Hello!
I would like to estimate the effect of a flood event on land prices. I have decided to use a difference-in-difference design, with a treatment group containing observed land transactions before and after the event (repeated cross-section) inside the floodplain, and a control group outside the floodplain. In addition to the price per square meter, the data also include various details about each land unit (distance to the city center, distance to the river, square meters...). The pre-treatment price trend is quite different for the two groups, so I want to restrict the control group to observations where it is more plausible to assume a parallel trend. Since the number of possible observations for the control group is large (45000 observations), I want to select only those that might closely resemble my counterfactual.
I have read a lot about matching techniques, but I am struggeling to implement them properly.
My approach was the following:

1. I performed PSM with different types (NN, Calapier...) and chose the one where the covariates are best balanced.
2. I created a dataset with only the matched data and estimated the DiD.

My questions are:
1. is it ok to use covariates that correlate with the dependent variable and treamtnet status?
2.Can I include the covariates in the DiD regression with the matched data?
3.What problems may arise with cross-sectional data? Should I match the pre and post treamtent observations separately?
4. Are there other (better) techniques for control group selection?