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Using these two equations, and estimates for annual
fixed costs of car ownership and variable costs of an additional mile of
driving, allow the calculation of a matrix of average annual household auto
expense as a function of density and the transit accessibility index. These
are presented in Table 8 of the text. Application of the Results to Mortgage Qualification
The predicted average annual household transportation
cost savings for a particular dwelling unit could be calculated as follows:
Thus, the primary objective of this study has been
achieved: providing a first cut at a formula for quantifying the value of
location efficiency that can be used in the context of energy efficient
mortgages. Several areas for additional research present themselves as a
consequence of the results derived in this study. Discussion
This study's results are also significant because
previous studies focused only on different neighborhoods in a single metropolitan
area, whereas this study's results were derived from four different metropolitan
areas and one rural county throughout California. After density, the only other variable that produced
statistically significant explanatory power was the transit accessibility
index. While all of the other variables, including household income and
household size, were statistically significant predictors of observed driving
behavior when considered individually, they failed to be significant when
the effects of density and transit were considered first. This result may
be due to limitations in the sample size. They may also be due to the correlation
between the other neighborhood characteristics and the two with the most
explanatory power: density and transit accessibility. The failure of the income variable to be statistically significant is an important departure from previous studies, and suggests further research. It is well established that higher income families drive more, everything else being equal. But this study finds that when everything else isn't equal -- when the characteristics of the neighborhoods in which people live are taken into account -- income fails to provide statistically significant results. This may be in part because none of the neighborhoods studied was extremely poor: one can hypothesize that the inclusion of extreme poverty neighborhoods in the sample would demonstrate an income effect. But for the range of neighborhoods covered by this study, and to which the results are likely to be applied, income variations do not change the predictions of driving. This is a valuable result for application to energy
efficient mortgages, because it suggests the possibility of evaluating transportation
savings without regard to the demographic characteristics of a neighborhood. Summary Variations in automobile usage per household and
in personal transportation costs between communities can be quantified with
a good deal of accuracy using two simple equations. These can provide the
basis for calculating a first approximation to average transportation cost
savings. It appears to be feasible to use this to develop repeatable estimates
of transportation cost savings for use in energy efficient mortgages. TABLE OF CONTENTS
2. Survey of Previous Studies
3. The Communities and Their Characteristics
4. The Results of the Analysis
5. Predicting Annual Auto Costs Appendix: Detailed Description of the Communities
References 1. INTRODUCTION
Driving costs vary systematically between neighborhoods
within urban areas. Denser, central areas with good transit and nearby employers
and shopping require less driving than sprawling bedroom suburbs with little
transit and no nearby shopping or jobs. John Holtzclaw estimated an average
annual auto cost per household in dense northeast San Francisco of $4,200
(0.6 autos per household) compared to $17,800 (2.3 autos per household)
in suburban Danville-San Ramon, for an annual savings of $13,600, or
$1,130 per month, for San Francisco households (Holtzclaw, 1991). These
costs are consistent with the Moffet and Miller national averages when Californians'
higher driving rates are considered and central city and suburban costs
are averaged. It makes sense to allow families incurring lower
transportation costs to apply those savings to mortgage payments. If a family
choosing to buy a house and live in the denser area with good transit could
apply a $500 per month auto and transit savings to their mortgage payments,
that family should be able to qualify for a $50,000 more expensive house
at the same combined monthly housing and transportation payments. Implementing
such mortgage criteria requires predicting the variation in average local
transportation costs, and how they are affected by such measurable neighborhood
variables as density, the availability of nearby transit and neighborhood
shopping and the safety and appeal of the neighborhood to pedestrians and
bicyclists. The calculation of transportation costs includes private autos
and light trucks, and local public transit costs. Auto use is encouraged by the auto's versatility,
speed (as long as congestion is avoided and parking is ample), privacy,
comfort and apparent safety. Auto dependence is fostered by subsidies which
decrease the marginal or per mile cost of driving; economic policies and
social attitudes which encourage moving from denser, convenient central
areas to sprawling suburbs; urban renewal and freeway construction; low
