Proponents of advanced bikeways will point out a growing body of research on these facilities’ safety and benefits for cycling. They can now add another benefit: higher home values.
Research led by Jenny Liu of Portland State University looked at property around advanced bikeways in Portland, defined as bicycle boulevards, protected bike lanes and buffered bike lanes. She found positive effects on property values close to one of these bikeways and an even stronger effect where the network was denser.
Liu presents her research Wednesday at the annual meeting of the Transportation Research Board in Washington, D.C. Learn more or download the research paper.
For single family home sales, being a quarter mile closer to an advanced bikeway translated to a $686 premium, while increasing the density by a quarter mile represented a $4,039 premium. For multi-family homes, the effect of being close to a bikeway wasn’t statistically significant on sale price, but increasing the density of bikeways translated to $4,712 of value.
The research can inform policymakers who may question how much residents value bikeways and provide insight into siting decisions. “My results don’t...
Although it is widely claimed that Oregon's economy is dependent on freight movement, economic activity in Oregon has decoupled from physical goods movement. Truck traffic per unit of gross state product has fallen, and even the loss of regular container service to Portland has had no measurable effect on the region's economy.
Oregon's economy has shifted away from freight intensive industries and now depends on knowledge driven sectors (e.g. electronics, software, athletic apparel and footwear professional services) that move very small amounts of freight. In addition freight costs for most output is so small—and declining—that it is a negligible factor in industry profitability and location decisions.
In recent decades, Bus Rapid Transit (BRT) has gained popularity across the United States due to its relatively low costs of development (compared to the investment requirements of putting in a new light rail system, for example) and its potential to drive economic development.
However, there is a need for more comprehensive research devoted to understanding its economic impacts across various sectors.
NITC researcher Joanna Ganning is the lead author on a research paper that will be presented at the annual meeting of the Transportation Research Board this month, which seeks to estimate the effects of BRT stations on employment growth.
Using Longitudinal Employer-Household Dynamics data, Ganning and her research team investigated the impacts of BRT on employment changes of each major industry sector between 2002 and 2010.
The researchers analyzed employment data surrounding 226 BRT stations along nine BRT corridors which were opened during the study period, as well as employment data from equally sized areas around control points.
Metropolitan areas included in the analysis were Phoenix, Los Angeles, Kansas City, Las Vegas, Salt Lake City, New York City, Cleveland, Ohio and Eugene, Oregon.
With the presence or absence of BRT stations as the independent variable, the team found that BRT statistically significantly influenced employment change for just one...Read more
As metropolitan area governments and others promote density-promoting “smart growth” policies, finer analysis is needed to quantify the impact of such policies on households' transportation and housing costs. Existing research suggests that households in urban areas trade-off between housing costs and transportation costs, but does not explore how policies to increase urban densities might explicitly impact this balance. Furthermore, the research does not adequately distinguish between the effect of urban area density and the effects of other factors associated with urban area density (e.g metropolitan area size and household incomes) on housing costs. This research uses the 2000 Census Public Use Micro Sample (PUMS) person and household data from 23 of the nation's most densely populated states to identify the impact of increased population density on three housing cost measures: household rents, housing unit values, and monthly mortgage payments. Log linear models were estimated for each housing cost measure using least-squares regression. Dependent variables included household, housing unit, and geographic area characteristics, including population density. The models were found to be very similar to one another in terms of the...Read more