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 don't think that having a time-stamp on the data is necessary problematic for housing price forecasting.
The procedure is to decompose the house into its attributes (size, location, etc.), add dummy variables for time and then do regression on these variables, this is called a "hedonic model" in the econometric literature, for a detailed discussion check for instance chapter 4.3 of "The practice of econometrics: Classic and contemporary" Berdnt, 1996.
When you use this method, you estimate the coefficients accompanying the dummy time variables, so the model does not use specific house values from the past to compute future values.
