Tide, the residential sales price from 2005-2013 serves as our dependent

variable. Alonso (1964) suggests a relationship between increased accessibility and home

prices. This study is unique in a few respects: (1) The Tide opened in 2011 making it one

of the newest light rail systems in the United States (2) we use difference-in-difference

(DID) estimation rather than hedonic price analysis (HPA) to control for unobservable

factors that affect home price.

The remainder of the paper is as follows: section 2 provides a brief review of

relevant literature on the effect of light rail systems on home prices, section 3 describes

our data set and provides summary statistics, in section 4 we put forth our methodology,

section 5 contains the empirical results of our analysis, and section 6 summarizes our

findings.

2. Literature Review

In urban settings proximity to downtown has become increasingly scarce and

valuable. Given a city’s layout, there exists a finite amount of land upon which new

structures can be developed. This can be seen by the fact that from 1930 to 2006, the

percent of home value accounted for by land has increased from 15% to 47% (Shiller,

2007). Homeowners are not just paying for the physical structure, but a variety of other

attributes that come along with the location of the home. Transportation literature

suggests that proximity to public transportation may be an attribute that accounts for a

relative rise in home price. Individuals are willing to pay more for a home as a tradeoff

for a reduction in transportation costs to downtown (Garrett, 2004).

When analyzing the effect of a light rail system on home prices, researchers are

looking for the accessibility effect (increase in home prices) or nuisance effect (decrease

in home prices).1 However, there fails to be a consensus among the literature as to how

far the expected effect can permeate. This is due to the wide variety of light rail systems

(straight line, spoke, 2 tracks) and varying demographics of areas where these systems

are built.

1 Nuisance effect can be due to a variety of reasons: increased noise level, increased traffic, having to regularly wait at street crossings for the light rail to pass, bringing lower income individuals and as a by-product increased perception of crime into a neighborhood. Homeowners who observe any of these effects, and do not value (or place less value) on the increased accessibility due to living near a light rail station are said to experience the nuisance effect.

In Charlotte, North Carolina, Billings (2011) found homes within ½ mile of a

proposed station experienced a decrease in home values while homes between ½ -1 mile

saw an increase in home values. The homes within ½ mile of a proposed station are

anticipating increased nuisance effects due to being near a station as is evidence by the

decrease in home prices.2

While it might appear logical that all homes near a light rail station would

experience some sort of nuisance effect, it is not always the case. Chatman et al. (2012)

used hedonic price analysis on the log difference of home price along the New Jersey

River Line, and found homes within ¼ of a mile experienced an appreciation rate of

11.9% relative to other homes.3 This positive finding within ¼ mile contradicts that of

Pan (2013) who finds that homes within this distance experience between a 17%-30%

decrease in price relative to all other homes sold in Houston.4

In Buffalo, New York it was found that homes within ¼ mile and ½ mile,

measured both as straight-line and walking distance, observed higher assessed values

(Hess & Almeida, 2007)5. The massive economic downtown and population decay in

Buffalo altered the approach of the authors to look at the effects of similar homes in

neighborhoods with drastically different demographics. The finding was that homes

located in neighborhoods with higher incomes had a larger increase in assessed value as

2 A similar finding in Charlotte by Yan et al. suggests that home prices near a light rail station decrease

during the construction phase. However, positive time trend was found; as the light rail opens home prices

increase as distance to the light rail station decreases (2012). 3 It should be noted the New Jersey River Line is a light rail-commuter rail hybrid. The train acts as a light

rail in town centers, but as it reaches high speeds along the 34 miles of track it serves as a commuter rail. 4 Pan conducts a series of robustness checks using hierarchical regression techniques and ordinary least

squares. He finds a 17% decrease in price using the multi-level regression model and 30% reduction with

OLS. Pan indicates that the magnitude is less important than the sign, which is consistently negative within

¼ mile. 5 Hess and Almeida find homes within ½ mile of a light rail station are assessed at 2 to 5% higher relative

to similar properties located outside ½ mile.

distance to a station decreased relative to similar homes in lower income neighborhoods.

This finding is congruent with the results of Fan et al. (2012), who found that in

Minneapolis the light rail greatly increased job accessibility for workers with low,

medium, and high paying jobs. All workers value increased accessibility, however; only

those with large enough incomes can afford to own the premium properties near the

station.6

This neighborhood income result observed by Hess and Almeida differs from the

findings of Cervero (2004) in San Diego. The latter, who studied 4 stations along the San

Diego light rail system and found the only homes who responded positively to the light

rail were the homes in the area with the lowest home value on average. Cervero (2004)

found between a $5,659 and $48,707 price reduction for single family homes within ½

mile with the exception of the homes located in the area with the lowest average home

price. In that case, the homes experienced a $6,774 price premium for proximity within

½ mile of a station. 7

Chen at al. (1997) limit their sample to only homes within 1000 meters of the

Portland MAX, and they suggest individuals are unwilling to walk farther than 1000m to

public transportation. Rather than accounting for home prices as their distance relates to a

station, they also measure home prices as their distance relates to the physical line itself.

6 Low wage workers earn below $1,200/month, medium wage workers earn between $1,200-$3,400/month

and high wage workers earn greater than $3,400/month. The dependent variable was the number of jobs

before the light rail station and after the light rail station within 30 minutes of transit time. Transit time can

be by bus, rail connection, and walking. 7 The reduction of $5,659 and $48,707 in single family homes found by Cervero (2004) are statistically significant at the 1% level. The lone premium of $6,774 is not statistically different from 0.

