transportation costs, inequities, and trade-offs

TRANSPORTATION COSTS, INEQUITIES, AND TRADEOFFS

Thomas W. Sanchez, Ph.D.

Urban Affairs and Planning Program, Virginia Tech

1021 Prince Street, Suite 200

Alexandria, VA 22314

T: (703) 706-8112, F: (703) 518-8009

E: [email protected]

Carrie Makarewicz

Center for Neighborhood Technology

2125 W. North Avenue

Chicago, IL 60647

T: (773) 269-4093, F: (773) 278-3840


Peter M. Haas, Ph.D.

Center for Neighborhood Technology

2125 W. North Avenue

Chicago, IL 60647

T: (773) 269-4034, F: (773) 278-3840


Casey J. Dawkins, Ph.D.

Urban Affairs and Planning Program, Virginia Tech

201 Architecture Annex

Blacksburg, VA 24061

T: (540) 231-2690, F: (540) 231-3367


August 2006

KEYWORDS: Transportation costs, housing, equity

mailto:[email protected]

Sanchez, Makarewicz, Haas, and Dawkins 2

TRANSPORTATION COSTS, INEQUITIES, AND TRADEOFFS

ABSTRACT

Transportation costs are frequently identified as having socially inequitable effects, especially for

low-income households who have limited financial resources. The concerns are that low-income

persons spend a disproportionately larger proportion of their total income on transportation due

to the fixed costs associated with financing automobile purchase. Furthermore, low income

persons unable to purchase an automobile often reside in locations that are not well connected by

public transit to employment concentrations. This study examines neighborhood housing and

transportation choices available to working households in 28 metropolitan regions in the U.S.

The study is unique because it analyzes household characteristics at the census travel level. We

first describe the trends in transportation costs by household income levels. We then argue that

based on microeconomic theory predicting trade-offs between housing and transportation costs

(H+T) as households choose residential locations, transportation cost burdens should not be

considered separate from housing costs. In addition, we perform a cluster analysis to show that

low income households are significantly burdened by the combination of housing and

transportation costs and that these households and their neighborhoods potentially experience

other social and economic burdens because of it.

INTRODUCTION

Transportation is the second-largest expenditure category for American families, accounting for

18.6 cents of every dollar spent annually (1). Only shelter, at 19.2 cents per dollar spent, exceeds

transportation (see Figure 1). Transportation has not always consumed such a high percentage of

the family budget, but since 1985 it has been at least 18 percent of household budgets (according

to CES surveys since 1985). But as public investments in transportation began to emphasize

roads and highways over public transit, private spending on transportation increased dramatically

(2,3,4). This has resulted in shifting household spending more toward private transportation, due

to the lack of public transportation options. The large initial downpayment cost associated with

car purchase combined with the added financing and maintenance costs generally increased the

relative transportation cost burden for low income families choosing to rely on auto-based forms

of transportation. Families living in sprawling metropolitan areas, with little public transportation

and destinations so spread out as to be unreachable by foot or bicycle, must spend even more on

transportation, in some cases spending more than they do on rent or mortgages.

As families are forced to spend thousands of dollars annually on owning and operating

cars and trucks (which are rapidly depreciating assets), they have less money to invest in home

ownership, hindering wealth creation and the ability to enjoy other benefits of home ownership.

The poorest Americans are especially hard hit, spending a significant portion of their take-home

pay on transportation costs, an expense that may require those families to dip into savings,

borrow from relatives, and look for nontraditional sources of income to make ends meet.


FIGURE 1 How Families Spend Each Dollar.

Source: Surface Transportation Policy Project (2003).

