Problems with cross-validation for time-ordered data Let's assume a simple scenario: You want to forecast the house prices of various properties. The dataset is cross-sectional in nature, as you only observe each property once. However, you also have a timestamp of the observation (month/year).
In a sense, this means that the data is intrinsically time-ordered. In general, I know that with time series data, the use of e.g., k-fold cross-validation can be problematic. However, I don't have real time series data (multiple obs. for one house), but time-ordered data across houses. It is also clear that for time series data, there are other approaches such as rolling estimation.
In particular, I would like to understand theoretically from a statistical/econometric perspective why this might be problematic rather than just intuitively. Does anyone have a theoretical guide to this topic or know of relevant literature to delve deeper into this topic?
EDIT: Based on @Björn's answer, it seems useful to provide more information about the goal here. The goal is actually to predict future prices based on the trained model.

I can't speak to the theoretical aspects, but why not preprocess your data to avoid the problem? If you can get some house price inflation data you can inflate the prices of the older (earlier timestamped) properties to values you can reasonably expect them to fetch today and then train your predictive algorithm using apple to apple comparisons across the data.
