- Email: [email protected]
Externalities of auto traffic congestion growth: Evidence from the residential property values in the US Great Lakes megaregion
Journal of Transport Geography 70 (2018) 131–140
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Journal of Transport Geography journal homepage: www.elsevier.com/locate/jtrangeo
Externalities of auto traffic congestion growth: Evidence from the residential property values in the US Great Lakes megaregion
T
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Jangik Jina, , Peter Raffertyb a b
Kyung Hee University, Graduate School of Tourism, Department of Real Estate, 1, Hoegi-dong, Dongdaemun-gu, Seoul, South Korea Gannett Fleming, Inc., 8025 Excelsior Drive, Madison, WI 53717, United States
A R T I C LE I N FO
A B S T R A C T
Keywords: Traffic Congestion Housing Prices Great Lakes Megaregion Panel Data Analysis
Traffic congestion is getting worse and commuting cost is getting higher in every major metropolitan area. In this study, we attempt to explore the negative externalities of traffic congestion focusing on housing prices. We use a unique transportation dataset, the National Performance Management Research Data Set (NPMRDS), which contains information on every 5 min travel time across the extensive region to measure traffic congestion at the local level. In order to estimate the effect of traffic congestion on residential property values, we use a dynamic hedonic price model with a monthly panel dataset. Our findings show that while there is no significant relationship between congestion growth and housing price growth in rural and non-metro areas, the effects of congestion growth on housing price growth in urban and metro areas are obvious. We verify that traffic congestion functions as a disamenity in a local neighborhood. Transportation policies that aim to relieve traffic congestion should consider that severe traffic congestion may have an adverse effect on local property values.
1. Introduction Transportation accessibility is one of the primary determinants of residential property values and changes in those values, since the effects of the transportation accessibility are generally reflected in residential property values. People can easily access jobs, shopping, restaurants, museums, or other important points of reference when transportation infrastructure improves in their communities. Hence, increases in accessibility provide a better quality of life to local residents, which results in positive effects on the property values in their communities. In contrast, accessibility will decrease when transportation demand increases without additional development of transportation facilities. Decreased accessibility may cause traffic congestion and gridlock that negatively affects the quality of life of residents. In this case, property values will decrease. Numerous studies have demonstrated there is a significant relationship between transportation accessibility and residential property values (Ossokina and Verweij, 2015; Osland and Thorsen, 2008; Matthews and Turnbull, 2007; Cervero and Duncan, 2004). Also, various transportation accessibility measures such as job accessibility, transit accessibility, and travel time accessibility have been developed and used in many previous studies. Nevertheless, there is little agreement regarding which measure appropriately evaluates the transportation accessibility in a local community. Studies demonstrated that
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Corresponding author. E-mail addresses: [email protected] (J. Jin), praff[email protected] (P. Rafferty).
https://doi.org/10.1016/j.jtrangeo.2018.05.022 Received 14 April 2017; Received in revised form 28 May 2018; Accepted 28 May 2018 0966-6923/ © 2018 Published by Elsevier Ltd.
two elements, such as transportation and activity, are considered in measuring accessibility (Handy and Niemeier, 1997). The former, transportation element, is related to the transportation costs measured by travel distance, travel time, or traffic congestion, whereas the latter, activity element, represents the degree of the locational attractiveness measured by closeness to the opportunities for jobs, shopping, and recreational activities. A number of studies have investigated the effects of activity-based accessibility on housing values (Osland and Thorsen, 2008; Matthews and Turnbull, 2007; Cervero and Duncan, 2004) and the effects of transportation accessibility measured by travel time and distance on housing values (Iacono and Levinson, 2017; Yan et al., 2012). However, only a few studies have attempted to measure accessibility using traffic congestion (Sweet et al., 2015), and examined the relationship between traffic congestion and housing values (Ossokina and Verweij, 2015). Hence, our understanding of the relationship between traffic congestion and property values is insufficient and limited. One reason for the lack of the research on the relationship between traffic congestion and housing values is the deficiency of micro-level traffic data covering extensive regions to measure the traffic congestion at the neighborhood level. The widely used traffic congestion data provided by Texas A&M Transportation Institute (TTI) is at the metropolitan level. The data do not provide any information regarding traffic congestion at the neighborhood level.
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demonstrated that transportation accessibility is one of the important factors in explaining housing prices (Iacono and Levinson, 2011; Osland and Thorsen, 2008; Cheshire and Sheppard, 1995).
Recently, along with the development of information technology and transportation engineering, transport big data, such as probe data offer the potential to develop transformative applications that can improve transportation operations, planning, and maintenance as well as detect travel conditions and traffic congestion (Rafferty et al., 2017). Particularly, the National Performance Management Research Data Set (NPMRDS), since 2013, enables researchers to examine the conditions of transportation performance management at the micro level as well as across the extensive regions. This dataset offers an unprecedented opportunity to evaluate traffic congestion by providing travel times for the entire National Highway System by both passenger and freight traffic in 5-min increments. The data also provide spatial references with GIS features, which enables spatial analysis. Using the transportation big data (NPMRDS) with the research perspective, this study is to explore the effect of traffic congestion on residential property values. Particularly, by focusing on the Great Lakes Megaregion, we investigate whether or not the traffic congestion is negatively capitalized into the median housing prices at the neighborhood level. We obtain monthly housing price panel data (2013–2016) at the zip code level from Zillow (real estate company), and calculate monthly PTI (Planning Time Index) as a traffic congestion index using NPMRDS data obtained from the Federal Highway Administration (FHWA). In order to estimate the effect of traffic congestion on the residential property values, we use a dynamic hedonic price model with a monthly panel dataset. Our research is expected to contribute to the growing literature on the effect of transportation policies that aim to mitigate severe traffic congestion. In addition, we suggest the applicability of transportation big data into research in the field of transportation engineering, planning, and geography.
2.2. Traffic congestion and housing values Increasing traffic congestion is not only a primary source of greenhouse gases, local air pollution, and noise annoyance, but also one of the negative factors affecting local economies with respect to the employment and productivity (Sweet, 2014). Generally, congestion is indicative of high economic activities. However, firms are likely to relocate from the areas undergoing severe traffic congestion to the areas with unused road capacity when diseconomies caused by traffic congestion are much larger than the benefits of agglomeration economies (Rosenthal and Strange, 2004). In this sense, traffic congestion can drag down the local economy. Previous studies demonstrated that traffic congestion has a negative effect on the local economy, particularly it leads to a decrease in employment growth (Jin and Rafferty, 2017; Sweet, 2014; Hymel, 2009). This perspective, especially among engineers and economists, is based on the notions that traffic congestion is considered as a diseconomy. Unlike this perspective, urban designers regard the traffic congestion as a function of local disamenity. According to the urban economic theory, individual households choose their location to maximize their utility, and features of neighborhoods such as average commute time, proximity to rail lines, and ethnic characteristics have substantial effects on the household residential location decision. Contrary to the local amenities that positively affect household location decision, local environmental disamenities such as noise, crime, pollution, and local traffic congestion have negative effects on household residential location decision (Roback, 1982; Rosen, 1974), and thus they have detrimental effects on residential property values (Ossokina and Verweij, 2015). A number of previous studies have empirically demonstrated that such disamenities have negative effects on the housing values (Brasington and Hite, 2005; Kohlhase, 1991; Nelson, 1982). They showed that wide range of tangible attributes such as structural, physical, and environmental factors have substantial effects on the housing prices. For example, an increase in traffic noise pollution resulted from the new transportation infrastructure development has a negative effect on housing prices (Kim et al., 2007; Nelson, 1982). In contrast, relatively little work has been conducted to quantify the impact of intangibles, especially traffic congestion. Only a few recent studies attempted to directly measure the traffic congestion at the local level (Sweet et al., 2015), and identified its effects on the housing prices (Ossokina and Verweij, 2015). As numerous studies show that traffic congestion are getting worse in the metropolitan areas (Schrank et al., 2012), additional investigation on the negative externalities of traffic congestion is worthwhile.