density settlement patterns and isolation of residential areas from shopping,
services and jobs; poor public transit service; and pedestrian- and bicycle-unfriendly
residential and shopping areas. The marginal cost of driving (additional cost per
mile) comprises only 14 percent of total direct automobile costs in the
first year of ownership, and averages 38 percent (less than 13 cents per
mile) over 12 years of ownership according to the Federal Highway Administration
(1991). The marginal costs include the fuel, some parking, tolls, maintenance
and tires; ownership costs include annual registration and taxes, depreciation,
finance costs, insurance and some parking fees. The low marginal costs encourage driving by decreasing
the apparent cost of driving, and, in fact, increased mileage, while raising
the total costs, decreases the cost per mile. Many of the hidden
subsidies to driving are dependent on the miles driven and would discourage
additional driving if their costs were levied directly on auto use, more
than doubling the cost per mile. These subsidies include road repair, policing
and motorist protection, parking, accidents, noise, vibration damage, pollution
damage, global warming, petroleum subsidies, policing the petroleum supply
line, and congestion. Since before World War II, federal housing policies,
including those of the Federal Housing Administration (established by the
National Housing Act in 1934), Veterans Home Administration and the Federal
National Mortgage Association (FNMA), have driven up single-use suburban
sprawl by "redeveloping" urban centers, officially or unofficially
redlining urban centers, strongly biasing ownership towards single-family
homes, encouraging or mandating low density developments, and prohibiting
commercial activities in residential areas (Glazer, 1973; Stone, 1973; and
National Commission on Urban Problems, 1968 & 1973). Redlining impedes
upkeep and rehabilitation of central areas. Urban renewal removes more affordable housing,
usually from the urban center near jobs, neighborhood shopping and transit.
Construction of freeways through urban centers and into rural areas removes
block-wide swaths homes and businesses from the urban center and opens up
rural areas for development. Many suburban areas have development codes and
practices which inhibit walking, bicycling and public transit. These include
the absence of sidewalks, bus shelters and inviting street furniture, or
where buildings are set back behind parking or landscaping and lack attractive
fronts. Winding and dead-end streets, cul de sacs, and such pedestrian
barriers as freeways, drainage ditches and parking in front of businesses
increase trip distances. High traffic speeds and the absence of sidewalks,
stop signs and stoplights decrease pedestrian safety and discourage walking
or biking. Since most public transit users get to transit by walking or
bicycling, conditions which discourage walking or bicycling cut transit
patronage.
This study tests the proposition that these community
characteristics decrease average trip lengths, facilitate transit use and
encourage walking and bicycling, thereby decreasing auto use and residents'
transportation costs. The neighborhood shopping measure is also a surrogate
for job, entertainment, and recreation trips, and visits to relatives and
friends. This study intends to measure the independent impacts
of each of these community characteristics on total household driving. If
that can be accomplished, the average household auto costs could be calculated
for a housing location using these variables. The calculation might take the following form: a) Use census tract population density or net
household density to look up the average annual household auto expenses
in a Density-Auto Expense Table. Census tract density could easily be mapped
on a land use map. b) Calculate the daily average number of standardized
buses within 1/4 mile walking distance and the standardized rail cars or
ferries within 1/2 mile of the residence. Look up the resulting average
annual additional savings or costs in a Transit Access- Auto Expense Table,
and subtract from the auto costs derived above. If the equation relating density and transit to
VMT is more complicated than addition, a table of auto costs at each density
and transit accessibility could be used. c) If five restaurants, food stores and drugstores
are within 1/4 mile walking distance subtract the shopping accessibility
bonus from auto costs derived above. d) Look up the average annual household auto savings
for pedestrian access in the Pedestrian Access-Auto Expense Table and subtract
them from the auto costs derived above. These savings or costs are based
upon the pedestrian grid completeness, hilliness, sidewalks, building setbacks
and traffic controls in the census tract. Census tract pedestrian accessibility
could be mapped on a land use map. e) Add up the average household transit costs
for the city and add to the auto costs derived above. f) Subtract the total annual household transportation
cost obtained in a-e from the suburban average to obtain the annual household
savings; divide by 12 months; and add to the standard PITI (principal,
interest, taxes and insurance) mortgage qualification formula. The average transportation costs associated with
a specific location would be added to the house's mortgage, tax and insurance
costs to get the total transportation and mortgage expenses for a house.
