I have the following multiple linear regression model:

Call:
lm(formula = Y ~ X1 + X2 + X2 + X3 + X4 + X5 + X6 + X7, 
    data = my.model, na.action = na.omit)

Residuals:
    Min      1Q  Median      3Q     Max 
-43.836  -1.507   0.010   1.485  46.231 

Coefficients:
               Estimate Std. Error t value Pr(>|t|)    
(Intercept)  -0.0244927  0.0245157  -0.999    0.318    
X1           -0.3484619  0.0134383 -25.931   <2e-16 ***
X2            0.1195273  0.0106940  11.177   <2e-16 ***
X3            0.1224587  0.0108849  11.250   <2e-16 ***
X4           -0.0010173  0.0028247  -0.360    0.719    
X5            0.5496942  0.0156319  35.165   <2e-16 ***
X6           -0.2287941  0.0145018 -15.777   <2e-16 ***
X7           -0.2315801  0.0146361 -15.823   <2e-16 ***
X8            0.0005465  0.0003595   1.520    0.128    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Residual standard error: 2.936 on 35849 degrees of freedom
  (12534 observations deleted due to missingness)
Multiple R-squared: 0.05968,    Adjusted R-squared: 0.05947 
F-statistic: 284.4 on 8 and 35849 DF,  p-value: < 2.2e-16 

The model is affected by multicollinearity but my question is about the forecast, so this shouldn't be an issue.

I checked the absolute values of my model forecast and compared against the actual Y absolute values. The average of the absolute predicted values is significantly lower than the absolute observed values mean:

> lm1.predict = predict(lm1, mydata)
> mean(abs(lm1.predict))
[1] 0.3294776
> mean(abs(mydata$Y))
[1] 1.206954

Does this mean that the linear regression variables I am using tend to underestimate the outcomes? Can any other conclusion be derived from this simple comparison?