Multicollinearity and impact of individual features
When you face multicollinearity, your regression coefficients will likely be biased, because under multicollinearity, the regression can not tell apart the different effects: https://datascience.stackexchange.com/a/57118/71442.

When you use only one variable at a time, you will face the omitted variable bias, because there are no other confounders, to which the regression could attribute relevant effects. https://en.wikipedia.org/wiki/Omitted-variable_bias

To my best knowledge, there is no easy-to-go way to mitigate one or both of the described effects. You should carefully check the correlation between your $x$ and decide if multicollinearity is a problem. If this is the case, and you believe (for theoretical reasons), that all the highly correlated $x$ are important, you could try to find other representations of (some) $x$ to mitigate multicollinearity, e.g. dummy/indicator representations.

I guess your chair example is generic, so I don‘t speculate on this. Maybe you can give some more background regarding the actual problem.

The discussion above refers to causal modeling. If you are only interested in making predictions (not statistical inference), you could look into shrinking coefficients using lasso.