Household Transportation Costs

There are some conflicting perspectives on the amounts and proportions of household income

expended on transportation by different income groups (5,6,1). Despite the variation in

estimates, it is undeniable that transportation costs are high. As previously mentioned,

transportation costs rank second only to housing costs in terms of household expenditures for

Americans. Data from the Consumer Expenditure Survey (CES) show that low-income

households devote a greater proportion of their incomes to transportation-related expenses,

regardless of whether they use public transportation or own automobiles, but households using

public transportation in place of, or more heavily than, a private vehicle do have much lower

transportation costs. A Surface Transportation Policy Project (STPP) report from 2001 found

that those in the lowest income quintile spent 36 percent of their take-home pay on

transportation, compared with those in the highest income quintile, who spent only 14 percent on

transportation. Figure 2 shows the level of household spending for transportation both in terms

of proportion to income and in proportion to total household expenditures. Transportation costs

are a higher percentage of income than expenditures for the bottom two income quintiles because

their expenditures are higher than their incomes whereas the reverse is true for the top two

quintiles. The third quintile households spend about equal to their incomes.

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FIGURE 2 Household Transportation Spending, by Income Group.

Source: U.S. Consumer Expenditure Survey (2001).

Using CES data, Rice (2004) found that the poorest households in California spend

smaller shares of their total household budgets on transportation compared to low-income or

high-income households. Rice acknowledged that these results can be interpreted in different

ways and that lower expenditures (as a share of income) on transportation cannot be equated

with different levels of transportation affordability. As poor or low-income households spend

less on transportation, other factors such as time costs, quality of service, and consumer trade-

offs fail to be accounted for. For instance, only 45 percent of poor households in the survey were

vehicle users, and therefore the lower average expenditures for this income group are influenced

by the 55 percent of poor households that are not vehicle users and therefore do not bear the cost

of the most expensive item in total transportation costs—vehicle purchases. Therefore, as Rice

notes, it is not necessarily that poor households are able to obtain the same type of transportation,

e.g. vehicle ownership and use, as other households for lower costs; rather many spend less by

consuming a different bundle of transportation items, in this case more public transit use than

auto use. In addition, the figures on average household expenditures on transportation do not

control for household size or life cycle (age of household members and presence of children), or

the number of workers commuting on a regular basis. The analysis also showed that separating

out vehicle users from transit users for each income level, and breaking each income level into

terciles by expenditures, showed similar expenditure shares when each income group and tercile

within each income are compared by similar mode choice, e.g. vehicle users separate from public

transit users. This seems to indicate that transportation costs are not just a factor of income, but

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of mode choice as well as the extent of use within each mode (5). Rice also found that the

patterns of costs for California were similar to those of the United States as a whole.

Drawing the same conclusion as Rice, while analyzing the full sample of CES data,

Blumenberg (2003) concluded that, despite the findings of the STPP report, lower-income

households do not suffer a disproportionate impact from transportation costs compared to higher-

income households. Blumenberg reported that there is slight variation in transportation costs by

income quintile, with the lowest spending 17 percent of their household income, followed by 19

percent, 21 percent, 20 percent, and 18 percent, respectively. These compare to Rice’s 11

percent, 14 percent, and 16 percent reported for poor, low-income, and high-income households

(terciles).

Both Rice and Blumenberg briefly discuss the differences between transportation costs as

a proportion of household income and total household expenditures. When comparing results

from these two methods depicting transportation cost burdens, an important factor to consider is

the inelasticity of transportation costs. With the total household expenditures as the denominator,

the proportions of expenditures devoted to transportation remains relatively constant across

income groups because transportation is an inelastic consumption item (6). Despite households

devoting about the same proportion of their total expenditures to transportation, the lowest

income quintile travels about one-third as much as households in the highest income quintile. In

addition to total distance, total travel time must be considered. Data from the 2001 National

Household Travel Survey (NHTS) shows that households in the lowest income quintile average

nearly four minutes per mile traveled compared to three minutes per mile for households in the

highest income quintile—much of this being explained by high rates of public transit ridership

by low-income persons (7). While it may be true that low-income households pay similar

proportions of their overall budgets for transportation compared to high-income households, they

also consume far less in terms of overall travel distance resulting in a higher per-unit time cost.

