A linear pattern occurs on my residual plot: what can I do? I'm a bit stuck with a problem here and any kind of help would help a lot :)
Just to give a clue about my data. I have 6 independant variables (IV) which are:

*

*$X_1$ = Population -within a block-

*$X_2$ = Households -within a block-

*$X_3$ = Total Rooms -aggregated-

*$X_4$ = Total Bedrood -aggregated-

*$X_5$ = Median Income

*$X_6$ = Ocean Proximity [Categorical]

and my dependant variable (DV) is $Y$ = Median House Price.
I ran a regression including all IVs but there is a violation for almost all OLS assumptions in addition to huge multicollinearity. Here are the residual plot and normality plot before any adjustments.

What I did then is transform all my IVs and my DV using the Box-Tidwell method which I think it is not the correct way of solving the issue so this is my first question what can I do to solve the normality issue?
The other problem is that even after transforming all variables I still have an issue in my residual plot which is the main problem here. I have a linear pattern on the graph that I don't know how to solve. I run my DV against each IV separately and still have the same issue. Here is the graph for the transformed model.


I'm not sure why the linear model, with its many assumptions, was chosen as the default model.  Ordinal semiparametric models are very efficient and are invariant to how Y is transformed.  They allow for floor and ceiling effects, bimodality, and any other kind of distributional quirks you can throw at them.  The most popular semiparametric models are the proportional odds and proportional hazards models.
I would also not want to assume up front that the continuous predictors operate linearly.  I'd expand them using regression splines such as restricted cubic splines (aka natural splines).  A detailed case study may be found in Chapter 11 of the RMS course notes.
