Fitting a distribution to data I have a large data set of property values at two points in time. I have calculated the change in value between those two points. I want to assess which distribution most closely matches the variation in change in property values. What are some possible ways of selecting the closest type of distribution?

I plan to use python, scipy and pandas to do this, although my question is more about what are the correct statistical tests to do to determine whether one distribution is a better fit to the data than another.
My understanding of your problem: you have realizations of $X$ (property value at first point in time) and $Y$ (value at second point in time). Want to fit a distribution for $Y-X$--more generally, a "model for the data."

One way of doing this would be plot a histogram of these values and then come up with plausible distributions that might fit the data, and then use maximum likelihood estimation to find the optimal parameters. If the models are nested, then you can use, e.g., the likelihood ratio test to determine which is a better fit to the data. 

If not, then things are trickier. One good option is to hold out some of the data for testing, fit the models on the training data, predict the test data using each model, and see which one performs better.