It can be argued that higher time costs are a useful indicator of poor service quality or

convenience.

On the other hand, when comparing transportation expenditures as a proportion of

income, low-income households pay significantly higher levels than do higher-income

households (1). Using this approach also highlights the significant burden that lower-income

households realize and difficult trade-offs they make to satisfy their travel needs. Using

household income as the denominator for estimating the financial burden of transportation costs

(as opposed to total household expenditures, as done by Rice and Blumenberg) better illustrates

the impact of high travel costs on lower income households. As shown in Figure 3, according to

the CES, overall annual expenditures by the lowest income groups exceed their annual incomes,

potentially contributing to higher levels of debt and financial liabilities. In 2001, annual

household expenditures were 225 percent of household income for the lowest-income group but

only 64 percent for the highest income group. These differences suggest that low income

households finance much of their current expenditures from debt sources, rather than from

current income. While some of the discrepancy between income and expenditure-based

calculations may be attributable to underreporting of income or debt, it is not likely that

transportation costs are being systematically over-reported as a percent of income. In many

ways, annual income is a more effective denominator, because it represents the household’s

current revenues available for expenditures absent debt-financing. Therefore, it gives a better

picture of what types of consumption choices families can feasibly sustain, given their current

budget constraint.

https://www.researchgate.net/publication/237247094_The_Long_Journey_to_Work_A_Federal_Transportation_Policy?el=1_x_8&enrichId=rgreq-b13a8275-5728-42a8-8837-93f97e146b42&enrichSource=Y292ZXJQYWdlOzI3ODAxNzM2NDtBUzoyMzg4MTgwOTE1OTc4MjVAMTQzMzk1MDA3ODE5OA==


FIGURE 3 Household Expenditures as a Percentage of After-Tax Income.

Source: U.S. Consumer Expenditure Survey (2001).

Another measure of the impact of transportation costs on low-income households is the

rate of increase in transportation expenditures. Between 1993 and 2003, households in the lowest

income quintile saw the amount of their income spent on transportation increase by over 4

percent. While not a dramatic increase, this was the highest rate of change among household

income quintiles. By comparison, households in the highest income quintile spent about 11

percent less on transportation in 2003 than they did 10 years earlier. These trends suggest not

only that low-income families are spending more of their incomes on transportation but also that

transportation costs are increasing at a faster rate for them. The increasing burden of

transportation costs compounds the financial challenges that lower-income households face.

Increasing costs and growing debt problems further reduce the lower-income population’s ability

to pay for other needs, further removing the lower-income population from the possibility of

home ownership and wealth accumulation. Other evidence suggests that the debt incurred by

families related to car ownership makes buying a home more difficult, which is the primary

means of wealth accumulation among low- and middle-income households.

Analyzing household transportation costs along with travel activities helps to illustrate

travel costs versus benefits. Thus far, most previous analyses have focused on either one or the

other—and not the cost per unit of transport consumed. Highly mobile persons or households

are those paying relatively lower amounts per unit of travel, but higher total absolute costs). The

most significant costs of auto ownership and operation are monthly finance payments, insurance,

and in some cases state vehicle registration and taxes. However, the most visible costs are for

fuel, parking, maintenance, and repair. Despite the fact that these costs are much higher per unit

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traveled compared to public transit, auto owners are not bound by service schedule restrictions or

physical route coverage limitations—transport mobility is essentially unlimited.