2. Literature review 2.1. Accessibility and housing values In the perspective of urban economic theory, the study of the relationship between transportation and housing price has two streams. First, it starts with Alonso (1964)’s urban monocentric model that explains residential activities are determined by a trade-off between the accessibility of being close to the central business district (CBD) and property values. This monocentric model gained widespread popularity because it provides a framework for empirically examining the relationship between location and property values. This theory has been further developed by Muth (1969) and Mills (1972), and has offered elaborated theoretical frameworks on urban spatial structure by explaining trade-off between housing price and transportation cost. However, the original model assumes that transportation is ubiquitous and available via a single mode, which has been criticized by others researchers who argued that multiple nodes are better to explain the urban spatial structure (Anas and Moses, 1979). Recently, rapid urbanization and suburbanization have changed urban spatial structures, and thus numerous studies have shown that a polycentric model is more appropriate to explain urban spatial structures (Giuliano and Small, 1991; McMillen, 2001). A second approach to explore the relationship between transportation and housing price is a hedonic price model developed by Rosen (1974). The basic idea of the model is that people consider many factors of their residential environments that influence their property values when they choose their residential locations. Thus, this model explains the composition of housing price by disentangling the bundle of housing services. According to the urban economic theory, if highway or transportation networks improve accessibility, its premium will be reflected in higher housing prices (Boarnet and Chalermpong, 2001). Moreover, numerous studies have tested the effects of accessibility to the light rail station and public transportation, average commuting time, road capacity, and highway network system on the property values. Through this hedonic price model, the empirical studies have
3. Methodology 3.1. Analytical framework In this section, we discuss our analytical framework to examine the relationship between traffic congestion and housing prices. Housing prices are primarily determined by supply of and demand for housing. Hence, we employ a reduced form equilibrium model to explain the effect of traffic congestion on changes in housing prices across zip code regions. In equilibrium, supply and demand for housing services during the period determine housing prices. This relationship can be described as follows;
HPt = f (QtD , QtS ) where HPt is the housing prices during period t, 132
(1) QtD
is a demand for new
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Fig. 1. US Megaregions and US Great Lakes Megaregion (source:America, 2050http://www.america2050.org)
construction obtained from the United States (US) Census. Ut is an unemployment rate. Vt and Zt are vacancy rates of housing and firms, respectively. Substitution of Eqs. (2) and (3) into the Eq. (1) yields the function of housing price, and the equilibrium reduced-form housing price equation is established as follows;
HPt = f (Tt , Dt , Mt , Lt , St , It , Ct , Ut , Vt , Zt )
(4)
Through the Eq. (4), we establish our empirical model for analyzing the effects of traffic congestion on housing prices. Particularly, we focus on the growth in housing prices and growth in traffic congestion with location-specific fixed-effects based on the panel dataset, which is the following function: j
∆HPit = α + β1 ∆Tit + β2 Ti, t − 1 + β3 HPi, t − 1 + β4 ∆HPi, t − 1 + βk
∑ Xk,t−1 k=5
+ δi + θt + ϵit
where ΔHPit is growth in housing prices in zip code i, during the time, t, ΔTit is growth in traffic congestion in zip code i, during the time, t, Xk, t−1 is the control variables such as interest rates, mortgage rates, construction cost index, unemployment rates, housing vacancy rates, the number of households, and firm vacancy rates during the lagged time, t1, δi is the location-specific fixed-effects, θt is the time-specific fixedeffects, and ϵit is the error term. This dynamic panel data analysis with fixed-effects has advantages over cross-sectional data analysis. A benefit of using the panel data is to eliminate unobserved variables that are specific to each sample and time-invariant over time. The panel data also allow researchers to study dynamics of housing prices which change over time. In addition, the panel data contain more information, more variability and thus less collinearity among the variables, which produce more efficient and precise estimations (Anna et al., 2014; Baltagi, 2008). Therefore, the dynamic panel model is appropriate to investigate the effects of traffic congestion measured by longitudinal transportation big data, such as the NPMRDS, on housing prices.
Fig. 2. Time Trend of Planning Time Index
housing during period t, and QtS is a supply of new housing during period t. Housing price is affected not only by macro factors such as economic growth rates and changes in interest rates, but also micro factors such as neighborhood environments, household characteristics, and housing characteristics. Particularly, these components are related to supply and demand for housing services. Thus, we need to consider both factors to estimate of housing prices. The demand for housing services can be derived based on the assumption of household utility maximization. The basic equation of housing demand is as follows;
QtD = g (Tt , Dt , Mt , Lt , St )
(2)
where Tt is a traffic congestion index, Dt is the number of household during the period t, and Mt is a mortgage rate during the period t, and Lt is locational characteristics, measured by percentage of park and lake, distance to the CBD, and urban or rural area. St is a seasonal dummy variable designed to control for seasonal fluctuations in housing prices. On the other hand, it is assumed that builders are interested in their profit maximization, so they consider the current and expected future risk-adjusted returns associated with home building (Reichert, 1990). Hence, the function of housing supply includes interest rates, construction cost, and financing or carrying costs.
QtS = h (It , Mt , Ct , Ut , Vt , Zt )
(5)
3.2. Data and variables We focus on the US Great Lakes Megaregion, which includes large metropolitan areas such as Chicago, Detroit, Minnesota, Cleveland, St. Louis, and Pittsburgh as well as ten states such as Wisconsin, Minnesota, Michigan, Iowa, Illinois, Ohio, Kansas, Indiana, Kentucky, and Missouri (See Fig. 1). The dataset consists of a balanced panel based on the monthly data obtained from various sources, which are described in this section. We focus on the time period between 2013 July
(3)
where It is an interest rate during period t, Mt is a mortgage rate during period t, Ct is a construction cost index for single-family houses under 133
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Fig. 3. Spatial Distribution of Planning Time Index, Chicago and Detroit (2016 May)
Fig. 4. Spatial and temporal distribution of traffic congestion (PTI), Great Lakes Megaregion
index cannot provide any information on the traffic congestion at the neighborhood level. The NPMRDS (National Performance Management Research Data Set) enables researchers to examine the conditions of transportation performance management at the micro level as well as across the extensive regions. Particularly, this dataset offers an unprecedented opportunity to evaluate traffic congestion by providing travel times for the entire National Highway System by both passenger and freight traffic in 5-min. increments. It comprises around 280 thousand directional segments, defined by traffic message channel (TMC), typically around two miles long. The data also include nearly two million GIS features, enabling mapping and spatial analysis, which allows us to directly measure traffic congestion at the neighborhood level (Rafferty et al., 2017). Generally, traffic congestion can be measured in a variety of ways, but
and 2016 May for data consistency.
3.2.1. Traffic congestion measure Traditionally, two measures have been widely used for measuring traffic congestion. The first one is to use the average journey to work travel time, which is measured and provided by the US Census Bureau. Average travel time is a simple way to measure overall levels of congestion, but it cannot provide specific commuter’s experience. For example, it only captures the average travel time during the certain time period, but it cannot consider traffic delay during the peak-period hours (rush hours) that are highly concerned by every commuter. The second one is to use travel delay index that can be obtained from TTI. This index provides a direct measure of the delayed hours due to traffic congestion. However it is measured at the metropolitan level, so this 134
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shown in Fig. 2, the average measured PTIs have increased in the US Great Lakes Megaregion between 2013 July and 2016 May. Fig. 3 describes the spatial distribution of the aggregated Planning Time Index at the zip code level in Chicago and Detroit (these two samples are the biggest cities in the US Great Lakes Megaregion), which shows that traffic congestion appears to be centered near downtown. Fig. 4 describes the spatial and temporal distribution of traffic congestion growth in the Great Lakes Megaregion. It shows that traffic congestion growth decreases away from the CBD. In addition, the second figure in Fig. 4 shows that the range of the monthly PTI growth rate is between -4 and 6, but its average growth rate is almost 1.5 and constant during the period. This indicates that although the congestion growth rate is not stable, overall traffic congestion has increased in the US Great Lakes megaregion during the time periods. 3.2.2. Housing price data The extensive dataset obtained from various sources permits estimation and construction of several indices at the zip code level. First, we obtained monthly housing price data (especially, single-family housing price data) at the zip code level from Zillow (Real Estate Inc.). Zillow provides estimates of housing price for individual houses as well as median housing prices at the zip-code level. Using the data provide two advantages; first, the data contain monthly median housing prices at the local level (zip-code); second, our intention is to examine the effects of traffic congestion at the neighborhood level measured by NPMRDS on housing prices. So, recent housing prices data at the neighborhood level are necessary to combine our transportation big data. Zillow data are the best option for the analysis. In order to match the datasets of NPMRDS and housing price, we use monthly housing price data between 2013 July and 2016 May (35 months). Since the original data obtained from Zillow are not seasonally-adjusted, we adjusted the housing prices using CPI inflation index based on the 2016 May. Fig. 5 shows the time trend of the inflation-adjusted median housing prices in the study area. Fig. 6 presents a spatial distribution of the median housing prices in
Fig. 5. Time Trend of Housing Price
in this study, we use Planning Time Index (PTI) as a traffic congestion.
PTIit =
95%Travel Timeit Free Flow Travel Timeit
(6)
The PTI is an indicator of the variability of the average travel time, and it is defined as a ratio of the 95th percent peak period travel time (6~9am/3~6pm) to the free flow travel time (9am~3pm/7pm~10pm) in every weekday. If the travel time is very volatile along a particular road segment (i), it relates to the percent of time that a segment experiences congestion. Travelers may have to plan additional time into their trip for on-time arrival at their destination. For example, a value of 2 means that for a 20 min trip in light traffic, 40 min. should be planned. In order to measure the monthly traffic congestion at the neighborhood level through the PTI, the measured PTIs at each road segment (i) are aggregated by mean value at the zip code level. As
Fig. 6. Median Housing Price, Chicago and Detroit (2016 May) 135
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Fig. 7. Spatial and temporal distribution of Median Housing Price, 2013 July~2016 May
the construction cost of new houses that are under construction as defined for the single-family houses. These data are obtained at the national level. Monthly unemployment rates are obtained from the Bureau of Labor Statistics (BLS), which are at the county levels. American Community Survey (ACS) data are commonly used to do research at the micro level, such as zip code, census tract, and census block. However, these data are only available for specific time-periods, so they do not provide monthly demographic or socioeconomic characteristics at the zip code level. Instead, we use the United States Postal Service (USPS) vacancy data that include information on the number of residential and business units and vacancy rates of them at the zip code level, and those are obtained from the Department of Housing and Urban Development (HUD). Vacancy rates of residential buildings and the number of residential units are important indicators in explaining housing prices. There are other important factors that might impact housing prices, such as household income, school quality, and public transportation service. Unfortunately, these are not available as a panel dataset in extensive regions like our study area, US Great Lakes Megaregion. However, as we explained above, our panel data analysis with fixedeffects can reduce the omitted variable bias by controlling for the location-specific effects (Anna et al., 2014; Baltagi, 2008).
Table 1 Descriptive Statistics and Data Sources Variables
Mean
S.D.
