Normalized metric for comparing regression models performance
A couple suggestions


MAE represents your mean error. This is essential to recognize as having an error of even half of your MAE on the low end of your spectrum ($34,900) is huge. However, to your point, at the high end of your spectrum it is quite small.
To solve the above, you should plot a graph to identify the patterns in your error. You'll plot your predicted y value vs. your actual y value and see where your predictions deviate from the y=x line.


Normalized metrics:

you may use an r-squared value. This might be ideal because its looking at the percent of variance explained by your regression and thus is a relative measure of fit. Your r-squared value will always be between 0 and 1 (assuming your model is not worse than always guessing your mean value) where 1 is perfect. Implement as follows:

from sklearn.metrics import r2_score
y_pred = model.predict(X_train)
print(r2_score(y_true, y_pred))