A positive effect on homes was found for homes within 700m of a light rail station

whereas, a negative effect was found for homes within 700m of the light rail line. 8

As shown above, there is no clear agreement among the studies as to what the

expected effect of a light rail system should be on home prices. In Norfolk, we do not

expect The Tide to have a statistically significant effect on home prices for a number of

factors, namely the track is relatively short at only 7.4 miles and The Tide does not offer

increased accessibility to major areas in Norfolk (Old Dominion University and Norfolk

Naval Base).

3. Data Description

3.1 Home Data

The housing data used in this analysis was obtained from the Hampton Roads Multi-List

Service, Real Estate Information Network (REIN). The data includes all homes sold in

Norfolk from January 2005 until December 31, 2013. The Tide line has four stops that

are located in downtown Norfolk. Our data includes attached as well as detached homes;

this allows us to include downtown condos in our sample. The descriptive statistics

included are list price, date on the market, date off the market, sale price, number of

bedrooms, bathrooms, and half baths, year the home was built, style, square footage, and

address. After inspecting the data, outlying observations were dropped; this caused the

total number of home observations to drop from 21,439 to 21,255. 9 ,10

8 Homes within 700m of the light rail line decrease by -2.538% for every meter closer to the line. Homes

within 700m of a light rail station increase by 2.647% for every meter closer to a light rail station (Chen et

al. 1997). 9 Observations were dropped if the following outlier conditions were met: age equal to 0 or greater than

150, square footage less than 1 foot or greater than 9,700ft, and if minimum distance to a station was

greater than 20,000 meters. These outlier observations are omitted either for human error entering the data,

or because the home characteristics are so unique it is unlikely that they face the same real estate market as

other homes. For example a home more than 150 years old is likely to have a niche real estate market.

Using the addresses obtained from REIN, we were able to geocode the data which

gave us the latitude and longitude coordinates for each home. We used the latitude and

longitude to assign each home to its census block, and this is used to account for

unobservable differences in demographics among neighborhoods. The address for each

station is given on the Hampton Roads Transit website, and the 11 station addresses were

translated into latitude and longitude coordinates. The distance between each home and

station were calculated as straight-line or “as the crow flies” distance. The distance is

reported in meters, and each home was assigned to a station based on its minimum

distance.

Table 1 includes summary statistics for all homes sold within our observation

time period, and then restricted by different bands of minimum distance to a light rail

station. The average sale price, square footage, number of full baths and bedrooms, and

age were respectively $210,675, 1631ft, 1.7 baths, 3.1 bedrooms, and 56.9 years old.

Within our sample: 5.4% are within 500 meters of a light rail station; 7.6% between 500

meters and 1000 meters; and 6.3% between 1000 meters to 1500 meters. Homes within

500 meters of a light rail station tend to be slightly smaller, newer and more expensive

relative to all other homes. These preliminary differences provide the motivation to delve

deeper into the effect of the light rail station openings on home prices.

3.2 The Tide and Its Stations

Norfolk is home to the world’s largest naval base, and Norfolk’s downtown

largely serves as the economic hub for the Hampton Roads region.11

The City of

10

Age was calculated as year built-2014. Since our sample ends in 2013, it is not mathematically possible

for a home to have an age of 0. 11

Hampton Roads includes the following cities in south eastern Virginia: Virginia Beach, Norfolk,

Portsmouth, Chesapeake, Newport News, Hampton, Poquoson, York County, Williamsburg, James City

Norfolk’s light rail, The Tide, opened in August of 2011. The rail itself runs 7.4 miles of

straight-line track with 11 stations running to the border of Norfolk and Virginia Beach.

The Tide is serviced by a 9 vehicle fleet, each controlled by an on-board operator. The

line starts at Eastern Virginia Medical School; a medical school of 1,200 students which

is located in a larger medical park that includes Sentara Norfolk General Hospital and

Children’s Hospital of the King’s. Along the line, station points include four stops that

run through downtown Norfolk, a stop at Harbor Park (home to the Triple-A Norfolk

Tides), and a station at Norfolk State University; a public university of roughly 7,000

students. For a majority of track, including downtown, The Tide runs along existing

roadways and shares the space with motor vehicles. Construction on The Tide began on

December 8, 2007 and was completed roughly four and a half years later in August 2011.

Figure 1 provides a map of the track, and buffer rings of 1500m. Table 2 provides

the number of homes that are assigned by minimum distance to each station, and whether

the station is located in downtown Norfolk, a park and ride is present, and whether a bus

connection is available. It is reasonable that the first stop (EVMS) is the station where the

largest number of homes are assigned using minimum distance. Downtown Norfolk is

home to few residential properties, and the data corroborate this fact.12

Four of the 11

stations have a designated spot for Park and Ride, and 6 of the stations have bus service.

The final column in this table contains the straight-line distance between two successive

stations. This presents a causation concern in the regression analysis because it is likely

there are homes that are included as a treatment for 1 station and a control for another. If

County, Gloucester County, Mathews County, Suffolk, Isle of Wight County, and Gates County and

Currituck County, both of which are located in North Carolina. (State of the Region 2013) 12 The four stations downtown (York St, Monticello, MacArthur Square, and Civic Plaza) account for 1,956 homes. These four stations are responsible for 36% of the stops along The Tide, while only providing 9% of the homes.

we again reference Figure 1, we can see that every station has overlapping buffer rings at

the 1500m distance. This occurrence will lead to downward bias in our standard errors,

which may falsely indicate statistical significance. We account for this in sections 5.2 and

5.3 by limiting our sample size.

