I have a set of real estate data; historic sales price, square meters, location (latitude, longitude), neighbourhood, city, sold date and bunch of other features. I have used a boosting model to estimate the price of an individual house. I would like to estimate the price of a house 1 month ahead; but this requires extrapolating and something boosting is not capable of.

For this reason, I am trying to create inflation adjusted houses prices which will be fed into the boosting model. Old sale prices will therefore be inflation adjusted to 1 month ahead. This way I can capture inflation, and extrapolate over time. My objective is to create a separate model that can capture local changes in housing prices and extrapolate them forward. Essentially a price index.

I have aggregated the data at a city level by taking the median price per square meter for each month. The data exhibits strong AR characteristics. However, when I try this same approach on a neighbourhood (more local) level the AR characteristics disappear. I am unfortunately limited to 2 years of data.

What approach would you suggest to modelling a local price index that can be extrapolated with such a dataset? Kriging appears to be powerful, but is not made for extrapolation..


Isn't this an ML practice problem?


  • $\begingroup$ Thanks for the reply. No, its not. I want to create a local price index that can be used to make forecasts into the future. $\endgroup$ Jun 25 at 7:23
  • $\begingroup$ If that's the case, you can look at how the FHFA defines HPI: fhfa.gov/PolicyProgramsResearch/Research/Pages/… $\endgroup$
    – VVKK77
    Jun 25 at 17:20
  • $\begingroup$ Old approach + based on repeat sales. I don't have repeat sales.. $\endgroup$ Jun 29 at 16:00
  • $\begingroup$ What is "historic sales price" then? Not sure why you're being critical of an "old approach" that is still consistently used in the housing market. Take a look at Zillow's definition, might help you. zillow.com/research/data Good luck with your problem. $\endgroup$
    – VVKK77
    Jun 29 at 22:51

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