Data source
Median housing price ($)
164,447
111,061
Zillow (2013June~2016May)
Growth in Median housing price (%) Planning Time Index(PTI)
0.31
0.76
3.01
1.50
Growth in Planning Time Index (%) Interest rate (%) Mortgage rate (%) Construction Price Index Unemployment rate (%)
1.34
16.66
0.79 4.03 112.46 5.64
0.09 0.27 3.53 1.69
Number of household (N) Number of firm (N) Residential vacancy rate (%) Business vacancy rate (%) Urban (Urban area, pop. more than 2,500) Metro (Metropolitan statistical area) Distance to CBD (mile) Park (%)
8581 726 3.32 9.58 0.59
6546 677 4.01 6.56 0.49
Federal Reserve Bank Freddie Mac Census BLS (Bureau of Labor Statistics) HUD (USPS vacancy data) HUD (USPS vacancy data) HUD (USPS vacancy data) HUD (USPS vacancy data) Census Tiger/shape file
0.97
0.14
Census Tiger/shape file
24.26 2.21
23.46 4.07
Lake (%)
2.06
3.45
Census Tiger/shape file USGS (United States Geological Survey) USGS (United States Geological Survey)
Spring (March–May) Summer (June–August) Fall (September–November) Winter (December–February)
0.26 0.23 0.26 0.25
0.44 042 0.44 0.44
HERE (2013June~2016May)
4. Empirical results 4.1. Descriptive statistics Table 1 presents descriptive statistics of the independent and dependent variables used in our analysis. Median housing price of the Great Lake Megaregion is $164,447 and its monthly growth rate is 0.31% during 2013 June through 2016 May. The average value of the Planning Time Index is 3.01 and its monthly growth rate is 1.34%. Interest rates and mortgage rates are 0.79% and 4.03%, respectively. The average Construction Price Index obtained from the Census is 112.46. The average unemployment rate is 5.64%. The average number of households and the average number of firms at the zip code level is 8,581 and 726, respectively. And their vacancy rates are 3.32% and 9.58%, respectively. Urban and Metro dummies, defined by the US Census as shown in Table 1, are used. Among the zip-codes used in our analysis, 59% is urban area and 97% is metropolitan area. The average percentages of the park and lake are about 2%.
Chicago and Detroit. It shows that housing prices are spatially different. Particularly, Fig. 7 (left) presents growth rate in median housing price decreases away from the CBD. In addition, as shown in Fig. 7 (right), the growth rate in the housing price is not stable and slightly decreases. However, the overall growth rate is constantly positive, indicating that overall average housing prices in this region have continuously increased during the period.
3.2.3. Other variables Four macro factors affecting housing price are used in this study: interest rates, mortgage rates, construction costs, and unemployment rates. Data for interest rates and mortgage rates that are important elements associated with housing price are obtained from the Federal Reserve Bank and Freddie Mac, respectively. Data for monthly construction cost index are obtained from the Census. This index presents
4.2. Estimation results of housing price model Table 2 presents estimation results of our empirical model represented by the Eq. (5). The first column shows estimation results only 136
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Table 2 Estimation Results of the Housing Price Model Variables
Model (1)
Model (2)
Model (3)
Model (4)
Model (5)
Model (6)
PTI Growth
−0.0005*** (0.0001) 0.7134*** (0.0026) 0.0027 (0.0038) 0.0055*** (0.0014) 0.0021 (0.0450)
−0.0004*** (0.0001) 0.7040*** (0.0025) −0.0365*** (0.0050) 0.0158*** (0.0017) −0.7025*** (0.0261) 0.2798*** (0.0162) 0.0105*** (0.0014) −0.0276*** (0.0015) −0.0067*** (0.0008) −0.0009 (0.0025) 0.0016*** (0.0005)
−0.0004*** (0.0001) 0.7039*** (0.0025) −0.0394*** (0.0051) 0.0153*** (0.0017) −0.7027*** (0.0261) 0.2796*** (0.0162) 0.0105*** (0.0014) −0.0274*** (0.0015) −0.0068 (0.0008) −0.0013 (0.0027) 0.0015*** (0.0005) 0.0011* (0.0006) 0.0006 (0.0006) 0.0000 (0.0001) −0.0013 (0.0056) 0.0078 (0.067)
−0.0004*** (0.0001) 0.7076*** (0.0026) −0.0280*** (0.0050) 0.0104*** (0.0017) −0.2583*** (0.0266) 0.4448*** (0.0166) 0.0298*** (0.0014) −0.0177*** (0.0015) −0.0060*** (0.0008) −0.0011 (0.0027) 0.0016*** (0.0005) 0.0011** (0.0005) 0.0008* (0.0005) 0.0000 (0.0001) −0.0020 (0.0064) 0.0082** (0.043) 0.2357*** (0.0065) 0.1976*** (0.0062) −0.1025*** (0.0059) −4.5488*** (0.2248) No No 74,552 0.5497
−0.0007*** (0.0001) 0.6954*** (0.0026) −4.6948*** (0.0640) −0.0093** (0.0037) 0.1008*** (0.0271) 0.3193*** (0.0165) 0.0324*** (0.0014) −0.0726*** (0.0028) −0.0176*** (0.0043) 0.3542*** (0.1263) −0.0061*** (0.0021)
−0.0003*** (0.0001) 0.7127*** (0.0025) −4.9632*** (0.0605) −0.0049 (0.0035) −1.2229** (0.6136) 1.1630*** (0.1991) 0.1161*** (0.0087) −0.0129*** (0.0035) −0.0184*** (0.0042) −0.0761 (0.1192) −0.0034* (0.0019)
0.2244*** (0.0064) 0.2022*** (0.0062) −0.0664*** (0.0058) 48.1618*** (1.3161) Yes No 74,552 0.5512
43.2400*** (2.0940) Yes Yes 74,552 0.6137
HP Growth (t-1) ln(HP) (t-1) PTI (t-1) Interest Rate (t-1) Mortgage Rate (t-1) Construction Cost Index (t-1) Unemployment rate (t-1) Housing vacancy rate (t-1) ln(Household) (t-1) Firm vacancy rate (t-1) Park Lake Distance to CBD Urban Metro Summer Fall Winter Constant Fixed-effects (Zip-code) Fixed-effects (Month) Number of observation R2
No No 74,552 0.5134
−1.1440*** (0.2187) No No 74,552 0.5257
−1.1182*** (0.2197) No No 74,552 0.5257
***, ** and * indicate significance at the 0.1%, 1% and 5% levels, respectively.
negatively associated with growth in housing prices. More specifically, all else being equal, an additional one percentage point increase in unemployment rates is associated with a decrease in 0.012 percentage point of growth rate in median housing prices. Vacancy rates of firms are negatively associated with growth in housing prices. In model (3) and (4), we added location-specific characteristics, such as percentage of park and lake, distance to CBD, and urban and metro dummy variables, which are related to the growth in housing prices. As expected, higher percentages of park and lake in a neighborhood are associated with higher housing price growth. The urban dummy variable is not statistically significant, but the metro dummy variable is statistically significant and positively associated with growth in housing prices. Seasonality variables are significantly associated with housing price growth. Particularly, the summer dummy variable has a positive effect on housing price, indicating that the growth rate in housing prices is generally 0.2 percentage point higher during the summer in comparison to the spring. On the other hand, housing prices during the winter are −0.06 percentage point lower as compared to growth in housing price in the spring season. In general, higher density generates higher traffic volumes that make traffic worse. Hence, traffic congestion growth would be different between urban and rural areas and between metro and non-metropolitan areas. And such different growth patterns would differently
including housing prices and traffic congestion index without other control variables. The result presents that traffic congestion growth is negatively associated with growth in median housing prices. This result is slightly changed when including control variables and fixed-effects terms, but almost consistent and robust as shown in the model (2) through model (6). Since different locations and different time influence growth in housing prices (see Fig. 7, again), model (6) that includes location-specific and time-specific fixed-effects is the best fit to explain the relationship between congestion growth and housing price growth. Specifically, a 10 percentage point increase in the growth rate of the Planning Time Index at the zip code level would lead to a decrease in growth rate of monthly median housing prices by 0.003 percentage point. In other words, all else being equal, every 10 percentage point increase in traffic congestion growth in a local neighborhood is associated with a decrease in the growth rate of average annual median housing prices by 0.003 percentage point. As expected, the estimated coefficients for each macro factor such as interest rates, mortgage rates, construction costs, and unemployment rates are significantly associated with housing prices in model (6). Specifically, interest rates are negatively associated with housing price growth, whereas mortgage rates are positively associated with housing price growth. An increase in construction cost is positively associated with growth in housing prices. In contrast, unemployment rates are 137
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Fig.8. Correlations between growth rates of housing price and traffic congestion
affect growth in housing prices in the areas. In order to verify it, we first check the correlation of congestion growth and housing prices growth using all samples, metropolitan samples, and urban area samples. As shown in Fig. 8, correlations between them exist in each sample. And then, we estimate the effects of traffic congestion growth on the housing price growth using the same equations with location and time fixedeffects. Table 3 shows the estimation results. The results show that the effects of congestion growth on housing price growth are different between urban and rural areas and between metro and non-metro areas. Particularly, while the effects are not statistically significant in rural and non-metro areas, the effects are significant in urban and metro areas, indicating that congestion growth negatively affects housing price growth in urban and metro areas, but not in rural and non-metropolitan areas. Finally, we attempt to closely look at the spatial differences in the effects of congestion growth on housing price growth because traffic congestion is generally higher in areas close to CBD. We first include an interaction term of PTI growth and distance to CBD, and then check its effects on housing price growth. As shown in Table 4, the interaction term is statistically significant and positive, indicating that the effects of congestion growth on housing price growth become smaller in the areas getting away from the CBD. We test the distance-sensitivity effects of
the congestion growth. Model (2) in Table 4 shows that there is no significant effect of congestion growth on the housing price growth in areas within 5 miles from the CBD. One possible explanation is that urban amenity-premium such as better access to jobs, shopping, restaurants, and recreational facilities are greater than the disamenities of traffic congestion in areas near CBD. However, congestion growth has a negative effect on housing price growth in areas 5~15 miles away from the CBD. There is no significant relationship between congestion growth and housing price growth in areas 15~20 miles away from the CBD. This may be because urban spatial structure is not monocentric, but polycentric because several employment subcenters exist in areas away from the CBD (McMillen, 2001; Arnott, 1998). Like areas within 5 miles from the CBD, benefits of amenities near employment subcenters are greater than the disamenities of traffic congestion. To confirm it, more specific analysis should be followed to generalize our results, but it is beyond our scope. Nevertheless, these results provide important implications that congestion relief policy should be spatially differentiated.