Cluster Analysis: Comparing costs to other household and neighborhood characteristics

A more complete picture of the transportation cost burden facing low income households must

include an analysis of both transportation and housing expenditures and the range of the

combined costs according to location. It is well-known that housing prices reflect both the

inherent characteristics of housing units plus the capitalized value of neighborhood amenities,

including accessibility to employment. In general, housing located closer to employment centers

will be more expensive, per unit of housing services, than housing located in areas that are not

accessible to employment or other amenities. Given the monocentric urban model’s prediction

that the income elasticity of housing exceeds the income elasticity of leisure time, higher income

households will tend to live in more inaccessible locations to consume larger homes. This

implies that while higher income households may spend a smaller portion of the total budget on

transportation, these lower expenditures may be exactly offset with increased housing

expenditures. The reverse applies to low income households, who likely spend a larger

proportion of the total budget on transportation, in some locations, and less on housing. Thus, a

complete picture of the total spatial “cost” of residing in a given location must take these

tradeoffs into account.

In order to characterize the impacts of housing and transportation costs on lower and

moderate income households, we analyze the range of factors determining a household’s

transportation costs and how they compare and combine with their housing costs according to the

location in the region and the characteristics of that location. We do this separately for each of

six income classifications based on the income breaks in the Census. These incomes range from

less than $20,000 to less than $250,000.

To do this analysis, we first needed measures of income by census tract, including how

many households of each income are in a census tract, the percentage of income spent on

housing by each income group within a census tract, and the percentage of income spent on

transportation by the same income groups within a census tract. To compare these expenditures

by income and neighborhood to location characteristics, we developed measures to represent

total household transportation costs, accessibility to all jobs within a region (job accessibility),

distance to major employment centers, and workers commute distance, commute time, and

commute speed. With this complete set of measures we were able to look for the associations

between costs, expenditures, incomes, and locations. The following briefly outlines the approach

and sources for each of these measures.

Neighborhood Data

This analysis uses the following seven key measures:

Weighted Average Household Income by Census tract in 2000 for the entire tract and for

each of six income categories within the tract. (Census 2000)

Housing Costs by Tenure as a percentage of household income in 2000 (H) (Census

2000)

Total Household Transportation Costs as a percentage of household income in 2000 (T)

(Transportation Cost Model) (8).

Housing + Transportation cost burden (H+T)


Job Locations, Concentrations and Accessibility to Jobs- three uses of the Census

Transportation Planning Package allow us to create three measures that represent: 1) the

location of each job in the region; 2) the accessibility to all jobs in the region from each

census tract; and 3) employment centers, which we define as relatively dense clusters of

5,000 or more jobs in contiguous tracts of more than 7 jobs per acre (CTPP 2000)

Worker Commuting Characteristics: the estimated commute distance, and speed, and the

reported commute time for each worker in each census tract by transportation mode. The

commute distance and speed are estimated using the reported commute times in the

census paired with the worker origin and destination census tracts. Using GIS, we

calculated the distance “as the crow flies” using the origin and destination census tracts

from the 2000 Census Transportation Planning Package, and then used time and distance

to calculate commute speed. (CTPP 2000)

Household socioeconomic characteristics such as educational attainment levels,

unemployment rates, and household size (Census 2000)

Availability of Affordable housing (CHAS 2000)

Household Income

Using Census 2000 household income breakout for each tract we summed the number of

households within the following six annual income ranges:

Less than $20,000

$20,000 to less than $35,000

$35,000 to less than $50,000

$50,000 to less than $75,000

$75,000 to less than $100,000

$100,000 to less than $250,000

We chose these categories because they represent, roughly, quintiles of national

household incomes—i.e., each category contains nearly 20 percent of U.S. households. We did

not include households above $250,000 since they are less than 3 percent of the population and

the high incomes in this group would have greatly skewed the highest category. And as the

average median household income is approximately $46,000 in these regions, the first three

categories roughly match the 30-50, 80, and 100 percent of area median income (AMI) measures

that are often used in qualifying households for affordable housing. This makes these income

categories useful for policy makers that use AMI to operate programs based on incomes. While

they are not exactly the same as AMI, we used a small range within each category, $15,000 to

$20,000, and several categories, to help make the comparison between these ranges and the

percentage of AMI in each region.