4. Empirical Strategy

Land upon which a structure sits is increasingly valuable, and the price an

individual is willing to pay for a home is often a function of housing characteristics and

location. (Shiller, 2007) The previous literature on home prices and light rail systems

largely relies upon hedonic price analysis to model the relationship, and I failed to find a

study that applies difference in difference to analyze the effect of a light rail system on

home prices. In traditional hedonic price analysis developed by Rosen (1974), the change

in a home’s price caused by a change in the explanatory variables is known as the

implicit price of the attribute. This model is so widely used because it is relatively easy to

alter the attributes that affect the price of a home (Hess & Almeida, 2007 and Cervero,

2006).

Unfortunately, there are a number of issues associated with this estimation. The

model can be subjected to omitted variables bias and multicollinearity among the

explanatory variables (Chen et al. 1997). Light rail stations are often specifically built in

areas in an attempt to direct economic activity to a certain area. Failing to account for

demographic differences between areas surrounding different stations will cause implicit

price estimates to be biased downward in areas with greater crime, lower income, etc.

Hedonic price analysis is also subjected to heterogeneity across individual tastes across a

sample, and this can lead to selection bias. 13

There have been a multitude of variations

upon the traditional hedonic price analysis; however, it is still uncertain whether these

models are not subject to omitted variables bias or multicollinearity. 14

To provide a more comprehensive model to express the factors influencing the

price of a home, an experimental methodology known as difference-in-difference (DID)

can be employed. The DID estimation lends itself to analysis when a natural experiment

is observed; a distinct before and after time period exists and organic treatment and

control groups form. Rather than just comparing the price change for homes within 500m

of a light rail station, DID compares the price change for homes within 500m relative to

the price change of homes outside of 500m. A key assumption of DID is that the trend in

the observations (in our case home prices) would be the same, absent an exogenous

change that only affects a portion of the sample (Card & Krueger, 1994).

Using DID with spatial analysis is exceptionally useful because it allows us to

account for variables that change over time, while controlling for variables that affect a

home’s price but do not vary over time. This enables us to use pooled cross section data

rather than strict panel because we can control for unobservable factors affecting home

prices over time. Relative to hedonic price analysis, DID has two key advantages 1)DID

addresses problems of omitted variables bias and multicollinearity by including time

invariant variables and 2) DID captures the before and after effect of an exogenous

13

Chay and Greenstone (2005) analyze the effect of clean air on home prices. They state if an individual

has a preference for clean air and self-selects to a location outside the study area based on this unobservable

factor, the hedonic price analysis would only observe those individuals who do not care about clean air and

offer no information about those who do. 14

Chatman et al. (2012) use repeat sales data; the issue here is how to control for homes that are more

likely to be re-sold due to unobservable factors. Pan (2013) in Houston runs a hierarchical regression

controlling first for home characteristics then a set of aggregate variables measured at the census tract, city,

and county level. Pan (2013) admits that in trying to control for a variety of factors that affect home price,

he has subjected his model to multicollinearity.

change on our treatment and control groups (Chay & Greenstone, 2005 and J. Dube et al.

2014).

Our model follows from Pope and Pope (2012) who used DID to analyze the

effect of opening a Wal-Mart on home prices. Our DID model is:

𝐿𝑛(𝑃𝑖𝑦𝑚𝑓) = 𝛼𝑓𝑦𝑚 + 𝜸𝑿𝒊 + 𝛽0𝐷𝑖 + 𝜃0𝐷𝑖 + 𝛿0𝐷𝑖 + (𝛽1𝐷𝑖 + 𝜃1𝐷𝑖 + 𝛿1𝐷𝑖) ∗ 𝑃𝑜𝑠𝑡𝑖𝑦𝑚 + 휀𝑖𝑦𝑚𝑓

The log of sale price is a function of an individual specific (𝛼𝑓𝑦𝑚) effect accounting for

census block specific demographics (f) by year (y) by month (m), measurable individual

(i) home attributes (𝑿𝒊), indicator variables (𝛽, 𝜃, 𝛿) of individual homes whose minimum

distance to a station is within 500m, 500m to 1000m, and 1000m 1500m respectively,

interactions of each of these indicator variables with 𝑃𝑜𝑠𝑡𝑖𝑦𝑚 if the home was sold after

the light rail opened, and a random error term (휀𝑖𝑦𝑚𝑓) that allows for year by month by

census block specific correlation. The parameters of interest are the interaction terms

between the spatial indicator variables and the indicator for homes sold after the light rail

opened ((𝛽1𝐷𝑖 + 𝜃1𝐷𝑖 + 𝛿1𝐷𝑖) ∗ 𝑃𝑜𝑠𝑡𝑖𝑦𝑚).