5. Discussion and conclusions Traffic congestion is getting worse and commuting cost is getting 138
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areas. Our findings show that while there is no significant relationship between congestion growth and housing price growth in rural and nonmetro areas, the effects of congestion growth on housing price growth in urban and metro areas are significant. Moreover, we found that the effect of congestion growth on housing price growth is not the same across the US Great Lakes Megaregion, but spatially different across areas moving away from the CBD. Specifically, the result shows that there is no significant effect of growth in traffic congestion on growth in housing prices in areas within 5 miles from the CBD, whereas the effect appears in areas 5~15 miles away from the CBD. Our findings suggest two important implications. First, the negative externalities of traffic congestion include noise, air pollution, stress, energy consumption, and wasting travel time as previous studies demonstrated, which may lead to falling residential property values. Although the direct mechanism of the relationship between traffic congestion and housing prices is not explained in this study, we verified that traffic congestion functions as a disamenity in a local neighborhood. Transportation policies that aim to relieve traffic congestion should consider that severe traffic congestion may have an adverse effect on local property values. Second, the negative effect of traffic congestion does not cause a decrease in housing values in areas close to the CBD. This is likely because the benefits of urban amenities exceed the disamenities of traffic congestion. However, as distance increases from the CBD, the benefits of urban amenities become smaller, consequently, the negative effect of congestion on property values is clear. Although more investigation with consideration of location-specific characteristics and sensitivity analysis is necessary for providing further policy implications, congestion reduction efforts can bring people to move into these areas. There are some limitations in this study. First, although our direct measures of traffic congestion provide evidence of its negative effect on property values, investigations with various measures of traffic congestion are needed before stronger conclusions can be drawn. Second, some have worried that the quality of Zillow data is not high. However, the estimates have been used in several housing-related studies and found to be reliable (Kaplan et al., 2016; Huang and Tang, 2012; Mian and Sufi, 2009). Therefore, we used housing price data from Zillow and median housing values at the zip code level. We suggest that investigations with data for individual property values would provide more information on the relationship between traffic congestion and property values. We also suggest that such approach would be helpful for investigating the relationship between traffic congestion and housing prices at the specific region. Third, in this study, we only focused on limited time periods because the available NPMRDS data are recently released. Future research using the same dataset with more time periods would provide more solid and better results. In addition, we focused only on the single-family housing prices, although there are various types of housings such as condominiums, apartments, and town homes. Future study needs to investigate how the traffic congestion differently affects housing prices of different types of housings. Finally,
Table 3 Estimation Results of the Housing Price Model (Urban vs. Rural/Metro vs. NonMetro)
PTI growth HP growth (t-1) ln(HP) (t-1) PTI (t-1) Interest rate (t-1) Mortgage rate (t-1) Construction Cost Index (t-1) Unemployment rate (t-1) Housing vacancy rate (t-1) ln(Household) (t-1) Firm vacancy rate (t-1) Constant Fixed-effects (Zipcode) Fixed-effects (Month) Number of observation R2
Metro
Non-Metro
Urban
Rural
(1)
(2)
(3)
(4)
−0.0003*** (0.0001) 0.7121*** (0.0026) −4.9195*** (0.0605) −0.0051 (0.0035) −1.2014** (0.6109) 1.1779*** (0.1983) 0.1162*** (0.0086) −0.0135*** (0.0034) −0.0194*** (0.0041) −0.0592 (0.1192) −0.0037* (0.0019) 42.5413*** (2.0900) Yes
−0.0012 (0.0014) 0.7325*** (0.0187) −6.4212*** (0.5408) −0.0013 (0.0417) −2.7523 (6.6993) 0.5355 (2.1223) 0.1084 (0.0927) −0.0242 (0.0480) 0.1503** (0.0728) −0.0230 (1.1217) −0.0078 (0.0178) 61.3830*** (20.5957) Yes
−0.0004** (0.0002) 0.7071*** (0.0033) −4.6174*** (0.0765) −0.0050 (0.0041) −0.4207 (0.7552) 1.0695*** (0.2455) 0.0886*** (0.0107) −0.0185*** (0.0043) −0.0212*** (0.0046) 0.4337** (0.1965) −0.0103*** (0.0024) 37.4938*** (2.8522) Yes
−0.0002 (0.0002) 0.7205*** (0.0040) −5.4774*** (0.0984) −0.0005 (0.0064) −2.5645** (1.0263) 1.2694*** (0.3318) 0.1564*** (0.0144) −0.0027 (0.0058) −0.0001 (0.0104) −0.3844** (0.1565) 0.0073** (0.0033) 47.6470*** (3.2900) Yes
Yes
Yes
Yes
Yes
72,994
1,558
43,838
30.714
0.6168
0.5603
0.6176
0.6127
***, ** and * indicate significance at the 0.1%, 1% and 5% levels, respectively.
higher in every major metropolitan area. In this study, we explored the negative externalities of traffic congestion focusing on the housing prices. We used a unique transportation dataset, NPMRDS, which contains information on every 5-min travel times across extensive regions to measure traffic congestion at the local level. Through the data we calculated monthly Planning Time Index as a measure of traffic congestion at the zip code level. In addition, in order to examine the relationship between the congestion growth and housing price growth, we combined the traffic congestion measures with median housing price data and a variety of socioeconomic data from various sources. Our dynamic housing price panel models accounting for locationand time-specific fixed-effects show that growing traffic congestion is negatively associated with growth in housing prices. Particularly, the effect of traffic congestion growth on housing price growth are different between urban and rural areas and between metro and non-metro Table 4 Estimation Results of the Housing Price Model (Distance from CBD)
PTI growth PTI growth × CBD Other variables Fixed-effects (Zip code) Fixed-effects (Month) Number of observation R2
All
< 5 miles
5~10 miles
10~15 miles
15~20 miles
(1)
(2)
(3)
(4)
(5)
−0.000716*** (0.000166) 0.000016*** (0.000005) Yes Yes Yes 74,552 0.6137
−0.0003 (0.0004)
−0.0006** (0.0003)
−0.0005** (0.0002)
−0.0005 (0.0004)
Yes Yes Yes 8,875 0.5642
Yes Yes Yes 14,252 0.6322
Yes Yes Yes 10,327 0.6575
Yes Yes Yes 7,720 0.6498
***, ** and * indicate significance at the 0.1%, 1% and 5% levels, respectively. 139
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we examined the relationship between traffic congestion and housing prices, but traffic-type is also important in explaining housing prices. For example, the negative effects of freight traffic can be much larger than those of auto traffic. Because NPMRDS data did not contain the information on traffic volume, we could not explore the effects of different types of traffic congestion. We suggest that combining volume data with NPMRDS would be helpful for future research.
Jin, J., Rafferty, P., 2017. Does congestion negatively affect income growth and employment growth? Empirical evidence from US metropolitan regions. Transp. Policy 55, 1–8. Kaplan, G., Mitman, K., Violante, G., 2016. Non-durable Consumption and Housing Net Worth in the Great Recession: Evidence from Easily Accessible Data, Working Paper, No. 22232. National Bureau of Economic Research, Cambridge, MA. http://www. nber.org/papers/w22232. Kim, K., Park, S., Kweon, Y., 2007. Highway traffic noise effects on land price in an urban area. Transp. Res. D 12 (4), 275–280. Kohlhase, J., 1991. The impact of toxic waste sites on housing values. J. Urban Econ. 30, 1–26. Matthews, J., Turnbull, G., 2007. Neighborhood street layout and property value: The interaction of accessibility and land use mix. J. Real Estate Financ. Econ. 35, 111–141. McMillen, D., 2001. Nonparametric employment subcenter identification. J. Urban Econ. 50 (3), 448–473. Mian, A., Sufi, A., 2009. The consequences of mortgage credit expansion: Evidence from the U.S. mortgage default crisis. Q. J. Econ. 124 (4), 1449–1496. Mills, E., 1972. Markets and efficient resource allocation in urban areas. Swed. J. Econ. 74, 100–113. Muth, R., 1969. Cities and Housing, Chicago IL. University of Chicago Press. Nelson, J., 1982. Highway noise and property values: A survey of recent evidence. J. Transp. Econ. Policy 16, 117–138. Osland, L., Thorsen, I., 2008. Effects on housing prices of urban attraction and labormarket accessibility. Environ. Plan. A 40, 2490–2509. Ossokina, I., Verweij, G., 2015. Urban Traffic Externalities: Quasi-experimental Evidence from Housing Prices. Reg. Sci. Urban Econ. 55, 1–13. Rafferty, P., Jin, J., Silber, H., 2017. Examining multistate mobility performance in the Mid-America Region. J. Transp. Eng. 143 (2), 1–9. Reichert, A., 1990. The impact of interest rates, income, and employment upon regional housing prices. J. Real Estate Financ. Econ. 3 (4), 373–391. Roback, J., 1982. Wages, rents, and the quality of life. J. Polit. Econ. 90 (6), 1257–1278. Rosen, S., 1974. Hedonic prices and implicit markets: Product differentiation in pure competition. J. Polit. Econ. 82 (1), 34–55. Rosenthal, S., Strange, W., 2004. Evidence on the nature and sources of agglomeration economies. In: Henderson, J.V., Thisse, J.F. (Eds.), Handbook of Urban and Regional Economics 4. Elsevier, Amsterdam, pp. 2119–2171. Schrank, D., Eisele, B., Lomax, T., 2012. TTI’s 2012 Urban Mobility Report: Powered by INRIX Traffic Data, Texas A&M Transportation Institute. The Texas A&M University System, College Station. https://www.pagregion.com/Portals/0/documents/ HumanServices/2012MobilityReport.pdf. Sweet, M., 2014. Traffic congestion’s economic impacts: Evidence from US metropolitan regions. Urban Stud. 51 (10), 2088–2110. Sweet, M., Harrison, C., Kanaroglou, P., 2015. Gridlock in the Greater Toronto area: Its geography and intensity during key periods. Appl. Geogr. 58, 167–178. Yan, S., Delmelle, E., Duncan, M., 2012. The impact of a new light rail system on singlefamily property values in Charlotte, North Carolina. J. Transp. Land Use 5 (2), 60–67.
