The Flyer Index gave a signal

After several years, the Hungarian housing market raised from its ashes in 2014. A long stagnation was broken and prices began a steep climb, just as the number of transactions also went up significantly. Rising prices and higher volumes persisted — up until recently.

Five years ago, a noticeable sign of reversal was that my mailbox became flooded with flyers saying “Looking to buy an apartment.” In line with the state of the housing market, I had not received any of these before then. Most are from realtors that seek out buyers for the restless sellers. The amount of flyers thus somehow indicates the strength of the demand side.

Only in fall 2015 did I realize that maybe I should compile statistics on them before throwing them in the trash. Since then, I have been counting the flyers in my mailbox that urge me to sell my apartment. I get 4-5 of them each month on average, even 7-8 in outstanding months, and only 1-2 during scarce periods. Unfortunately, the standard deviation of the raw data is too high to draw any specific conclusions about the course of the demand side. All that is further obfuscated by seasonality. This is why we should take the yearly moving average of the monthly data points, as seen in the chart below.

It very much looks like the housing market fell into a pit, so to speak. The curve set its previous minimum in September, and continued to slide down ever since. In November, the yearly moving average was only 3.33 fliers per month, so the demand side may be weakening.

Is there any use to this information? Is there a point in counting flyers? In this case, not much, other than self-entertainment. With minor delays, the Central Statistical Office or other market participants provide more accurate statistics about the housing market. We have been long aware of the decrease in trade volume. And one could anticipate it, due to the launch of the Hungarian “super bond” in July. This provides housing market-level returns through a less risky and much more liquid product, with way less trouble. The waning housing demand is no wonder.

Nevertheless, the Flyer Index nicely demonstrates how we can extract information about a market via improvised, alternative data collection. Were there no public statistics on the housing market, I would still know about the declining situation just by watching my mailbox.

And, in certain cases, this information can be monetized. This is mostly when the majority has no, or only delayed, access to the information, because data collection, processing and interpretation requires specialized knowledge that no statistical office offers.

Just as an example, the blockchain contains a wide array of information that is highly valuable to a cryptocurrency-focused investment fund. Extracting this information through data mining, they can get a real-time picture about the state of the crypto market. This can be utilized right away in automated trading by applying the insights to the fund’s algorithmic strategy.

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