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In theory, proximity to light rail transit (LRT) may have two different effects on residential property values. On the one hand, accessibility (proximity to LRT stations) may increase property values. On the other hand, nuisance effects (proximity to the LRT line and stations) may decrease property values. Existing empirical studies are inconclusive, and failure to separate the effects of accessibility from the nuisance effects may explain some of the ambiguity. An examination is presented of the impact of the light rail system (MAX) in Portland, Oregon, on single-family home values using distance to rail stations as a proxy for accessibility and distance to the line itself as a proxy for nuisance effects. Geographic information system techniques are employed to create spatial-related variables and merge data from various sources. The study results confirm the hypothesis that the light rail has both a positive effect (accessibility effect) and a negative effect (nuisance effect) on single-family home values. The positive effect dominates the negative effect, which implies a declining price gradient as one moves away from LRT stations for several hundred meters. Without controlling for the nuisance effect of the distance to the rail line, the estimated coefficients on distance from stations appear to be biased and would underestimate the accessibility effect. The finding of an independent nuisance effect suggests that previous hedonic models may have reached contradictory results because the nuisance effect differs with different types of rail or other local characteristics.

The Oregon Department of Transportation tested a system to collect a vehicle-based mileage fee as a replacement for the Oregon gas tax. Devices based on the Global Positioning System were installed on participating vehicles to determine the location and time of vehicle travel. The only information collected was vehicle mileage by location and time category. Midway through the study, vehicles were assigned to one of three groups: control, vehicle miles traveled (VMT), and peak hour. The VMT group was charged a flat fee per mile traveled that approximated the amount of the state gas tax then paid by the average vehicle and was given a credit for the amount of gas tax included in the purchase. The peak hour group was charged a higher mileage fee during peak periods and given a discount on the mileage fee outside the peak times or peak zones. The control group continued to pay the price, including gas tax at the pump, as before. The VMT group showed a reduction in total mileage relative to the control group, and survey responses indicated that people did change their behavior even though there was little price differential. However, this change might have developed because of being in an experiment. The peak hour group showed about a 20% reduction in travel during peak periods relative to the VMT group. This reduction appeared to be due to the pricing system, because factors other than the congestion price differential were likely to affect each group similarly.

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