However, in order to use the transportation cost model, which is based on a specific

income, we could not use a range. Therefore, for each census tract, we used the Census PUMS 5-

percent data from the PUMA that encompasses each tract to determine the weighted average

income of households in each income category. For instance, to determine what actual income to

use in the income category range of “Less than $20,000”, we used the PUMS data which

provides a count of households at a specific income level. By querying the PUMS data for

households by income restricted to just households earning an income of $0 to $20,000, and to

households not living in group quarters, we could identify that the weighted average income in

this category and in that PUMA was actually, $11,368 for all households, $9,837 for renters, and


$10,385 for owners. We did this query for each PUMA and each income category in each of 28

selected metro areas (the 28 metros sampled by the CES). We then applied the results to each

income category in each census tract in the 28 metro areas. While this method is not exact since

PUMAs are 100,000 persons or more and census tracts are typically 3,000 persons, the error is

contained within each income category and is only used to obtain a weighted average in place of

a range. The other alternative would have been to take a simple average of the $0 to $20,000

range, e.g. $10,000, but this would be even less precise (see Table 1).

TABLE 1 Weighted Average Household Income in each Income Bracket

Census

Income

Weighted

Average

Renters

Weighted

Average

Owners

Weighted

Average

All HHS

Renter

HHS

Owner

HHS All HHS

% of

HHS

<$20,000 $9,837 $11,368 $10,385 971,172 3,190,910 5,691,595 12%

$20,000 to

<35,000 $26,941 $27,516 $27,221 1,144,763 3,956,933 7,080,693 15%

$35,000 to

<50,000 $41,506 $42,175 $41,899 2,834,351 4,321,022 7,369,761 16%

$50,000 to

<$75,000 $60,211 $61,599 $61,189 3,048,739 4,546,832 8,138,869 17%

$75,000 to

<$99,000 $85,138 $86,059 $85,875 4,181,936 6,109,521 8,932,939 19%

$100,000 to

<$250,000 $132,773 $138,051 $137,291 5,742,029 6,713,796 9,548,147 20%

Total Households 17,922,990 28,839,014 46,762,004 100%

No. of 5% PUMAs 963 941

Housing Costs as a percent of income

In a similar manner to the household income measure from the census, we developed the average

housing cost as a percent of income by tenure for the same six income categories, e.g. rather than

using 30-35 percent of income. This allows us to examine the housing cost burden as a function

of income for each income as well as the tract by using the weighted average of the housing costs

for all households in the tract.

Transportation Costs as a percent of income

The transportation cost data was predicted with a unique model developed by Center for

Neighborhood Technology and Center for Transit Oriented Development that uses Census,

transit system, National Household Travel Survey, and other data sources to estimate a

household’s auto use, auto ownership, and transit use at the census tract level for a particular

household size and income. This model used the specific income categories described above for

each tract. The monthly transportation cost derived from the model is then applied as a percent of

each weighted average income for each income category in each census tract. This is to report on

transportation costs by income for each neighborhood. To characterize the entire neighborhood


in terms of transportation costs, we calculated a weighted average of the percentage of income of

the six income categories (8).

Housing + Transportation Cost Burden

To further compare and quantify housing and transportation variations across and within regions

we created a neighborhood typology that represents the proportion of income spent on housing

and transportation by the weighted average household income in that neighborhood using the

income, housing, and transportation measures described above. This typology is based on

housing costs plus transportation costs and results in one of four combinations; high or low

expenditures on housing as a percentage of income plus high or low expenditures on

transportation as a percentage of income. The four categories are illustrated in Figure 4.

Note the matrix does not have values on either the vertical or horizontal axis. This is

because the average percent of income spent on H and T is relative to each region. What

constitutes above average in one region might not be above average in another. We used the

regional average expenditure on H and T as the best measure for what a typical household might

spend on housing and transportation rather than using a fixed percentage such as 30 percent of

income on housing. While 30 percent on housing is an industry standard for lending and public

subsidies, it is not the typical amount spent by most households. In the U.S., the average

expenditure is closer to 21 percent on housing. Therefore, we used the average of all households

as a fair measure of whether households were taking on a housing and/or transportation burden.

