The model itself isn't any better or worse. The percent error statistic you are using assumes the distance of your predicted value from the observed value in units of how far the observed value is from $0$ is meaningful. If that is true, then situations where the data have the same residual SD, but the observed values are further from $0$ are more fortuitous situations. If that isn't true, then this statistic isn't appropriate.
The percent error metric probably makes more sense for a model in which the effects are multiplicative and you have a constant coefficient of variation, rather than constant variance. Another possible example might be a case where you estimate a certain amount of return on investment. Consider two cases: in the first you might make $\$1\pm\$2$ and in the second you might make $\$10\pm\$2$; in the first case you can loose money, but in the second case you make a decent return either way.
If you want to compare two models to the same dataset using this metric, it shouldn't make any difference, because the actual values will be the same in either case, so both would be standardized identically. What would happen is if the models differ in accuracy in different parts of the response space, that would be differentially weighted. For example, if one model yields predictions that are closer to the observed values when the observed values are high, and further when they are low, relative to the other model, the first would look better. Again, this assumes that metric is appropriate.
In addition, be aware that predicted values that are below the observed values will scale differently from those that are above. You may want to take the $\log$ of each of the percent errors to make that symmetrical and easier to interpret.