In our experiment, the exogenous change is the opening of the Tide, the treatment

groups are homes within 500m, between 500m and 1000m, and homes between 1000m

and 1500m straight-line distance of a light rail station, and the control group is homes

outside 1500m of a station. These base distances were chosen based off the literature,

which suggests individuals are willing to walk up to a mile to get to a light rail station

(Billings, 2011). We will estimate 3 sets of models. Section 5.1 uses the three distance

rings, with maximum distance of 1500m following Billings. Section 5.2 limits the sample

to homes within 1000m of a light rail station and the effect of home prices using the

opening date of The Tide. This is to account for the suspicion that using distance rings up

to 1500m leads to biased standard errors, and to follow Chen et al. (1997) who suggest

that individuals will only be willing to walk up to 1000m to a station. Section 5.3

investigates a possible anticipation effect of The Tide; we again limit the sample to

homes within 1000m straight-line distance and now use sample period up to the opening

of The Tide which captures the 4 year construction period. Using Census Block fixed

effects, we can effectively control for demographic factors between the different homes

that affect a home’s price.15

We include month by year dummy variables to control for

seasonality in the housing market, and the effect on home prices due to the Great

Recession.

5. Results

Three distinct sets of DID regression analysis were conducted. The first set

includes all three distance rings; the second set limits the sample to only homes within

500m with the opening date of the Tide; and the third set again limits the sample to

homes within 500m and now uses the first day of construction as the date of our

exogenous effect. The coefficient on the interaction term represents the relationship

between the accessibility factor vs. the nuisance effect. If individuals value the increased

accessibility due to being within walking distance of a light rail station, we expect a

positive coefficient. However, if the nuisance effect is greater, we then expect a negative

coefficient.

15

Census blocks are the smallest geographic areas by which the Census Bureau creates demographic data.

They are composed of 15 digit code. Using census block allows us to pick the most specific differences

between homes sold in different blocks, and to account for any demographic changes within a block over

our 8 year sample.

5.1 Three Distance Rings

In this first set of analysis, we include all three measures of distance. Column 1 in

Table 4, describes a base regression which includes every home in the sample without

housing traits.16

Column 2 retains the same regression but limits the sample to only

homes within 3200m. The motivation behind this limitation is that individuals will not be

willing to walk beyond 2 miles to a light rail station, and thus homes outside of this

distance should not be affected by the opening of the light rail. Columns 3 and 4 are

respectively the same as columns 1 and 2; however, we now account for observable

housing characteristics. The dummy variables representing minimum distance to a station

are all negative, albeit statistically insignificant. The coefficients on the interaction term

of distance and light rail opening indicate a large effect on home prices due to the light

rail. Homes within 1500m observe sell for 10% more relative to homes outside this

distance, statistically significant at the 1% level. In terms of accessibility, the results

indicate that individuals greatly value being near the light rail. The relative increase in

home price due to accessibility to a light rail station is comparable to having a home near

the light rail is comparable to having a waterfront home.17

While this result might initially be promising, we believe that there is significant

downward bias in the standard errors. Referencing table 1 again, we see there are 7

stations along the light rail that have less than 1500m of straight-line distance between

them. This concentric circle layout can lead to a home being counted as part of the

treatment group for station 2 and simultaneously being counted as the control group in

station 3. This concern is confirmed by the map presented in Figure 1, and the data in

16

All regressions include the census block by month by year fixed effects and robust standard errors. 17

Homes sold within 1500m after the light rail opened sell for 10% greater relative to homes outside

1500m. Waterfront homes sold for 13.7% more relative to non-waterfront homes.

Table 2 which displays all homes sold within 1500m of 2 stations by year of sale. For

example, in 2005, there were 207 homes sold within 1500m of station 1 and station 2,

and in total there were 848 homes sold within 1500m of 2 consecutive stations in 2005.

We address this concern in sections 5.2 and 5.3 by limiting our treatment group to only

homes sold within 500m of a station and creating the control group to be homes sold

between 500m and 1000m.

5.2 Distance Restriction using Tide Opening Date

Due to the possibility that the above model may suffer from downward bias of the

standard errors, we employ a variety of restrictions on our sample. Column 1 in Table 5

uses standard hedonic price analysis in a cross section model, including only homes sold

after the opening of The Tide. The interpretation uses the implicit price of various

characteristics and having a home within 500m of a station. The effect is negative,

indicating a statistically insignificant 9% lower sale price if a home is located within

500m relative to all other homes. However, for the reasons stated above, we move to DID

estimation.

Column 2 provides the base regression, which does not account for any home

characteristics, only the census block by month by year fixed effects, distance indicator

variables, and an interaction term. This specification indicates that a home within 500m

of a light rail station will sell for 12.9% less relative to all other homes (statistically

significant at the 5% level). However, the interaction term is statistically insignificant.

Column 3 re-runs the above regression, and now accounts for home traits. There is no

significant effect found for being within 500m of a light rail station after the light rail

opened.

Next in column 4, we restrict the sample to only observed homes sold within

1000m of a light rail station. This is to limit the downward bias of the standard errors

observed in section 5.1. The overall regression includes 2,767 observations, and the

variables jointly account for 84% of the variation in home prices. Homes sold after the

light rail opened within 500m of a station sell for 1.3% less relative to homes 500m to

1000m, although this result is statistically insignificant. The final column adds an

additional restriction using only homes within 1000m of a station and having a sale price

within 1 standard deviation of the mean.18

The motivation behind this restriction is to

observe the effects of a “typical” home. In this model, a home sold within 500m of a

station after the light rail opened, sold for 4.1% relatively less than homes 500m to

1000m away. However, this result is again insignificant.

