Car dependence and neighborhood affordability

Deborah Salon
Arizona State University, USA

As humanity continues to urbanize, urban planners and city decision makers have an exciting opportunity to shape the environmental impact of new urbanites. There has been much research done on the potential of higher density, “smart growth”, “new urban”, and “transit-oriented” style development to reduce driving in cities (e.g., Cervero & Murakami, 2010Hankey & Marshall, 2010). There has also been considerable concern that the high cost of living in these neighborhoods could price out lower income households (e.g., Downs, 2005; Alexander & Tomalty, 2002).

The trade-off between urban housing and transportation costs is well-known (Alonso, 1964). In general, housing closer to the city center will be more expensive, but commuting and other transportation costs will be lower in those locations. In the simplest theoretical model of a monocentric city, these costs – including both money and time costs of transportation – exactly balance out.

In the real world, however, the relationship is more complex. Cities are not monocentric, and people, housing units and neighborhoods are all heterogeneous in many ways. Understanding the relationship between housing and transportation costs in real cities is important. Cost of living helps determine where people actually choose to live, and where people choose to live contributes to the environmental impact of their everyday lives – in large part because of spatial differences in vehicle use for daily travel.

This study uses publicly available data to explore the following question:  Does the spatial distribution of affordability in US metropolitan areas encourage car-dependence?

To put it another way, within a metropolitan area, is it cheaper to live where you have to drive a lot (even counting the cost of that driving) than it is to live where you don’t? The answer has important implications for the future environmental sustainability of our urban areas. If the cost of living is systematically lower in car-dependent neighborhoods than in areas that have embraced smart growth principles, then households relocating in urban areas are more likely to choose car-dependent neighborhoods, and our metropolitan areas will become increasingly car dependent over time. If, however, the cost of living is systematically higher in car-dependent neighborhoods, then our metropolitan areas will become increasingly car independent over time, leading directly to an improvement in environmental sustainability.

Affordability on the map

The data used in this study comes from the Location Affordability Index (LAI) project and the US Census Bureau’s American Community Survey. The US Census provides a host of data, including the median monthly rent paid and median monthly cost of home ownership. The LAI provides formulas to predict both average household vehicle miles traveled (VMT) and household vehicle-related costs with readily-available US census data (Housing and Transportation Affordability Initiative, 2013).

The following maps depict the spatial distribution of VMT, housing costs, and affordability for the current residents of a sample of US metropolitan areas. Tracts are labeled “Affordable” where the percent of income spent on housing and car transport is 45% or less, “Burden” where it is between 45% and 60%, and “High Burden” where it is 60% or greater. The most heavily burdened tracts are generally those in low housing cost areas. This is because the people who live in these areas are also low income.

These maps do not seem to support the idea that there is a trade-off between housing and transportation costs. In fact, it appears that households are paying more for housing in places where they also have to drive more. However, this pattern is largely explained by the variation in housing unit size. Housing costs per room do indeed drop as one moves away from the city center and VMT rises, but total housing costs rise because house size increases faster than the cost per room drops.

It is worth noting that that topic of this blog post is closely related to that which has been explored by the Center for Neighborhood Technology (CNT) in their H+T Index work, as well as to the maps on the LAI website. The maps in this post, however, illustrate the spatial patterns of cost and affordability for the actual residents in each tract. In contrast, the CNT and LAI maps illustrate the spatial patterns of cost for a household with constant characteristics across the city.

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Does the spatial cost structure in our cities encourage car dependence?

To determine whether or not the spatial distribution of affordability encourages car dependence, I first created census tract-level estimates of both affordability and VMT that control for both housing unit size and key household characteristics. I did this for each of three housing submarkets (low, mid-range, and high cost) in each of 12 major metropolitan areas in the US. I then calculated the correlations between these cost estimates and VMT.

Negative correlations tell us that the cost of living is systematically lower where people drive more, and the spatial distribution of affordability is encouraging car dependence (more car ownership and use). Positive correlations tell us that the cost of living is systematically higher where people drive more, and the spatial distribution of affordability is encouraging car independence (less car ownership and use).

Table 1: Strength and Sign of Correlation between Comparable Estimates of VMT and Housing + Car Money Costs

  Low Cost

(<25th percentile)

Mid-Range Cost

(25th-75th percentile)

High Cost

(>75th percentile)

New York 0 0
Philadelphia 0 0 0
Los Angeles 0
San Francisco 0
Houston
Miami
Phoenix 0 +
Atlanta
Boston
Chicago
Seattle 0
Portland 0 0

Note: Correlations are coded as “0” for any correlation less than 0.25 in absolute value. A single “+” or “-” sign indicates a correlation between 0.2 and 0.5 in absolute value. A double sign “++” or “- -” indicates a correlation greater than 0.5 in absolute value.

When looking only at the monetary costs of housing and car transportation, I found clear evidence that it is cheaper to live where you have to drive more (Table 1). This relationship is especially strong for the low and mid-range market segments within most of the selected metropolitan areas. Considering only the monetary costs, then, the spatial structure of affordability in many US metropolitan areas does indeed appear to encourage car dependence.

However, this presents an incomplete picture because driving has both money and time costs. The correlations presented in Table 2 correct for this, including a rough estimate of the time costs of driving. Specifically, these correlations are between the money and time costs of housing plus car transportation and VMT. In the low cost residential submarket, the result is weaker, but in the same direction as before. In half of the metropolitan areas studied, it is cheaper to live where you have to drive more – even counting both the time and money cost of that driving. In the mid-range submarket, the result becomes mixed. In the high cost submarket, the result completely reverses, and the correlations strongly indicate that the spatial distribution of costs for this submarket in our cities encourages car independence. For the top 25% of households, then, it may be more expensive to live where you have to drive more.

Table 2: Strength and Sign of Correlation between Comparable Estimates of VMT and Housing + Car Money & Time* Costs

  Low Cost

(<25th percentile)

Mid-Range Cost

(25th-75th percentile)

High Cost

(>75th percentile)

New York 0 +
Philadelphia 0 + +
Los Angeles +
San Francisco 0 0 +
Houston 0 0 +
Miami 0 0 0
Phoenix ++ ++
Atlanta 0 0 0
Boston 0 ++
Chicago 0
Seattle + ++
Portland + ++

*Time costs are estimated at 50% of the hourly wage (Small, 1992) implied by the median income in each housing market segment. Travel time is estimated as the total vehicle miles traveled at an average travel speed of 40 miles per hour. Single occupant vehicle travel is assumed.

Conclusion

Which of these sets of results is closer to the truth? More research will be required to say, and to understand the drivers of these patterns more fully. In the meantime, this work provides evidence that there is cause for concern.

To reduce fossil fuel use, pollution, and traffic congestion, the cost structure of our metropolitan regions should encourage car independence. However, this work shows that the spatial distribution of neighborhood affordability in most US metro areas for most residential submarkets does not do so. In the low cost submarkets of many cities, it is actually cheaper overall – even counting both the money and time costs of driving – to live where you have to drive more.

There are multiple policy ideas that could help change this, including location efficient mortgages, pay-as-you-drive car insurance, and VMT fees. Most of these are not widely used, but could be. This analysis provides one more piece of evidence for their adoption.


Deborah

Dr. Deborah Salon is an Assistant Professor in the School of Geographical Sciences and Urban Planning at Arizona State University, and a Senior Sustainability Scientist at ASU’s Global Institute of Sustainability.
Email

Header Image: Bay Bridge and Downtown San Francisco covered by fog. Credit: Pung / shutterstock.com

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