Like @Joran told, %Var is the amount of total variance of Y explained by your random forest model. After the adjust, apply the model to your validation data (1/3 remain):
RFestimated = predict(r, data=ValidationData)
It is interesting also to check the residual:
qqnorm((RFestimated - ValidationData$V9)/sd(RFestimated-ValidationData$V9))
qqline((RFestimated-ValidationData$V9)/sd(RFestimated-ValidationData$V9))
the estimated versus observed values:
plot(ValidationData$V9, RFestimated)
and the RMSE:
RMSE <- (sum((RFestimated-ValidationData$V9)^2)/length(Validation$v9))^(1/2)
I hope this help!