Linear Regression bad results after log transformation
Make sure you transformed back your predictions and actual values before calculating MAPE.
You can check which observations contributed the most to high MAPE.
MAPE is very sensitive to prediction errors at small actual values. Most likely worst performing observations (&quot;from MAPE perspective&quot;) are those with small actual values.
Depending on the goal of your analysis you could check other metrics as well (eg: MAE).
Sparsity: Yes, you might have a neighborhood category in your test set, that does not exist in your training set (or has only a few examples). In this case predictions for that category might be very bad. Though this does not explain why you don't have high MAPE when you don't transform area.