The lack of statistical significance of the interaction term across all models

indicates that the light rail has no effect on home prices. This is consistent with the work

done by Hess and Almeida (2007) who note that light rail systems with limited service

are unlikely to create large home capitalization effects. Individuals do not value the

accessibility of being near the light rail (or do not value locations The Tide services), but

they also do not de-value a home for being near the light rail due to a nuisance effect.

5.3 Distance Restriction and Construction Date

Often times with the introduction of public transportation, there is an anticipation affect.

It is logical to believe that individuals observe the construction of a station around them,

and begin to value homes near a station more prior to the actual opening of light rail.

18

The mean home price for homes within 1000m is $265,425 with a standard deviation of $169,521. This

restricts the sample to homes with a sale price between $95,904 and $434,946. This model has 2,185

observations.

Construction of The Tide began on December 8, 2007. 19

To observe the effect on home

prices of the start of construction on The Tide, the sample was restricted to homes sold

before August 18, 2011 (opening date of The Tide), and we use an indicator variable for

if a home was sold between construction start and the opening of The Tide. This set of

models located in Table 6 follow the same progression as in section 5.2, and include

census block by month by year individual specific effects to control for unobservable

factors in the home and the housing market as a whole.

Our initial cross section model contains all homes sold between construction start

and opening day, which limits the sample to 7,308 homes. In this traditional hedonic

price analysis, homes within 500m of a prospective light rail station are sold for 3.4%

more relative to homes outside of that distance, although the finding is statistically

insignificant. The initial DID regression in column 2 of Table 6 indicates that being

within 500m of a light rail station results in a home selling for 14.4% (significant at 5%)

less relative to all other homes sold up to the opening of The Tide. Despite this large

observed nuisance effect, the interaction term controlling for whether the home was sold

during construction is statistically insignificant.

When home traits are added in column 3, we observe a positive and significant

interaction term.20

This indicates that homes within 500m of a prospective light rail

station sold during construction had a sale price 6.1% greater relative to homes outside

500m and/or before construction began, statistically significant at the 1% level. This

finding contradicts previous findings in the literature that indicate a negative price effect

19

Hampton Roads Transit has a timeline of The Tide, which is where this information was obtained

(Norfolk LRT Project: Chronology of FTA Project Activities, 2011). 20

The regression in column 3 does not limit the sample size to homes within 1000m, and compares homes

within 500m of a station to all other homes sold in Norfolk.

for homes near a light rail station during the construction phase (Chatman et al. 2012 &

Yan et al. 2012). This observed anticipation effect represents the only statistically

significant effect on the interaction term across all models. The fact that we fail to find a

statistically significant impact on home prices after The Tide had opened can lead to a

number of prospective implications. Perhaps individuals were underwhelmed with the

size and accessibility of the system. During construction, home buyers were optimistic

about the benefits of public transportation. However, once The Tide became operational,

individuals did not actually observe any benefit and were not willing to pay a premium to

be near a station.

Columns 4 and 5 limit the model to only homes within 1000m and then adding

the sale price restriction respectively. In both models, the interaction term is statistically

insignificant; however, differing signs are observed. When homes within 500m are

compared to homes sold 500m to 1000m from a station, they sell for 3.5% more. When

we restrict our sample on the basis of sale price, homes sold during the construction

period and within 500m experience a statistically insignificant effect of selling for 2.6%

less relative to homes outside 500m and/or before construction began. Column 4

indicates that jointly, these variables account for 86% of the variation in home price for

homes sold up to the opening of The Tide.

5.4 Home Composition Tests

The underlying assumption of DID estimation, is that the treatment and control

group follow the same trend absent an exogenous factor. In our sample, the exogenous

factor is a home’s location to a light rail station. However it is important that regardless

of a home’s distance to a light rail station, homes have basic homogeneity with respect to

their composition. The motivation behind this robustness check is that if homes in

Norfolk do not exhibit statistically different home attributes, a variation in sales price can

be explained by an exogenous effect (The Tide).

A series of home composition ordinary least squares regressions are carried out

to test the significance of home characteristics (Pope and Pope, 2012). The dependent

variables are square footage, full baths, age, and number of bedrooms. The independent

variables are distance indicator variables for within 500m and 500m to 1000m of a light

rail station, and their respective interaction terms that represent the opening of the light

rail and the start of construction. We continue to account for census block by month by

year fixed effects.

In Tables 7 and 8, we observe that the only statistically significant difference

between periods is for age. The homes sold in Norfolk during our sample period are on

average 57 years old, which indicates a majority of homes sold are not newly constructed.

This means that as we progress in our sample period, a majority of homes will get older

which explains the statistical difference in age between the period prior to the light rail

opening and the period after the opening. The lack of statistical significance for our

distance and interaction indicator terms (with the exception of age) indicate that there is

not statistical evidence of a difference in home composition before and after the opening

of The Tide and before and during construction of The Tide. The results in the home

composition tests provide support for the reliability of our DID estimations above.

6. Conclusion

The literature within analyzing the effect of a light rail system on home prices

fails to offer a uniform consensus. Our paper uses the quasi-experimental methodology of

DID to better control for the variety of unobservable factors that affect the sale price of a

home, while avoiding the issues of omitted variables bias and multicollinearity.

