Reason for not using “fundamental” variables in your analysis

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    • #25390
      AvatarC M

      Hi There,

      I just had a quick question for you if that is ok.

      Have been checking out your site and think it’s pretty good – very interesting.

      However, the FAQ section did intrigue me – in particular this question but obviously the answer:

      Do your forecasts take into account metrics other than historical price and sales data?
      At HtAG, we divorce the analysis of causality between fundamental market variables such as population and unemployment and property price changes. By recording real time movements in the property market, the plethora of causes that impact on fluctuations in property prices have already been accounted for within the collected Big Data that serves as an input into HtAG’s algorithms. At HtAG, any market changes become an instantaneous input into the HtAG prediction model ensuring the continuous agility of our predictions and alignment with existing market conditions.

      Can someone please explain to me exactly what you guys mean by this?

      All readings, newsletters, etc, etc always have “fundamental” research as part of the “finding a suburb” pursuit – except you guys !!!

      Look forward to your answer . . .

    • #25393
      AlexAlex
      Keymaster

      Hi,

      Before I answer your question, I need to explain the foundational principles of the HtAG methodology.

      1. We collect Australian property market data via a Web Crawler which systematically browses major real estate portals and agent sites in the country and indexes past sales, current listing and rental data. This means that there is minimal lag in our data.
      2. We train our model using a combination of historical and recent (days and weeks) sales data to produce the median values and forecasts. Recent data is given higher weightage than the historical data for forecasting.
      3. Property market is cyclical. It is also an amalgamation of many different submarkets that have complex inter-relationships with each other. These inter-relationships (spill-over effect) and referent submarket cycles are fed as additional inputs into our model.

      Our philosophy is simple: apply methodology that produces the best outcome. We conducted numerous tests and found that external variables (used as regressors) have no material impact on the accuracy of our model. What we did find, however, was that the cyclicity and spill-over effect paradigms, when applied correctly, produce the best results.

      Here are some of the “fundamental” external variables that were tested: Population, Unemployment, Building Approvals, Stock on Market, Migration etc.

      Having said all of the above, we are strong advocates of the common sense. Although our model was tested and proven, it is not a crystal ball and has the following error rate:

      • High Confidence: error <5%
      • Medium Confidence: error <10%
      • Low Confidence: error <15%

      We developed our methodology with a view that it will simplify property market research and minimise the need for fundamental analysis. However, our team recommends that fundamental analysis is always performed in localities tagged as “low confidence”.

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