Categorical and ordinal feature data representation in regression analysis? I am trying to fully understand difference between categorical and ordinal data when doing regression analysis. For now, what is clear:

Categorical feature and data example: 
Color: red, white, black  
Why categorical: red &lt; white &lt; black is logically incorrect

Ordinal feature and data example: 
Condition: old, renovated, new  
Why ordinal: old &lt; renovated &lt; new is logically correct

Categorical-to-numeric and ordinal-to-numeric encoding methods: 
One-Hot encoding for categorical data 
Arbitrary numbers for ordinal data

Categorical data to numeric:

data = {'color': ['blue', 'green', 'green', 'red']}


Numeric format after One-Hot encoding:

   color_blue  color_green  color_red
0           1            0          0
1           0            1          0
2           0            1          0
3           0            0          1


Ordinal data to numeric:

data = {'con': ['old', 'new', 'new', 'renovated']}


Numeric format after using mapping: Old &lt; renovated &lt; new → 0, 1, 2

0    0
1    2
2    2
3    1


In my data I have 'color' feature. As color changes from white to black price increases. From above mentioned rules I probably have to use one-hot encoding for categorical 'color' data. But why I cannot use ordinal representation. Below I provided my observations from where my question arised.

Let me start with introducing formula for linear regression:

Let have a look at data representations for color:

Let's predict price for 1-st and 2-nd item using formula for both data representations:
One-hot encoding: 
In this case different thetas for different colors will exist. I assume that thetas already derived from regression (20, 50 and 100). Prediction will be: 

Price (1 item) = 0 + 20*1 + 50*0 + 100*0 = 20$  (thetas are assumed for example)
Price (2 item) = 0 + 20*0 + 50*1 + 100*0 = 50$  


Ordinal encoding for color:
In this case all colors will have 1 common theta but  my assigned multipliers (10, 20, 30) differ:

Price (1 item) = 0 + 20*10 = 200$  (theta assumed for example)
Price (2 item) = 0 + 20*20 = 400$  (theta assumed for example)


In my model White &lt; Red &lt; Black in prices. Seem to be that correlation works correctly and it is logical predictions in both cases. For ordinal and categorical representations. So I can use any encoding for my regression regardless of the data type (categorical or ordinal)? This division in data representations is just a matter of conventions and software-oriented representations rather than a matter of regression logic?
The distinction between ordinal and categorical does matter. If in truth the difference between white and red was drastically different from red and black, your (10,20,30) ordinal model would not have performed well.

One hot encoding can learn the relationship between the ordinal values more finely, but throws out the information that the variables are related. Similarly, with insufficient data it is more likely to overfit.

Ordinal variables lessen those  problems but at the cost of forcing you to define the interval. There are a number of methods for defining the values of your ordinal variables, like rologit.