References Alonso, W., 1964. Location and Land Use: Toward a General Theory of Land Rent. Harvard University Press, Cambridge, MA. America, 2050 Regional Plan Association, http://www.america2050.org, Accessed date: 20 October, 2017. Anas, A., Moses, L., 1979. Mode choice, transport structure and urban land use. J. Urban Econ. 6 (2), 228–246. Anna, C., Antonello, D., Angelo, P., 2014. A panel data approach to evaluate the passenger satisfaction of a public transport service. Procedia Economics and Finance 17, 231–237. Arnott, R., 1998. Traffic congestion tolling and urban spatial structure. J. Reg. Sci. 38 (3), 495–504. Baltagi, B.H., 2008. Econometric analysis of panel data. Wiley. Boarnet, M., Chalermpong, S., 2001. New highways, house prices, and urban development: A case study of toll roads in Orange county, CA. Hous. Policy Debate 12 (3), 575–605. Brasington, D., Hite, D., 2005. Demand for environmental quality: A spatial hedonic analysis. Reg. Sci. Urban Econ. 35, 57–82. Cervero, R., Duncan, M., 2004. Neighborhood composition and residential land prices: Does exclusion raise or lower values? Urban Stud. 41 (2), 299–315. Cheshire, P., Sheppard, S., 1995. On the price of land and the value of amenities. Economica 62, 247–267. Giuliano, G., Small, K., 1991. Sucenters in the Los Angeles Region. Reg. Sci. Urban Econ. 21 (2), 163–182. Handy, S., Niemeier, D., 1997. Measuring accessibility: An exploration of issues and alternatives. Environ. Plan. A 29, 1175–1194. Huang, H., Tang, Y., 2012. Residential land use regulation and the US housing price cycle between 2000 and 2009. J. Urban Econ. 71, 93–99. Hymel, K., 2009. Does traffic congestion reduce employment growth? J. Urban Econ. 65 (2), 127–135. Iacono, M., Levinson, D., 2011. Location, regional accessibility, and price effects: Evidence from home sales in Hennepin County, Minnesota. Transp. Res. Rec. 2245, 87–94. Iacono, M., Levinson, D., 2017. Accessibility dynamics and location premia: Do land values follow accessibility changes? Urban Stud. 54 (2), 364–381.
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Journal of Transport Geography journal homepage: www.elsevier.com/locate/jtrangeo
Externalities of auto traffic congestion growth: Evidence from the residential property values in the US Great Lakes megaregion
T
⁎
Jangik Jina, , Peter Raffertyb a b
Kyung Hee University, Graduate School of Tourism, Department of Real Estate, 1, Hoegi-dong, Dongdaemun-gu, Seoul, South Korea Gannett Fleming, Inc., 8025 Excelsior Drive, Madison, WI 53717, United States
A R T I C LE I N FO
A B S T R A C T
Keywords: Traffic Congestion Housing Prices Great Lakes Megaregion Panel Data Analysis
Traffic congestion is getting worse and commuting cost is getting higher in every major metropolitan area. In this study, we attempt to explore the negative externalities of traffic congestion focusing on housing prices. We use a unique transportation dataset, the National Performance Management Research Data Set (NPMRDS), which contains information on every 5 min travel time across the extensive region to measure traffic congestion at the local level. In order to estimate the effect of traffic congestion on residential property values, we use a dynamic hedonic price model with a monthly panel dataset. Our findings show that while there is no significant relationship between congestion growth and housing price growth in rural and non-metro areas, the effects of congestion growth on housing price growth in urban and metro areas are obvious. We verify that traffic congestion functions as a disamenity in a local neighborhood. Transportation policies that aim to relieve traffic congestion should consider that severe traffic congestion may have an adverse effect on local property values.
1. Introduction Transportation accessibility is one of the primary determinants of residential property values and changes in those values, since the effects of the transportation accessibility are generally reflected in residential property values. People can easily access jobs, shopping, restaurants, museums, or other important points of reference when transportation infrastructure improves in their communities. Hence, increases in accessibility provide a better quality of life to local residents, which results in positive effects on the property values in their communities. In contrast, accessibility will decrease when transportation demand increases without additional development of transportation facilities. Decreased accessibility may cause traffic congestion and gridlock that negatively affects the quality of life of residents. In this case, property values will decrease. Numerous studies have demonstrated there is a significant relationship between transportation accessibility and residential property values (Ossokina and Verweij, 2015; Osland and Thorsen, 2008; Matthews and Turnbull, 2007; Cervero and Duncan, 2004). Also, various transportation accessibility measures such as job accessibility, transit accessibility, and travel time accessibility have been developed and used in many previous studies. Nevertheless, there is little agreement regarding which measure appropriately evaluates the transportation accessibility in a local community. Studies demonstrated that
⁎
Corresponding author. E-mail addresses: [email protected] (J. Jin), praff[email protected] (P. Rafferty).
https://doi.org/10.1016/j.jtrangeo.2018.05.022 Received 14 April 2017; Received in revised form 28 May 2018; Accepted 28 May 2018 0966-6923/ © 2018 Published by Elsevier Ltd.
two elements, such as transportation and activity, are considered in measuring accessibility (Handy and Niemeier, 1997). The former, transportation element, is related to the transportation costs measured by travel distance, travel time, or traffic congestion, whereas the latter, activity element, represents the degree of the locational attractiveness measured by closeness to the opportunities for jobs, shopping, and recreational activities. A number of studies have investigated the effects of activity-based accessibility on housing values (Osland and Thorsen, 2008; Matthews and Turnbull, 2007; Cervero and Duncan, 2004) and the effects of transportation accessibility measured by travel time and distance on housing values (Iacono and Levinson, 2017; Yan et al., 2012). However, only a few studies have attempted to measure accessibility using traffic congestion (Sweet et al., 2015), and examined the relationship between traffic congestion and housing values (Ossokina and Verweij, 2015). Hence, our understanding of the relationship between traffic congestion and property values is insufficient and limited. One reason for the lack of the research on the relationship between traffic congestion and housing values is the deficiency of micro-level traffic data covering extensive regions to measure the traffic congestion at the neighborhood level. The widely used traffic congestion data provided by Texas A&M Transportation Institute (TTI) is at the metropolitan level. The data do not provide any information regarding traffic congestion at the neighborhood level.
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demonstrated that transportation accessibility is one of the important factors in explaining housing prices (Iacono and Levinson, 2011; Osland and Thorsen, 2008; Cheshire and Sheppard, 1995).
Recently, along with the development of information technology and transportation engineering, transport big data, such as probe data offer the potential to develop transformative applications that can improve transportation operations, planning, and maintenance as well as detect travel conditions and traffic congestion (Rafferty et al., 2017). Particularly, the National Performance Management Research Data Set (NPMRDS), since 2013, enables researchers to examine the conditions of transportation performance management at the micro level as well as across the extensive regions. This dataset offers an unprecedented opportunity to evaluate traffic congestion by providing travel times for the entire National Highway System by both passenger and freight traffic in 5-min increments. The data also provide spatial references with GIS features, which enables spatial analysis. Using the transportation big data (NPMRDS) with the research perspective, this study is to explore the effect of traffic congestion on residential property values. Particularly, by focusing on the Great Lakes Megaregion, we investigate whether or not the traffic congestion is negatively capitalized into the median housing prices at the neighborhood level. We obtain monthly housing price panel data (2013–2016) at the zip code level from Zillow (real estate company), and calculate monthly PTI (Planning Time Index) as a traffic congestion index using NPMRDS data obtained from the Federal Highway Administration (FHWA). In order to estimate the effect of traffic congestion on the residential property values, we use a dynamic hedonic price model with a monthly panel dataset. Our research is expected to contribute to the growing literature on the effect of transportation policies that aim to mitigate severe traffic congestion. In addition, we suggest the applicability of transportation big data into research in the field of transportation engineering, planning, and geography.
2.2. Traffic congestion and housing values Increasing traffic congestion is not only a primary source of greenhouse gases, local air pollution, and noise annoyance, but also one of the negative factors affecting local economies with respect to the employment and productivity (Sweet, 2014). Generally, congestion is indicative of high economic activities. However, firms are likely to relocate from the areas undergoing severe traffic congestion to the areas with unused road capacity when diseconomies caused by traffic congestion are much larger than the benefits of agglomeration economies (Rosenthal and Strange, 2004). In this sense, traffic congestion can drag down the local economy. Previous studies demonstrated that traffic congestion has a negative effect on the local economy, particularly it leads to a decrease in employment growth (Jin and Rafferty, 2017; Sweet, 2014; Hymel, 2009). This perspective, especially among engineers and economists, is based on the notions that traffic congestion is considered as a diseconomy. Unlike this perspective, urban designers regard the traffic congestion as a function of local disamenity. According to the urban economic theory, individual households choose their location to maximize their utility, and features of neighborhoods such as average commute time, proximity to rail lines, and ethnic characteristics have substantial effects on the household residential location decision. Contrary to the local amenities that positively affect household location decision, local environmental disamenities such as noise, crime, pollution, and local traffic congestion have negative effects on household residential location decision (Roback, 1982; Rosen, 1974), and thus they have detrimental effects on residential property values (Ossokina and Verweij, 2015). A number of previous studies have empirically demonstrated that such disamenities have negative effects on the housing values (Brasington and Hite, 2005; Kohlhase, 1991; Nelson, 1982). They showed that wide range of tangible attributes such as structural, physical, and environmental factors have substantial effects on the housing prices. For example, an increase in traffic noise pollution resulted from the new transportation infrastructure development has a negative effect on housing prices (Kim et al., 2007; Nelson, 1982). In contrast, relatively little work has been conducted to quantify the impact of intangibles, especially traffic congestion. Only a few recent studies attempted to directly measure the traffic congestion at the local level (Sweet et al., 2015), and identified its effects on the housing prices (Ossokina and Verweij, 2015). As numerous studies show that traffic congestion are getting worse in the metropolitan areas (Schrank et al., 2012), additional investigation on the negative externalities of traffic congestion is worthwhile.