Our results indicate that the opening of The Tide has not had a significant effect

on home prices within 500m. This result is unsurprising for a variety of reasons. Our

“post light rail opening” period only contains 2 years of observations, and it is possible

that the accessibility effect of The Tide has yet to be capitalized into home prices. It is

also reasonable that individuals do not view the light rail as being a more efficient (both

in terms of time and cost) mode of transportation relative to driving, biking, taking the

bus, etc. This analysis also includes straight-line distance which is often not equivalent to

walking distance, especially when the area contains as many creeks and canals as

Norfolk. The Tide does not run to Old Dominion University, a moderately sized

university of about 25,000 students. Freshman at Old Dominion are not permitted to have

cars, and perhaps if The Tide extended to campus, ridership would increase and

individuals would value having a home near a light rail station.

While the opening of The Tide has not had a significant impact on home prices,

given more time (and the possibility of expansion into neighboring Virginia Beach) the

results might prove significant.

References

Alonso, W. (1964). Location and Land Use. Cambridge, MA: Harvard University Press.

Billings, S. B. (2011). Estimation the value of a new transit option, Regional Science

and Urban Economics, 41, pp. 525-536.

Billings, S. B., Leland, S., & Swindell, D. (2011). The effects of the announcement and

opening of light rail transit stations on neighborhood crime, The Journal of

the Urban Affairs Association, 33(5), pp. 549-565.

Card, D., & Krueger, A.B. (1994). Minimum wages and employment: a case study of

the fast-food industry in New Jersey and Pennsylvania, The American

Economic Review, 84(4), pp.772-793.

Chatman, D.G., Tulach, N.K., & Kyeongsu, K. (2012). Evaluating the economic impacts

of light rail by measuring home appreciation: a first look at New Jersey’s river

line, Urban Studies, 49(3), pp. 467-487.

Chay, K. Y., & Greenstone, M. (2005). Does air quality matter? Evidence from the

housing market, Journal of Political Economy, 113(2), pp. 376-424.

Cervero, R. (2004). Effects of light and commuter rail transit on land prices:

experiences in San Diego County. Journal of the Transportation Research Forum,

43(1) pp. 121-138.

Chen, H., Rufolo, A., & Dueker, K. J. (1997). Measuring the Impact of Light Rail

Systems on Single Family Home Values: A Hedonic Approach with GIS

Application. Paper presented at the 77th

Annual Meeting of the Transportation

Research Board, Washington, DC.

Chi, G., & Voss, P. (2005). Migration decision-making: hierarchical regression

approach, The Journal of Regional Analysis and Policy, 35(2), pp. 11-22.

Dube, J., Legros, D. Theriault, M. & Des Rosiers, F. (2014). A spatial difference-in-

differences estimator to evaluate the effect of change in public mass transit

systems on house prices, Transportation Research, Part B(64), pp. 24-40.

Fan, Y., Guthrie, A., & Levinson, D. (2012). Impact of light-rail implementation on

labor market accessibility: A transportation equality perspective, The Journal

of Transport and Land Use, 5(3), pp. 28-39.

Garrett, T. A. (2004). Light rail transit in America: Policy issues and prospects for

economic development. Federal Reserve Bank of St. Louis.

Hess, D.B., & Almeida, T.A. (2007). Impact of Proximity to Light Rail Rapid Transit on

Station-area Property Values in Buffalo, New York, Urban Studies, 44(5), pp.

1041-1068.

Hurst, N.B., & West, S.E. (2014). Public transit and urban redevelopment: the effect

of light rail transit on land use in Minneapolis, Minnesota, Regional Science

and Urban Economics, 46, pp. 57-72.

LRT Initial 6-Month Study. (2012). HRT Public Records, Hampton Roads Transit.

Norfolk LRT Project: Chronology of FTA Project Activities. (2011). HRT Public

Records, Hampton Roads Transit.

Pan, Q (2013). The Impacts of an Urban Light Rail System on Residential Property

Values: A Case Study of the Houston METRORail Transit Line, Transportation

Planning and Technology, 36(2), pp. 145-169.

Pope, D.G., & Pope, J.C. (2012). When Walmart comes to town: always low housing

prices? Always?, NBER Working Paper No. 18111.

Ryan, S. (2005). The Value of Access to Highways and Light Rail Transit: Evidence

for Industrial and Office Firms, Urban Studies, 42(4), pp. 751-764.

Shiller, R. J. (2007). Understanding recent trends in house prices and home

ownership (Working Paper No. 13553). Retrieved from National Burearu of

Economic Research website: http://www.nber.org/papers/w13553

The State of the Region. (2013). Regional Studies Institute, Old Dominion University,

Norfolk, VA.

Yan, S., Delmelle, E., & Duncan, M. (2012). The impact of a new light rail system on

single-family property values in Charlotte, North Carolina, The Journal of

Transport and Land Use, 5(2), pp. 60-67.

Figure 1

This figure provides the light rail track and its stations. Each station is surrounded by a

1500m buffer, where the radius of the circle is 1500m. The track starts at the eastern most

station, (station 1) and runs to the final station which borders with Virginia Beach.21

21 Map was made in ARCGIS.

Table 1

Summary Statistics of Homes

Within 500

meters

500 to 1000

meters

1000 to 1500

meters All Homes

Mean Mean Mean Mean

(Std. Deviation) (Std. Deviation) (Std. Deviation)

(Std.

