What data treatment/transformation should be applied if there are a lot of outliers and features lack normal distribution?
This comments aren't all mine, I have asked on a slack forum,

boxplot is shouting at you:  skewness, and also high dispersion.  Can not ask much more to a boxplot than location, dispersion and skewness..

Also checkout this term heteroscedasticity(completely suits your case)

Try switching the transformation to a log plot or lower..

Also your eda can't be boxplots dependent as prices are involved here..one of the remedy would be doing box-cox transformation

https://www.differencebetween.com/difference-between-dispersion-and-vs-skewness/

http://www.statsmakemecry.com/smmctheblog/confusing-stats-terms-explained-heteroscedasticity-heteroske.html

To have a look at it,
https://datascienceplus.com/how-to-detect-heteroscedasticity-and-rectify-it/