2. Literature review 2.1. Accessibility and housing values In the perspective of urban economic theory, the study of the relationship between transportation and housing price has two streams. First, it starts with Alonso (1964)’s urban monocentric model that explains residential activities are determined by a trade-off between the accessibility of being close to the central business district (CBD) and property values. This monocentric model gained widespread popularity because it provides a framework for empirically examining the relationship between location and property values. This theory has been further developed by Muth (1969) and Mills (1972), and has offered elaborated theoretical frameworks on urban spatial structure by explaining trade-off between housing price and transportation cost. However, the original model assumes that transportation is ubiquitous and available via a single mode, which has been criticized by others researchers who argued that multiple nodes are better to explain the urban spatial structure (Anas and Moses, 1979). Recently, rapid urbanization and suburbanization have changed urban spatial structures, and thus numerous studies have shown that a polycentric model is more appropriate to explain urban spatial structures (Giuliano and Small, 1991; McMillen, 2001). A second approach to explore the relationship between transportation and housing price is a hedonic price model developed by Rosen (1974). The basic idea of the model is that people consider many factors of their residential environments that influence their property values when they choose their residential locations. Thus, this model explains the composition of housing price by disentangling the bundle of housing services. According to the urban economic theory, if highway or transportation networks improve accessibility, its premium will be reflected in higher housing prices (Boarnet and Chalermpong, 2001). Moreover, numerous studies have tested the effects of accessibility to the light rail station and public transportation, average commuting time, road capacity, and highway network system on the property values. Through this hedonic price model, the empirical studies have
3. Methodology 3.1. Analytical framework In this section, we discuss our analytical framework to examine the relationship between traffic congestion and housing prices. Housing prices are primarily determined by supply of and demand for housing. Hence, we employ a reduced form equilibrium model to explain the effect of traffic congestion on changes in housing prices across zip code regions. In equilibrium, supply and demand for housing services during the period determine housing prices. This relationship can be described as follows;
HPt = f (QtD , QtS ) where HPt is the housing prices during period t, 132
(1) QtD
is a demand for new
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Fig. 1. US Megaregions and US Great Lakes Megaregion (source:America, 2050http://www.america2050.org)
construction obtained from the United States (US) Census. Ut is an unemployment rate. Vt and Zt are vacancy rates of housing and firms, respectively. Substitution of Eqs. (2) and (3) into the Eq. (1) yields the function of housing price, and the equilibrium reduced-form housing price equation is established as follows;
HPt = f (Tt , Dt , Mt , Lt , St , It , Ct , Ut , Vt , Zt )
(4)
Through the Eq. (4), we establish our empirical model for analyzing the effects of traffic congestion on housing prices. Particularly, we focus on the growth in housing prices and growth in traffic congestion with location-specific fixed-effects based on the panel dataset, which is the following function: j
∆HPit = α + β1 ∆Tit + β2 Ti, t − 1 + β3 HPi, t − 1 + β4 ∆HPi, t − 1 + βk
∑ Xk,t−1 k=5
+ δi + θt + ϵit
where ΔHPit is growth in housing prices in zip code i, during the time, t, ΔTit is growth in traffic congestion in zip code i, during the time, t, Xk, t−1 is the control variables such as interest rates, mortgage rates, construction cost index, unemployment rates, housing vacancy rates, the number of households, and firm vacancy rates during the lagged time, t1, δi is the location-specific fixed-effects, θt is the time-specific fixedeffects, and ϵit is the error term. This dynamic panel data analysis with fixed-effects has advantages over cross-sectional data analysis. A benefit of using the panel data is to eliminate unobserved variables that are specific to each sample and time-invariant over time. The panel data also allow researchers to study dynamics of housing prices which change over time. In addition, the panel data contain more information, more variability and thus less collinearity among the variables, which produce more efficient and precise estimations (Anna et al., 2014; Baltagi, 2008). Therefore, the dynamic panel model is appropriate to investigate the effects of traffic congestion measured by longitudinal transportation big data, such as the NPMRDS, on housing prices.
Fig. 2. Time Trend of Planning Time Index
housing during period t, and QtS is a supply of new housing during period t. Housing price is affected not only by macro factors such as economic growth rates and changes in interest rates, but also micro factors such as neighborhood environments, household characteristics, and housing characteristics. Particularly, these components are related to supply and demand for housing services. Thus, we need to consider both factors to estimate of housing prices. The demand for housing services can be derived based on the assumption of household utility maximization. The basic equation of housing demand is as follows;
QtD = g (Tt , Dt , Mt , Lt , St )
(2)
where Tt is a traffic congestion index, Dt is the number of household during the period t, and Mt is a mortgage rate during the period t, and Lt is locational characteristics, measured by percentage of park and lake, distance to the CBD, and urban or rural area. St is a seasonal dummy variable designed to control for seasonal fluctuations in housing prices. On the other hand, it is assumed that builders are interested in their profit maximization, so they consider the current and expected future risk-adjusted returns associated with home building (Reichert, 1990). Hence, the function of housing supply includes interest rates, construction cost, and financing or carrying costs.
QtS = h (It , Mt , Ct , Ut , Vt , Zt )
(5)
3.2. Data and variables We focus on the US Great Lakes Megaregion, which includes large metropolitan areas such as Chicago, Detroit, Minnesota, Cleveland, St. Louis, and Pittsburgh as well as ten states such as Wisconsin, Minnesota, Michigan, Iowa, Illinois, Ohio, Kansas, Indiana, Kentucky, and Missouri (See Fig. 1). The dataset consists of a balanced panel based on the monthly data obtained from various sources, which are described in this section. We focus on the time period between 2013 July
(3)
where It is an interest rate during period t, Mt is a mortgage rate during period t, Ct is a construction cost index for single-family houses under 133
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Fig. 3. Spatial Distribution of Planning Time Index, Chicago and Detroit (2016 May)
Fig. 4. Spatial and temporal distribution of traffic congestion (PTI), Great Lakes Megaregion
index cannot provide any information on the traffic congestion at the neighborhood level. The NPMRDS (National Performance Management Research Data Set) enables researchers to examine the conditions of transportation performance management at the micro level as well as across the extensive regions. Particularly, this dataset offers an unprecedented opportunity to evaluate traffic congestion by providing travel times for the entire National Highway System by both passenger and freight traffic in 5-min. increments. It comprises around 280 thousand directional segments, defined by traffic message channel (TMC), typically around two miles long. The data also include nearly two million GIS features, enabling mapping and spatial analysis, which allows us to directly measure traffic congestion at the neighborhood level (Rafferty et al., 2017). Generally, traffic congestion can be measured in a variety of ways, but
and 2016 May for data consistency.
3.2.1. Traffic congestion measure Traditionally, two measures have been widely used for measuring traffic congestion. The first one is to use the average journey to work travel time, which is measured and provided by the US Census Bureau. Average travel time is a simple way to measure overall levels of congestion, but it cannot provide specific commuter’s experience. For example, it only captures the average travel time during the certain time period, but it cannot consider traffic delay during the peak-period hours (rush hours) that are highly concerned by every commuter. The second one is to use travel delay index that can be obtained from TTI. This index provides a direct measure of the delayed hours due to traffic congestion. However it is measured at the metropolitan level, so this 134
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shown in Fig. 2, the average measured PTIs have increased in the US Great Lakes Megaregion between 2013 July and 2016 May. Fig. 3 describes the spatial distribution of the aggregated Planning Time Index at the zip code level in Chicago and Detroit (these two samples are the biggest cities in the US Great Lakes Megaregion), which shows that traffic congestion appears to be centered near downtown. Fig. 4 describes the spatial and temporal distribution of traffic congestion growth in the Great Lakes Megaregion. It shows that traffic congestion growth decreases away from the CBD. In addition, the second figure in Fig. 4 shows that the range of the monthly PTI growth rate is between -4 and 6, but its average growth rate is almost 1.5 and constant during the period. This indicates that although the congestion growth rate is not stable, overall traffic congestion has increased in the US Great Lakes megaregion during the time periods. 3.2.2. Housing price data The extensive dataset obtained from various sources permits estimation and construction of several indices at the zip code level. First, we obtained monthly housing price data (especially, single-family housing price data) at the zip code level from Zillow (Real Estate Inc.). Zillow provides estimates of housing price for individual houses as well as median housing prices at the zip-code level. Using the data provide two advantages; first, the data contain monthly median housing prices at the local level (zip-code); second, our intention is to examine the effects of traffic congestion at the neighborhood level measured by NPMRDS on housing prices. So, recent housing prices data at the neighborhood level are necessary to combine our transportation big data. Zillow data are the best option for the analysis. In order to match the datasets of NPMRDS and housing price, we use monthly housing price data between 2013 July and 2016 May (35 months). Since the original data obtained from Zillow are not seasonally-adjusted, we adjusted the housing prices using CPI inflation index based on the 2016 May. Fig. 5 shows the time trend of the inflation-adjusted median housing prices in the study area. Fig. 6 presents a spatial distribution of the median housing prices in
Fig. 5. Time Trend of Housing Price
in this study, we use Planning Time Index (PTI) as a traffic congestion.