Using the average of all households as the threshold was also necessary since there is no

analogous standard percentage of income recommended for transportation.

FIGURE 4 Housing + Transportation Neighborhood Typology.


RESULTS

A cluster analysis was used to determine whether the amounts households were spending on

housing and transportation (as a percent of income), have a relationship with other characteristics

in those tracts, including incomes, educational attainment (percent with a bachelor degree),

unemployment rates, household density, household size, vehicle ownership, distance to work,

tenure, and the daily number of household trips. Using these characteristics, the tracts clustered

into four categories, with income as a significant discriminate variable (see Table 2). The clusters

range from 30 percent of households in tracts with an average (weighted) income of $35,007 to

10 percent of households in tracts with an average (weighted) income of $100,128. The

clustering also reveals a spatial dimension through the housing unit density variable ranging

from urban for the lowest income category (2,700 housing units per square mile) through

suburban for the upper-income category (600 units per square mile). The spatial dimension is

further analyzed later.

TABLE 2 Neighborhoods Clustered by Socioeconomic and Place Characteristics

Cluster

Variables in Cluster Analysis 1 2 3 4

T as a % of Income (all households) 20% 16% 25% 13%

H as a % of income (all households) 28% 26% 34% 25%

H + T as a % of income (all households) 48% 42% 58% 38%

% unemployed 5% 4% 12% 3%

% bachelor degree 16% 24% 8% 33%

Avg. Distance to Work by Auto 9.6 10.5 7.7 10.7

Avg. Number of vehicles per household 1.7 2.0 1.2 2.2

Avg. Household Size 2.7 2.8 2.9 2.9

Housing Unit Density (Units per square mile) 1,212 812 2,697 602

Estimated Daily Trips per Household 10.2 10.6 10.1 11.0

Tenure (% Owner) 63% 77% 39% 88%

Weighted Average Income $54,490 $74,818 $35,007 $100,128

Number of Neighborhoods (tracts) 10,252 7,200 8,815 2,967 29,234

% of Neighborhoods (tracts) 35% 25% 30% 10% 100%

Across these neighborhood clusters, other characteristics besides income are also distinct

from each other and are reflective of income. The neighborhoods with the lowest median

incomes have the highest average unemployment rate (12 percent) and the lowest percentage of

households with college degrees (8 percent). Transportation-related characteristics also varied

across neighborhoods, with the low income tracts owning 1.2 vehicles per household on average

compared with 1.7 to 2.2 in the other three clusters, making the fewest trips per day (10.1), and

the having the shortest distance to work. The two high income clusters make the most daily

household trips, have the highest vehicle ownership, and the longest distances to work. Since

household sizes do not vary significantly among clusters, 2.7 to 2.9, and are the same for the

highest and lowest income clusters, transportation costs as a percentage of income are related to

density, number of daily trips, and distance to work, as well as income and household size.


Simply comparing the income of each cluster with the percentage of income spent on

H+T makes it appear that expenditures—as a share of income—are just a matter of income. As

incomes go up, expenditures go down. While this is true, it is not the complete story, especially

since the average in a cluster represents at least 2,967 neighborhoods and each of those

neighborhoods could vary from the average H+T expenditure, e.g. a household earning $20,000

to $35,000 could have combined expenditures ranging from 66 percent in neighborhoods we

classified as “Above Avg. H”, which are those with higher than average housing costs as a share

of income, but lower than average transportation costs as a share of income, to 71 percent in

neighborhoods classified as “Below Avg. H&T”, which are places with predominantly higher

income households paying below average percentages of income for both their housing and

transportation. Matching the demographic neighborhood classification from the cluster analysis

to our H+T neighborhood classification—which breaks neighborhoods into four categories based

on the combination of high or low housing expenditures and high or low transportation

expenditures as a share of the incomes in the neighborhoods, we get a sense of whether all

neighborhoods of a particular cluster have the same H+T expenditures, and whether all

neighborhoods of a particular H+T expenditure share similar demographic characteristics (see

Table 3).