Deviation)

Sale Price 293,863 245,230 220,174 210,675

(193274) (147153) (120507) (145751)

Square Footage 1592 1815 1660 1631

(816) (914) (692) (706)

# Full Baths 1.710 1.852 1.801 1.696

(0.672) (0.716) (0.670) (0.684)

# of Bedrooms 2.412 3.089 2.972 3.121

(1.098) (1.092) (0.958) (0.889)

Age 44.31 54.62 53.61 56.91

(38.49) (32.82) (33.88) (29.61)

Sample Size 1149 1618 1350 21255

% of Sample 5.41% 7.61% 6.35% 100.00%

Table 2

Home Frequency by Station

Station Name

# of

Homes

Park and

Ride

Bus

Service Downtown

Distance

Between

(Y/N) (Y/N) (Y/N) (Meters)

EVMS 7590 Y Y N -

York Street 737 N N Y 1300

Monticello 958 N N Y 250

MacArthur Square 123 N N Y 440

Civic Plaza 138 N Y Y 390

Harbor Park 428 Y N N 730

Norfolk State 993 N Y N 800

Ballentine 5899 Y Y N 1470

Ingleside 1655 N N N 1680

Military Hwy 1603 N Y N 1950

Newtown Road 1131 Y Y N 2040

Note: Distance between measures straight-line distance between 2 consecutive stations. For

example, 1300m represents the straight-line distance between EVMS and York St stations.

H

om

es S

old

Wit

hin

1500m

of

2 S

tati

ons

By Y

ear

and

Sta

tio

n

2

00

5

2006

2007

2008

2009

2010

20

11

20

12

20

13

Tota

ls

Sta

tion 1

and S

tati

on 2

173

264

268

183

114

92

105

122

119

1440

Sta

tion 2

an

d S

tati

on

3

20

7

312

242

169

116

95

99

1

21

11

5

14

76

Sta

tion 3

an

d S

tati

on

4

16

9

282

122

133

82

71

63

8

9

81

10

92

Sta

tion 4

an

d S

tati

on

5

11

4

146

84

106

62

47

47

6

7

56

72

9

Sta

tion 5

an

d S

tati

on

6

66

92

22

27

23

31

20

2

6

21

32

8

Sta

tion 6

an

d S

tati

on

7

49

88

22

34

33

35

34

4

3

38

37

6

Sta

tion 7

an

d S

tati

on

8

34

50

40

29

42

35

49

5

3

53

38

5

Sta

tion 8

an

d S

tati

on

9

16

14

28

13

19

21

16

2

2

22

17

1

Sta

tion 9

an

d S

tati

on

10

6

8

4

5

16

12

11

1

2

15

89

Sta

tion 1

0 a

nd S

tati

on

11

14

30

10

18

18

24

19

2

1

27

18

1

Tota

ls

84

8

1286

842

717

525

463

46

3

57

6

54

7

Tab

le 3

Table 4

Difference in Difference with 3 Distance Rings22

(1) (2) (3) (4)

Ln(Price) Ln(Price) Ln(Price) Ln(Price)

Within 500m -0.085 -0.097 -0.09 -0.106

(0.77) (0.88) (1.03) (1.22)

500m to 1000m 0.043 0.025 -0.064 -0.093

(0.44) (0.26) (0.85) (1.26)

1000m to 1500m -0.01 -0.026 -0.061 -0.084

(0.11) (0.29) (0.89) (1.28)

Within 500m*post 0.012 0.081** 0.039 0.105***

(0.35) (2.05) (1.48) (3.50)

500m to 1000m*post 0.008 0.077** 0.044** 0.109***

(0.30) (2.26) (2.05) (4.19)

1000m to 1500m*post 0.028 0.094*** 0.044** 0.109***

(0.94) (2.71) (2.00) (4.17)

Age

-0.005*** -0.005***

(31.83) (22.66)

Bedrooms

0.031*** 0.023**

(3.38) (2.37)

Full Baths

0.163*** 0.229***

(26.01) (19.74)

Half Baths

0.122*** 0.170***

(18.23) (13.59)

Waterfront

0.177*** 0.137***

(14.26) (5.35)

Square Feet

0.000*** 0.000***

(20.03) (12.67)

Constant 11.828*** 11.76*** 11.424*** 11.329***

(258.03) (172.30) (119.71) (103.64)

R2 0.66 0.66 0.81 0.80

N 21255 8032 21255 8032

Census block by month

by year fixed effects X X X X

Robust Standard Errors X X X X

Notes: *** represents significance at the 1% level, ** represents significance at

the 5% level. Standard errors are displayed in parenthesis. Price represents sale

price. The post-date used is the opening of The Tide, August 18, 2011.

22 Column 1 represents the base regression with all homes. Column 2 represents all homes sold within 3200m of a light rail station. Column 3 represents all homes sold, controlling for home traits. Column 4 represents all homes sold within 3200m of a light rail station controlling for home traits.