PTIit =
95%Travel Timeit Free Flow Travel Timeit
(6)
The PTI is an indicator of the variability of the average travel time, and it is defined as a ratio of the 95th percent peak period travel time (6~9am/3~6pm) to the free flow travel time (9am~3pm/7pm~10pm) in every weekday. If the travel time is very volatile along a particular road segment (i), it relates to the percent of time that a segment experiences congestion. Travelers may have to plan additional time into their trip for on-time arrival at their destination. For example, a value of 2 means that for a 20 min trip in light traffic, 40 min. should be planned. In order to measure the monthly traffic congestion at the neighborhood level through the PTI, the measured PTIs at each road segment (i) are aggregated by mean value at the zip code level. As
Fig. 6. Median Housing Price, Chicago and Detroit (2016 May) 135
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Fig. 7. Spatial and temporal distribution of Median Housing Price, 2013 July~2016 May
the construction cost of new houses that are under construction as defined for the single-family houses. These data are obtained at the national level. Monthly unemployment rates are obtained from the Bureau of Labor Statistics (BLS), which are at the county levels. American Community Survey (ACS) data are commonly used to do research at the micro level, such as zip code, census tract, and census block. However, these data are only available for specific time-periods, so they do not provide monthly demographic or socioeconomic characteristics at the zip code level. Instead, we use the United States Postal Service (USPS) vacancy data that include information on the number of residential and business units and vacancy rates of them at the zip code level, and those are obtained from the Department of Housing and Urban Development (HUD). Vacancy rates of residential buildings and the number of residential units are important indicators in explaining housing prices. There are other important factors that might impact housing prices, such as household income, school quality, and public transportation service. Unfortunately, these are not available as a panel dataset in extensive regions like our study area, US Great Lakes Megaregion. However, as we explained above, our panel data analysis with fixedeffects can reduce the omitted variable bias by controlling for the location-specific effects (Anna et al., 2014; Baltagi, 2008).
Table 1 Descriptive Statistics and Data Sources Variables
Mean
S.D.
Data source
Median housing price ($)
164,447
111,061
Zillow (2013June~2016May)
Growth in Median housing price (%) Planning Time Index(PTI)
0.31
0.76
3.01
1.50
Growth in Planning Time Index (%) Interest rate (%) Mortgage rate (%) Construction Price Index Unemployment rate (%)
1.34
16.66
0.79 4.03 112.46 5.64
0.09 0.27 3.53 1.69
Number of household (N) Number of firm (N) Residential vacancy rate (%) Business vacancy rate (%) Urban (Urban area, pop. more than 2,500) Metro (Metropolitan statistical area) Distance to CBD (mile) Park (%)
8581 726 3.32 9.58 0.59
6546 677 4.01 6.56 0.49
Federal Reserve Bank Freddie Mac Census BLS (Bureau of Labor Statistics) HUD (USPS vacancy data) HUD (USPS vacancy data) HUD (USPS vacancy data) HUD (USPS vacancy data) Census Tiger/shape file
0.97
0.14
Census Tiger/shape file
24.26 2.21
23.46 4.07
Lake (%)
2.06
3.45
Census Tiger/shape file USGS (United States Geological Survey) USGS (United States Geological Survey)
Spring (March–May) Summer (June–August) Fall (September–November) Winter (December–February)
0.26 0.23 0.26 0.25
0.44 042 0.44 0.44
HERE (2013June~2016May)
4. Empirical results 4.1. Descriptive statistics Table 1 presents descriptive statistics of the independent and dependent variables used in our analysis. Median housing price of the Great Lake Megaregion is $164,447 and its monthly growth rate is 0.31% during 2013 June through 2016 May. The average value of the Planning Time Index is 3.01 and its monthly growth rate is 1.34%. Interest rates and mortgage rates are 0.79% and 4.03%, respectively. The average Construction Price Index obtained from the Census is 112.46. The average unemployment rate is 5.64%. The average number of households and the average number of firms at the zip code level is 8,581 and 726, respectively. And their vacancy rates are 3.32% and 9.58%, respectively. Urban and Metro dummies, defined by the US Census as shown in Table 1, are used. Among the zip-codes used in our analysis, 59% is urban area and 97% is metropolitan area. The average percentages of the park and lake are about 2%.
Chicago and Detroit. It shows that housing prices are spatially different. Particularly, Fig. 7 (left) presents growth rate in median housing price decreases away from the CBD. In addition, as shown in Fig. 7 (right), the growth rate in the housing price is not stable and slightly decreases. However, the overall growth rate is constantly positive, indicating that overall average housing prices in this region have continuously increased during the period.
3.2.3. Other variables Four macro factors affecting housing price are used in this study: interest rates, mortgage rates, construction costs, and unemployment rates. Data for interest rates and mortgage rates that are important elements associated with housing price are obtained from the Federal Reserve Bank and Freddie Mac, respectively. Data for monthly construction cost index are obtained from the Census. This index presents
4.2. Estimation results of housing price model Table 2 presents estimation results of our empirical model represented by the Eq. (5). The first column shows estimation results only 136
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Table 2 Estimation Results of the Housing Price Model Variables
Model (1)
Model (2)
Model (3)
Model (4)
Model (5)
Model (6)
PTI Growth
−0.0005*** (0.0001) 0.7134*** (0.0026) 0.0027 (0.0038) 0.0055*** (0.0014) 0.0021 (0.0450)
−0.0004*** (0.0001) 0.7040*** (0.0025) −0.0365*** (0.0050) 0.0158*** (0.0017) −0.7025*** (0.0261) 0.2798*** (0.0162) 0.0105*** (0.0014) −0.0276*** (0.0015) −0.0067*** (0.0008) −0.0009 (0.0025) 0.0016*** (0.0005)
−0.0004*** (0.0001) 0.7039*** (0.0025) −0.0394*** (0.0051) 0.0153*** (0.0017) −0.7027*** (0.0261) 0.2796*** (0.0162) 0.0105*** (0.0014) −0.0274*** (0.0015) −0.0068 (0.0008) −0.0013 (0.0027) 0.0015*** (0.0005) 0.0011* (0.0006) 0.0006 (0.0006) 0.0000 (0.0001) −0.0013 (0.0056) 0.0078 (0.067)
−0.0004*** (0.0001) 0.7076*** (0.0026) −0.0280*** (0.0050) 0.0104*** (0.0017) −0.2583*** (0.0266) 0.4448*** (0.0166) 0.0298*** (0.0014) −0.0177*** (0.0015) −0.0060*** (0.0008) −0.0011 (0.0027) 0.0016*** (0.0005) 0.0011** (0.0005) 0.0008* (0.0005) 0.0000 (0.0001) −0.0020 (0.0064) 0.0082** (0.043) 0.2357*** (0.0065) 0.1976*** (0.0062) −0.1025*** (0.0059) −4.5488*** (0.2248) No No 74,552 0.5497
−0.0007*** (0.0001) 0.6954*** (0.0026) −4.6948*** (0.0640) −0.0093** (0.0037) 0.1008*** (0.0271) 0.3193*** (0.0165) 0.0324*** (0.0014) −0.0726*** (0.0028) −0.0176*** (0.0043) 0.3542*** (0.1263) −0.0061*** (0.0021)
−0.0003*** (0.0001) 0.7127*** (0.0025) −4.9632*** (0.0605) −0.0049 (0.0035) −1.2229** (0.6136) 1.1630*** (0.1991) 0.1161*** (0.0087) −0.0129*** (0.0035) −0.0184*** (0.0042) −0.0761 (0.1192) −0.0034* (0.0019)
0.2244*** (0.0064) 0.2022*** (0.0062) −0.0664*** (0.0058) 48.1618*** (1.3161) Yes No 74,552 0.5512
43.2400*** (2.0940) Yes Yes 74,552 0.6137
HP Growth (t-1) ln(HP) (t-1) PTI (t-1) Interest Rate (t-1) Mortgage Rate (t-1) Construction Cost Index (t-1) Unemployment rate (t-1) Housing vacancy rate (t-1) ln(Household) (t-1) Firm vacancy rate (t-1) Park Lake Distance to CBD Urban Metro Summer Fall Winter Constant Fixed-effects (Zip-code) Fixed-effects (Month) Number of observation R2