We found that the low income cluster neighborhoods (Cluster 3), in which the average

income was $35,007, are primarily Above Average H&T neighborhoods which means these H+T

neighborhoods are primarily places with high unemployment rates (12 percent), low educational

attainment (8 percent with a college degree), and low rates of home ownership (39 percent).

Above Avg. T neighborhoods primarily consist of the moderate and high income clusters; those

with incomes of $54,490 and $74,818 make up 85 percent of this H+T neighborhood type.

Therefore, these neighborhoods have lower unemployment rates, 4-5 percent, higher rates of

college degrees, 16-24 percent, and higher rates of home ownership, 63 percent to 77 percent.

Below Average H&T neighborhoods are almost exclusively the moderate and high income

demographic cluster neighborhoods with only 2 percent of the low income cluster neighborhoods

falling into this H+T type.

The lower half of Table 3, which shows the distribution of the demographic clusters

across the H+T types shows the segregation by income in neighborhood types for low and very

high incomes. While the moderate income cluster neighborhoods ($54,490) are nearly equally

distributed across the four H+T types, 88 percent of the low income cluster neighborhoods are in

Above Avg. H or Above Avg. H+T expenditure neighborhoods, nearly the converse of the high

income cluster neighborhoods ($74,818) of which 87 percent fall into the other two H+T

neighborhood types. The very high income cluster ($100,128) neighborhoods are almost

exclusively, 93 percent, in the Below Avg. H+T neighborhoods.


TABLE 3 Comparison of Neighborhoods and Households by Clusters and H+T

Neighborhood Type

Median Incomes of Clusters

Below Avg. H&T % in

Neighborhood

Above Avg. H % in

Neighborhood

Above Avg. H&T % in

Neighborhood

Above Avg. T % in

Neighborhood

$54,490 25% 48% 25% 59%

$74,818 47% 15% 3% 26%

$35,007 2% 35% 72% 14%

$100,128 27% 3% 0% 1%

TOTAL in H+T Type 100.0% 100.0% 100.0% 100.0%

Median Incomes of Clusters

Below Avg. H&T

% of Cluster Above Avg. H % of Cluster

Above Avg. H&T

% of Cluster Above Avg. T % of Cluster

$54,490 25% 22% 20% 33%

$74,818 67% 10% 3% 20%

$35,007 2% 22% 66% 10%

$100,128 93% 4% 0% 3%

How the income clusters are distributed across the H+T types matters because it indicates

the housing/transportation trade-off they experience. The moderate income cluster, $54,490, for

instance increase their burdens by 4 percent to 5 percent in the Below Avg. H&T and Above

Avg. T neighborhoods compared to their costs in the other two neighborhoods.

CONCLUSIONS

Studies to date on household transportation costs have been limited by the inability to study

household transportation costs at the neighborhood level. While the data at the MSA and national

levels show that transportation costs vary across incomes and within incomes, in particular if

households use transit instead of vehicles for transportation, without neighborhood location, it is

not possible to determine whether households that rely on transit do so because of costs, location,

or preference. The study by Rice (2004) and the Driven to Spend series by STPP indicate that

travel mode is a key determinant in household transportation costs; households with heavy transit

use and light vehicle use have much lower transportation costs. However, using CES data, it is

not possible to identify the locations where heavy transit use is possible without sacrificing

mobility. Additionally, without larger sample sizes for income groups it is difficult to tell how

many households in which regions can actually rely more heavily on transit and why.

