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Andrew, this is incredible, you look great for being 2,000 years old!!Really enjoyed this, Mike. It made me think back to something Seneca wrote ~2,000 years ago:
If an evil has been pondered beforehand, the blow is gentle when it comes. To the fool, however, and to him who trusts in fortune, each event as it arrives “comes in a new and sudden form,” and a large part of evil, to the inexperienced, consists in its novelty. This is proved by the fact that men endure with greater courage, when they have once become accustomed to them, the things which they had at first regarded as hardships. Hence, the wise man accustoms himself to coming trouble, lightening by long reflection the evils which others lighten by long endurance.
Truly, it is when things are at their best that we should be preparing for the worst. Great advice.
Man I wish I had the time . . . this sounds like a wonderful way to evaluate markets!As bubbles are created by a pot of water on a cooktop, the same could be true for the VR industry - maybe some kind of "heat index" could be formulated.
If there's a nerdy number cruncher in the IC (maybeWes), I'll wager that a heat index can be calculated for most destinations by, let's say, calculating the transient guest taxes in a community/county (numerator) and then dividing by the number of second homes multiplied by the destination's residential full-time population (denominator).
Transient guest taxes would be a monetary measurement of a destination's popularity and would have to include local motels, hotels and B&Bs too. Determining the number of second homes might be tricky unless there's a savvy local real estate guru who can suss out info on the county's website (but be wary relying solely on OTAs, as we all know many second homes are listed on quite a few platforms). By including the destination's permanent population, "small town" destinations, such as Breckenridge could have a much higher heat index than "big town" destinations such as Orlando or Scottsdale.
The data would be $ per household per resident.
For many destinations, this information would be quite dynamic, so there would have to be, let's say, five years of history to cover Airbnb's "black holes" of cancellations.
Once the heat index of a community is calculated, the said number cruncher could determine if there was a correlation between the heat index and the propensity of local regulations. Even whether the destination is under- or over-ripe for investment. Vacasa should be employing something like this.
Sounds like a doctoral thesis for a statistician? Or maybe there's a much easier way?