No No 74,552 0.5134
−1.1440*** (0.2187) No No 74,552 0.5257
−1.1182*** (0.2197) No No 74,552 0.5257
***, ** and * indicate significance at the 0.1%, 1% and 5% levels, respectively.
negatively associated with growth in housing prices. More specifically, all else being equal, an additional one percentage point increase in unemployment rates is associated with a decrease in 0.012 percentage point of growth rate in median housing prices. Vacancy rates of firms are negatively associated with growth in housing prices. In model (3) and (4), we added location-specific characteristics, such as percentage of park and lake, distance to CBD, and urban and metro dummy variables, which are related to the growth in housing prices. As expected, higher percentages of park and lake in a neighborhood are associated with higher housing price growth. The urban dummy variable is not statistically significant, but the metro dummy variable is statistically significant and positively associated with growth in housing prices. Seasonality variables are significantly associated with housing price growth. Particularly, the summer dummy variable has a positive effect on housing price, indicating that the growth rate in housing prices is generally 0.2 percentage point higher during the summer in comparison to the spring. On the other hand, housing prices during the winter are −0.06 percentage point lower as compared to growth in housing price in the spring season. In general, higher density generates higher traffic volumes that make traffic worse. Hence, traffic congestion growth would be different between urban and rural areas and between metro and non-metropolitan areas. And such different growth patterns would differently
including housing prices and traffic congestion index without other control variables. The result presents that traffic congestion growth is negatively associated with growth in median housing prices. This result is slightly changed when including control variables and fixed-effects terms, but almost consistent and robust as shown in the model (2) through model (6). Since different locations and different time influence growth in housing prices (see Fig. 7, again), model (6) that includes location-specific and time-specific fixed-effects is the best fit to explain the relationship between congestion growth and housing price growth. Specifically, a 10 percentage point increase in the growth rate of the Planning Time Index at the zip code level would lead to a decrease in growth rate of monthly median housing prices by 0.003 percentage point. In other words, all else being equal, every 10 percentage point increase in traffic congestion growth in a local neighborhood is associated with a decrease in the growth rate of average annual median housing prices by 0.003 percentage point. As expected, the estimated coefficients for each macro factor such as interest rates, mortgage rates, construction costs, and unemployment rates are significantly associated with housing prices in model (6). Specifically, interest rates are negatively associated with housing price growth, whereas mortgage rates are positively associated with housing price growth. An increase in construction cost is positively associated with growth in housing prices. In contrast, unemployment rates are 137
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Fig.8. Correlations between growth rates of housing price and traffic congestion
affect growth in housing prices in the areas. In order to verify it, we first check the correlation of congestion growth and housing prices growth using all samples, metropolitan samples, and urban area samples. As shown in Fig. 8, correlations between them exist in each sample. And then, we estimate the effects of traffic congestion growth on the housing price growth using the same equations with location and time fixedeffects. Table 3 shows the estimation results. The results show that the effects of congestion growth on housing price growth are different between urban and rural areas and between metro and non-metro areas. Particularly, while the effects are not statistically significant in rural and non-metro areas, the effects are significant in urban and metro areas, indicating that congestion growth negatively affects housing price growth in urban and metro areas, but not in rural and non-metropolitan areas. Finally, we attempt to closely look at the spatial differences in the effects of congestion growth on housing price growth because traffic congestion is generally higher in areas close to CBD. We first include an interaction term of PTI growth and distance to CBD, and then check its effects on housing price growth. As shown in Table 4, the interaction term is statistically significant and positive, indicating that the effects of congestion growth on housing price growth become smaller in the areas getting away from the CBD. We test the distance-sensitivity effects of
the congestion growth. Model (2) in Table 4 shows that there is no significant effect of congestion growth on the housing price growth in areas within 5 miles from the CBD. One possible explanation is that urban amenity-premium such as better access to jobs, shopping, restaurants, and recreational facilities are greater than the disamenities of traffic congestion in areas near CBD. However, congestion growth has a negative effect on housing price growth in areas 5~15 miles away from the CBD. There is no significant relationship between congestion growth and housing price growth in areas 15~20 miles away from the CBD. This may be because urban spatial structure is not monocentric, but polycentric because several employment subcenters exist in areas away from the CBD (McMillen, 2001; Arnott, 1998). Like areas within 5 miles from the CBD, benefits of amenities near employment subcenters are greater than the disamenities of traffic congestion. To confirm it, more specific analysis should be followed to generalize our results, but it is beyond our scope. Nevertheless, these results provide important implications that congestion relief policy should be spatially differentiated.
5. Discussion and conclusions Traffic congestion is getting worse and commuting cost is getting 138
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areas. Our findings show that while there is no significant relationship between congestion growth and housing price growth in rural and nonmetro areas, the effects of congestion growth on housing price growth in urban and metro areas are significant. Moreover, we found that the effect of congestion growth on housing price growth is not the same across the US Great Lakes Megaregion, but spatially different across areas moving away from the CBD. Specifically, the result shows that there is no significant effect of growth in traffic congestion on growth in housing prices in areas within 5 miles from the CBD, whereas the effect appears in areas 5~15 miles away from the CBD. Our findings suggest two important implications. First, the negative externalities of traffic congestion include noise, air pollution, stress, energy consumption, and wasting travel time as previous studies demonstrated, which may lead to falling residential property values. Although the direct mechanism of the relationship between traffic congestion and housing prices is not explained in this study, we verified that traffic congestion functions as a disamenity in a local neighborhood. Transportation policies that aim to relieve traffic congestion should consider that severe traffic congestion may have an adverse effect on local property values. Second, the negative effect of traffic congestion does not cause a decrease in housing values in areas close to the CBD. This is likely because the benefits of urban amenities exceed the disamenities of traffic congestion. However, as distance increases from the CBD, the benefits of urban amenities become smaller, consequently, the negative effect of congestion on property values is clear. Although more investigation with consideration of location-specific characteristics and sensitivity analysis is necessary for providing further policy implications, congestion reduction efforts can bring people to move into these areas. There are some limitations in this study. First, although our direct measures of traffic congestion provide evidence of its negative effect on property values, investigations with various measures of traffic congestion are needed before stronger conclusions can be drawn. Second, some have worried that the quality of Zillow data is not high. However, the estimates have been used in several housing-related studies and found to be reliable (Kaplan et al., 2016; Huang and Tang, 2012; Mian and Sufi, 2009). Therefore, we used housing price data from Zillow and median housing values at the zip code level. We suggest that investigations with data for individual property values would provide more information on the relationship between traffic congestion and property values. We also suggest that such approach would be helpful for investigating the relationship between traffic congestion and housing prices at the specific region. Third, in this study, we only focused on limited time periods because the available NPMRDS data are recently released. Future research using the same dataset with more time periods would provide more solid and better results. In addition, we focused only on the single-family housing prices, although there are various types of housings such as condominiums, apartments, and town homes. Future study needs to investigate how the traffic congestion differently affects housing prices of different types of housings. Finally,
Table 3 Estimation Results of the Housing Price Model (Urban vs. Rural/Metro vs. NonMetro)
PTI growth HP growth (t-1) ln(HP) (t-1) PTI (t-1) Interest rate (t-1) Mortgage rate (t-1) Construction Cost Index (t-1) Unemployment rate (t-1) Housing vacancy rate (t-1) ln(Household) (t-1) Firm vacancy rate (t-1) Constant Fixed-effects (Zipcode) Fixed-effects (Month) Number of observation R2
Metro
Non-Metro
Urban
Rural
(1)
(2)
(3)
(4)
−0.0003*** (0.0001) 0.7121*** (0.0026) −4.9195*** (0.0605) −0.0051 (0.0035) −1.2014** (0.6109) 1.1779*** (0.1983) 0.1162*** (0.0086) −0.0135*** (0.0034) −0.0194*** (0.0041) −0.0592 (0.1192) −0.0037* (0.0019) 42.5413*** (2.0900) Yes
−0.0012 (0.0014) 0.7325*** (0.0187) −6.4212*** (0.5408) −0.0013 (0.0417) −2.7523 (6.6993) 0.5355 (2.1223) 0.1084 (0.0927) −0.0242 (0.0480) 0.1503** (0.0728) −0.0230 (1.1217) −0.0078 (0.0178) 61.3830*** (20.5957) Yes
−0.0004** (0.0002) 0.7071*** (0.0033) −4.6174*** (0.0765) −0.0050 (0.0041) −0.4207 (0.7552) 1.0695*** (0.2455) 0.0886*** (0.0107) −0.0185*** (0.0043) −0.0212*** (0.0046) 0.4337** (0.1965) −0.0103*** (0.0024) 37.4938*** (2.8522) Yes
−0.0002 (0.0002) 0.7205*** (0.0040) −5.4774*** (0.0984) −0.0005 (0.0064) −2.5645** (1.0263) 1.2694*** (0.3318) 0.1564*** (0.0144) −0.0027 (0.0058) −0.0001 (0.0104) −0.3844** (0.1565) 0.0073** (0.0033) 47.6470*** (3.2900) Yes
Yes
Yes
Yes
Yes
72,994
1,558
43,838
30.714
0.6168
0.5603
0.6176
0.6127
***, ** and * indicate significance at the 0.1%, 1% and 5% levels, respectively.
higher in every major metropolitan area. In this study, we explored the negative externalities of traffic congestion focusing on the housing prices. We used a unique transportation dataset, NPMRDS, which contains information on every 5-min travel times across extensive regions to measure traffic congestion at the local level. Through the data we calculated monthly Planning Time Index as a measure of traffic congestion at the zip code level. In addition, in order to examine the relationship between the congestion growth and housing price growth, we combined the traffic congestion measures with median housing price data and a variety of socioeconomic data from various sources. Our dynamic housing price panel models accounting for locationand time-specific fixed-effects show that growing traffic congestion is negatively associated with growth in housing prices. Particularly, the effect of traffic congestion growth on housing price growth are different between urban and rural areas and between metro and non-metro Table 4 Estimation Results of the Housing Price Model (Distance from CBD)
PTI growth PTI growth × CBD Other variables Fixed-effects (Zip code) Fixed-effects (Month) Number of observation R2
All
< 5 miles
5~10 miles
10~15 miles
15~20 miles
(1)
(2)
(3)
(4)
(5)
−0.000716*** (0.000166) 0.000016*** (0.000005) Yes Yes Yes 74,552 0.6137
−0.0003 (0.0004)
−0.0006** (0.0003)
−0.0005** (0.0002)
−0.0005 (0.0004)
Yes Yes Yes 8,875 0.5642
Yes Yes Yes 14,252 0.6322
Yes Yes Yes 10,327 0.6575
Yes Yes Yes 7,720 0.6498
***, ** and * indicate significance at the 0.1%, 1% and 5% levels, respectively. 139
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we examined the relationship between traffic congestion and housing prices, but traffic-type is also important in explaining housing prices. For example, the negative effects of freight traffic can be much larger than those of auto traffic. Because NPMRDS data did not contain the information on traffic volume, we could not explore the effects of different types of traffic congestion. We suggest that combining volume data with NPMRDS would be helpful for future research.
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