Machine Learning Courses often teach house prices prediction using multiple linear regression - when we want to predict the value of a variable based on the value of two or more other variables. Indeed the one I did on coursera and that makes sense to me.
I note that others state that a techniques like time series is also a suitable method to predict the house price, given a set of input variable.
I am not clear as to why this could be so. Simply because there is no notion of selling of a house at a regular interval afaik. That is to say, the sale date (one of the variables) can be at any time, not at a reasonably regular interval. Or is it possible because it is not just one house but many houses that can contribute to such an interval?
So, looking for a clarification as to why time series could be an approach for house price prediction. To borrow from wikipedia: A time series is a series of data points indexed (or listed or graphed) in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data. Examples of time series are heights of ocean tides, counts of sunspots, and the daily closing value of the Dow Jones Industrial Average.