Transforming the independent variables is NOT improving fit for conditional logistic regression I built a conditional logistic regression model for 'guest booking a hotel from the hotel search results page'. In my initial model, I didn't do any transformations to the independent variables. This model fits fine, and I am exploring ways to see if I can improve the fit. 
So, later, I tried different transformations (log, normalizing etc.) to the independent variables (distance from search center, rate , reviews, etc.). However, whatever transformations and combination of transformations I try for the independent variables, the model fit is not improving than the initial model (without transformations). 
Here is what my data looks like. Instead of using absolute distance or price in below as the independent variable, I am using "distance/(mean distance in the search)". And this transformation is very relevant for the data and assume it should improve the fit atleast slightly. Any help would be greatly appreciated. 
Search   Property_id    distance (miles)    price   # of reviews    Booked 
1         abc             0.9                  75      125            0
1         ced             1.5                  67      541            0
1         der             2.3                  68      320            1
1         gft             1.1                  85      84             0
2         bcd             3                    70      64              0
2          bcr            2.3                  105      320            1
2          edr            4.4                  98       154            0
2          gft            7.8                 120       27             0
2          frt            6.2                  80       65             0

I have pretty good data size with some 50K searches in my model data.
 A: It would be helpful to see more data to be clearer on why this is occuring.
With that being said, here is my take on the problems you describe:
1) Have you already checked with a QQ-Plot (if you are using R, input: qqnorm(variable); qqline(variable) the distribution of your model? You need to make sure that the distribution (or the one you transform it to) makes theoretical sense, and you are not just doing so by a trial and error process.
2) A logistic regression, by its very nature, has much less variation in the dependent variable than an OLS regression, since the dependent variable is not interval. In this regard, higher numbers of observations are preferable when running a logistic regression (500 or greater according to Studenmund et. al). Does your dataset have sufficient observations to explain the variation in the dependent variable?
You should think carefully about the above two factors, along with reevaluating whether your independent variables make theoretical sense in the first instance, before attempting to transform data in this manner. 