This provides the basis for allowing a family moving into an area with low
auto dependence to qualify for larger housing mortgages based on its transportation
cost savings. Further, this study intends to define each of these community
characteristics in such a way that it can be used to guide land use and
transportation planners in designing urban areas to reduce dependence on
cars.
Consider density. Lower household densities result
in fewer households nearby, decreasing the average number of family members
and friends within walking, bicycling or transit distance. Lower density
neighborhoods are harder to serve by transit and in American cities are
usually located farther from job centers. So, residential density is also
a surrogate for nearness and for public transit access to concentrations
of jobs and shopping. One task is disentangling transit access and local
shopping from density. Some high density areas are built right next to low
density areas. High density apartments might be surrounded by extensive
low density housing, giving a low density average. To accommodate this variability,
this study looks at communities of 20,000 people or more in order to reflect
the larger community environment. At this scale, density and transit service
should reflect accessibility to jobs (comprising one-fifth of total trips
and one-third of total auto mileage) and to shopping, services and friends. However, some architects are designing transit
oriented developments (TOD), which are medium density neighborhoods with
shopping and pedestrian amenities, outside the developed metropolitan area.
How much will the higher density of these areas and perhaps better transit
service compensate for the area's isolation from job and perhaps shopping
centers? A neighborhood in the San Francisco area and one in the Los Angeles
area were selected to test this. There is one important caveat. The high density
evaluated here does not fit the media image of monolithic run-down high-rise
projects. The highest density area studied, Nob- Russian-Telegraph Hills-Chinatown-North
Beach-Fisherman's Wharf in San Francisco, achieved nearly 120 units per
net acre primarily with 3 to 6 story apartment houses. The area is popular
and well kept up. Its high housing prices show high demand. Achieving high density need not mean massive redevelopment.
It can be achieved by infilling with 3 to 4 story apartments or condos,
over restaurants and markets, constructed on lots left empty, on parking
lots, and on other underused land along transit corridors and around transit
stations. The pedestrian and commercial activities of these higher density
areas enhance their neighborliness, excitement and livability. As these
core areas grow and densify, new transit corridors between them could provide
additional avenues for infill growth. Neighborhoods farther from these transit
corridors remain at low density. This is the traditional urban growth pattern. 2. SURVEY OF PREVIOUS STUDIES
The study used a novel source of vehicle miles
traveled (VMT) data: odometer readings taken during California's mandatory
biennial auto emissions (smog check) inspections. This data captured all
auto travel, including vacation, so should have been an accurate indication
of total vehicular use and how much total VMT could be saved by increasing
the density of residential areas. And these measurements eliminate concerns
about reporting accuracy, trip length estimation accuracy, data completeness,
response rate adequacy, response bias and inhomogeneous analytical areas. Five communities within the San Francisco region were selected to achieve a wide range of density, including one of the densest communities west of Manhattan.
Comparing the extremes, the Nob Hill area was found
to have 31 times higher net household density, 26 times higher gross population
density and 198 times higher local serving job density than Danville-San
Ramon, while only about 1/3 the auto ownership per capita and 1/4 the auto
ownership per household. The other communities fell within these extremes,
except for Walnut Creek's auto ownership per capita, and varied uniformly
in the order presented. Comparing communities showed that the higher the
population, household or local serving job density, the lower the VMT, as
shown in Table 1. VMT measurements for other large counties and the state
were included for comparison. 
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