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Re-post.

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.

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I agree, the simple version of this problem isn't really a time series problem. There are some ways to deal with irregular intervals, but, houses are not all the same and we do not have a series of sale prices for it in most cases.

What may of course be very predictive is time. Housing prices probably have a long-term trend that's linear over the space of a few years, at least. There is probably a seasonality component, so month might be meaningful if used correctly.

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