Table 5

Difference in Difference w/ Tide Opening Date23

1 2 3 4 5

Ln(Price) Ln(Price) Ln(Price) Ln(Price) Ln(Price)

Within 500m -0.092 -0.129** -0.039 -0.036 0.025

(0.76) (2.20) (0.81) (0.73) (0.89)

Within 500m*post

0.010 0.033 -0.013 -0.041

(0.29) (1.28) (0.42) (1.55)

Age -0.006***

-0.005*** -0.004*** -0.002***

(16.09)

(31.80) (10.84) (7.81)

Bedrooms 0.029

0.031*** 0.026** 0.029***

(1.22)

(3.37) (2.05) (2.43)

Full Baths 0.246***

0.163*** 0.172*** 0.136***

-13.42

(25.98) (10.87) (11.05)

Half Baths 0.170***

0.122*** 0.134*** 0.062***

-9.47

(18.21) (7.79) (4.43)

Waterfront 0.172***

0.177*** 0.167*** 0.117***

(5.20)

(14.27) (5.01) (3.97)

Square Feet 0.000***

0.000*** 0.000*** 0.000***

(4.93)

(20.02) (10.99) (7.29)

Constant 11.057*** 11.830*** 11.405*** 11.703*** 11.748***

(143.55) (276.63) (119.56) (202.73) (188.27)

R2 0.86 0.66 0.81 0.84 0.77

N 5523 21255 21255 2767 2185

Census block by

month by year fixed

effects X X X X X

Robust Standard

Errors X X X X X

Notes: *** represents significance at the 1% level, ** represents significance at the 5% level.

Standard errors are displayed in parenthesis. Price represents sale price. The post-date used is

the opening of The Tide, August 18, 2011.

23 Column 1 represents the cross section analysis, analyzing all homes sold between the opening of The Tide and the end of the sample period. Column 2 is the base DID estimation, and includes all homes sold in Norfolk from 2005-2013. Column 3 is the exact same estimation as column 2 and now includes home traits. Column 4 restricts the sample to homes only homes sold within 1000m of a light rail station. Column 5 adds the additional restriction of including only homes with a sale price that is within 1 standard deviation of the mean sale price; $95,904 to $434,946.

Table 6

Difference in Difference w/ Tide Construction Date24

1 2 3 4 5

Ln(Price) Ln(Price) Ln(Price) Ln(Price) Ln(Price)

Within 500m 0.034 -0.144* -0.047 -0.044 0.021

(0.28) (2.18) (0.87) (0.81) (0.63)

Within 500m*post

0.046 0.061*** 0.035 -0.026

(1.69) (2.93) (1.29) (1.41)

Age -0.006***

-0.004*** -0.004*** -0.003***

(26.45)

(22.39) (8.98) (7.77)

Bedrooms 0.027

0.030*** 0.010 0.021

(1.87)

(4.91) (0.66) (1.69)

Full Baths 0.199***

0.138*** 0.160*** 0.130***

(21.22)

(20.00) (8.89) (9.15)

Half Baths 0.152***

0.106*** 0.115*** 0.045***

(15.10)

(14.35) (5.93) (2.87)

Waterfront 0.175***

0.180*** 0.122*** 0.109***

(9.55)

(13.05) (3.21) (3.21)

Square Feet 0.000***

0.000*** 0.000*** 0.000***

(11.92)

(24.30) (9.64) (6.74)

Constant 11.828*** 11.854*** 11.450*** 11.728*** 11.773***

(181.25) (218.55) (184.32) (170.04) (150.40)

R2 0.82 0.70 0.82 0.86 0.81

N 7308 15720 15720 2092 1700

Census by month

by year fixed

effects X X X X X

Robust Standard

Errors X X X X X

Notes: *** represents significance at the 1% level, ** represents significance at the

5% level. Standard errors are displayed in parenthesis. Price represents sale price.

The post-date used is the start of construction, December 8, 2007. The sample is

restricted to all homes sold in Norfolk up until the opening of The Tide.

24 Column 1 represents the cross section analysis, analyzing all homes sold between the start of construction and the opening of The Tide. Column 2 is the base DID estimation, and includes all homes sold in Norfolk until the opening of The Tide. Column 3 is the exact same estimation as column 2 and now includes home traits. Column 4 restricts the sample to homes only homes sold within 1000m of a light rail station. Column 5 adds the additional restriction of including only homes with a sale price that is within 1 standard deviation of the mean sale price; $95,904 to $434,946.

Table 7

Home Composition Before and After LR Opening

Variable Sq. Ft.

Full

Baths Age Bedrooms

Constant 1400*** 1.542*** 58.51*** 2.897***

(236) (0.132) (1.39) (0.165)

Within 500m -35.24 -0.131 -2.12 -0.147

(84.69) (0.098) (3.69) (0.123)

500m to 1000m 77.16 0.056 -7.87*** 0.044

(62.58) (0.071) (2.72) (0.092)

Within 500m*post 17.03 -0.020 5.94*** 0.035

(41.49) (0.046) (1.48) (0.059)

500m to

1000m*post -11.95 -0.029 3.97*** -0.073

(39.38) (0.038) (1.39) (0.054)

N 21255 21255 21255 21255

R2 0.5721 0.4303 0.5952 0.4343

Table 8

Home Composition Before and After LR Construction

Variable Sq. Ft

Full

Baths Age Bedrooms

Constant 1400*** 1.483*** 58.26*** 2.862***

(259) (0.155) (1.34) (0.198)

Within 500m -85.60 -0.1230 -3.80 0.010

(95.17) 0.120 (4.63) (0.154)

500m to 1000m -14.53 (0.042) -8.94** 0.087

(73.99) 0.084 (3.55) (0.112)

Within 500m*post 56.17 (0.019) 1.50 -0.056

(45.11) (0.042) (1.32) (0.056)

500m to

1000m*post 40.87 0.009 0.07 0.028

(36.41) (0.040) (1.40) (0.060)

N 15720 15720 15720 15720

R2 0.6125 0.4695 0.6448 0.4828

Note: In Tables 7 and 8, *** represents statistical significance at the 1% level, and **

represents significance at the 5% level.