Let $Y$ be a random variable defined on the domain $[1;\infty)$ that is distributed according to the cdf $G_Y(y)$.
A Pareto distribution,
$$ G_Y(y) = 1 - y^{-\theta}$$
has the property that
$$ P(Y>ay) = 1 - G_Y(ay) = a^{-\theta} (1-G_Y(y)).$$
Similarly, the exponential distribution,
$$ G_Y(y) = 1 - \exp(-\theta (y-1)).$$
has the property that
$$ P(Y>y+b) = 1- G_Y(y+b) = \exp(b)^{-\theta} (1-G_Y(y)).$$
Is there any distribution that combines these two properties, i.e. where $$ P(Y>ay+b) = z(a,b) (1-G_Y(y)),$$ where $z(a,b)$ is some function of $a$ and $b$?
I expect this distribution to work for any real parameters $a$ or $b$ where $a$ is strictly positive and $b$ can be zero or negative. I expect $z(a,b)$ to be a function of both $a$ and $b$.
The solution should hold for all possible values of Y that are in the domain $[1;\infty)$, and any combination of $a$ and $b$ where $a$ is strictly positive. Of course, if a solution can only found for a subset of these, then that's better than nothing. One might has to restrict $a$ and $b$ such that $aY+b$ has the same domain as $Y$.