By modeling transportation costs at the neighborhood level and combining these costs

with housing costs by income we are able to show that transportation costs are not only a factor

of income and household size or preference, but that there are strong relationships with

locational characteristics. Doing the study for 29,000 tracts in 28 metros and by six income

categories provides a large enough sample to show that locational characteristics do have similar

impacts on transportation costs, regardless of household income.

In 28 metros, we found 16 percent of households at all income levels, (households living

in the neighborhoods classified as “Above Avg. H” neighborhoods) consistently spend less on


transportation as a percentage of income than all other households in these regions. These

households live in neighborhoods with higher densities, closer proximity to jobs and

employment centers, higher prevalence of public transit—on average, 27 percent of workers

commute by public transit, the greatest mix of housing types in terms of size, price, and tenure,

and the greatest diversity of incomes. An additional 26 percent of households in these metros,

have slightly higher transportation costs than these households, but still have lower

transportation costs, at all income levels, than the other 58 percent of households. These

households also live in neighborhoods with greater densities, a more diverse housing stock, and

greater availability of public transit. However, the higher transportation expenditures in these

neighborhoods are associated with a lack of nearby jobs, less public transit, fewer neighborhood

amenities, and lower incomes.

Because of the lower transportation costs in these areas, combined housing and

transportation costs are also lower in these neighborhoods. In the Above Avg. H neighborhoods,

households trade-off much lower transportation costs for higher housing costs. In Above Avg.

H&T neighborhoods, households combine low to moderate priced housing with low to moderate

transportation costs for lower combined H+T cost than in other parts of the region in which

either both housing and transportation costs are higher or transportation costs are especially high.

Yet, for households earning less than $35,000 the combined costs are still greater than 60

percent of incomes in the two lower transportation cost neighborhoods and could be as high as

71 percent of incomes in the other two neighborhood types. For households earning $35,000 to

$50,000, they pay at least 50 percent of income for the two costs in all neighborhoods.

In addition, despite offering relatively lower combined housing and transportation costs

for lower income households, the two lower transportation cost neighborhoods also have higher

rates of unemployment and poverty and lower educational attainment levels than the two other

neighborhood types. The relatively lower housing and transportation costs are still not leaving

enough income for education, home ownership, and other wealth creating assets for lower

income households, although they are leaving more income than the other two neighborhood

types.

Looking at just the two lower transportation cost neighborhoods illustrates the

importance of thinking about housing and transportation combined. In the Above Avg. H

neighborhoods, households are benefiting from low transportation costs, but these same low

costs are reflective of the accessibility of the areas and therefore are contributing to the higher

demand for housing which results in higher housing costs. Lower income households living in

these neighborhoods need more affordable housing opportunities. In the Above Avg. H&T

neighborhoods, housing as a percent of income is 2-3 percent less, but the slightly higher

transportation costs, which are 1-8 percent more, offset the housing cost savings. Improving the

public transportation systems, adding jobs, and reducing the need for high vehicle use and

ownership in these neighborhoods could help lower income households benefit from the lower

housing costs. Improving transportation costs in these neighborhoods is easier than improving

them in areas with especially high transportation costs.

This analysis shows that location characteristics can be used to explain much of the

variation in expenditures and multiple elements of location influence the range of variation with

evidence from neighborhoods in 28 metros. Transportation costs are consistently lower across

household income groups in places with characteristics that allow households to spend less on

private transportation means and take advantage of the lower costs associated with greater use of

public transportation, walking, and biking. The location and cost information can be used to


shape targeted investment policies and subsidy programs for affordable housing, transportation,

and jobs access and to inform economic development incentives for employers and job creation.

ACKNOWLEDGEMENTS

The authors would like to acknowledge the Brookings Institution Urban Markets Initiative for

funding the creation of the T-cost model and the National Housing Conference’s Center for Housing

Policy for funding the 28 metro study, which allowed us to create the T-cost for 28 metros. In

addition, Albert Benedict from the Center for Neighborhood Technology provided excellent GIS

and analytical support.

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transportation costs, inequities, and trade-offs

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