Kyle Piira

Tag: finance

  • The Case for Prediction Markets (Even If People Hate Them)

    I’ve been thinking a lot about prediction markets lately. And honestly, I keep running into people who really don’t like them. Not just mild skepticism, more like “these should be banned” or even outright illegal. That reaction kind of bums me out.

    I’ve been into the idea for a long time, even before there were any big platforms. Back when it was mostly just theoretical, before Polymarket, Kalshi, all that, I thought it was a really elegant concept. It just made sense. We already know markets can be pretty good at aggregating information. The stock market, for example, does a decent job pricing in everything people know (or think they know), and that usually leads to pretty accurate valuations. Way better than small private markets where fewer people are involved and less information gets reflected.

    So if you start from the basic problem, “we want to predict the future,” it feels natural to look for systems that are good at combining lots of information. Prediction markets fit that really well. They’re kind of like a general-purpose forecasting tool. You can point them at almost anything.

    One example I really like is weather markets. Kalshi has some live ones where you can predict things like temperature or whether it’ll rain on a specific day. That’s a case where prediction markets actually feel useful in a very direct way. Everyone cares about the weather. You’re planning a weekend, a trip, even just tomorrow, having a solid sense of what’s coming matters.

    Sure, you can check a weather app or watch the forecast. And they’re usually fine. But the people trading in a weather prediction market are pulling from all of that too, plus their own models, their own insights, maybe even niche data sources. They’re constantly updating their beliefs because there’s money on the line. And that pressure forces information into the price.

    Over time, the people who are actually good at predicting, like really good, end up with more influence because they win more and can trade more. So the market kind of self-selects for accuracy. The end result is a price that reflects a very informed, constantly updated probability. In theory, it’s about as good an estimate as you can get.

    That’s kind of amazing, if you think about it. A single number that summarizes everything people collectively know (and believe) about some future event. It feels like a genuinely powerful tool. Which is why it’s frustrating to see people want to shut it down entirely, it feels like throwing away something valuable.

    Now, to be fair, there are criticisms.

    One that comes up a lot is insider trading. People don’t like the idea that someone with privileged information could profit off it. And yeah, I get why that feels unfair, especially since we regulate that heavily in stock markets.

    But prediction markets aren’t really about fairness in that sense. They’re not trying to give everyone an equal shot. If that’s the goal, there are plenty of games for that, go play roulette or something. The whole point here is accuracy.

    From that perspective, insider trading isn’t a flaw, it’s kind of the point. If someone knows something the market doesn’t yet reflect, you want them to trade on it. That’s how the information gets incorporated into the price. It actually makes the market better as a forecasting tool.

    Another criticism is that this is basically just gambling with extra steps. Especially with things like sports or politics. And yeah, I think there’s something to that. Gambling can be a real problem for some people, and a lot of these markets, especially sports, do look a lot like betting.

    Honestly, I don’t think sports prediction markets add much value. Predicting who wins a game doesn’t really matter in any meaningful sense. It’s entertainment. So if people want to clamp down there, I’m not too bothered by that.

    Politics is a bit more nuanced. There’s arguably some real value in forecasting elections. Markets can sometimes pick up on signals that polls miss. But even then, I could see the case for tighter rules or guardrails.

    Where I think we’d really lose something is in the more practical, information-heavy markets, like weather, economic indicators, maybe even things like supply chain risks or disease outbreaks. Those feel genuinely useful. They help people make decisions.

    So yeah, I don’t think prediction markets are perfect. But as a tool for aggregating information and forecasting the future, they’re kind of incredible. It would be a shame to throw that away entirely.

  • I’m Done With Bilt

    Bilt dropped details yesterday about their new credit cards. I’ve been using the original Bilt card for a little over a year, mostly for one very specific reason, and after reading through the announcement I’m pretty confident I won’t be switching to any of the new ones.

    I’ll probably just close the account at the end of the month and go back to paying rent directly out of my bank account.

    The original Bilt card worked because it did one thing unusually well. It let you pay rent with no fee and earn points on it. You got an account number and routing number, gave that to your landlord, and rent came out like it was a checking account. One point per dollar on rent. Simple.

    My rent is about $2,000 a month, so that came out to roughly 2,000 points every month. That’s not a ton of money, maybe $20 in value, but it was enough to matter in small ways. I mostly used the points for Lyft rides. Not flights, not aspirational travel redemptions, just “cool, this ride is free.” A couple of those a month was nice. It felt like getting something back for an expense that otherwise just disappears.

    There was a catch, though. You had to make at least five non-rent transactions per month to earn the rent points. And I never wanted to actually use the Bilt card for real spending. It wasn’t competitive with my other cards, and I didn’t feel like thinking about it.

    So I did what a lot of people probably did. I gamed it. I put five recurring charges on the card: iCloud storage for 99 cents, a few other subscriptions in the $5 to $10 range, and called it a day. Total monthly spend outside of rent was maybe $30. Rent was thousands. Points flowed.

    From Bilt’s perspective, I was almost certainly a terrible customer.

    Which is why none of this is surprising.

    The new cards are clearly designed to stop people from using the product the way I was using it. Under the new setup, if you want to earn points on rent, you need to spend a lot more elsewhere on the card. Roughly 75% of your rent amount, from what I can tell. If your rent is $2,000, you need to put about $1,500 of other spending on the card every month.

    That’s where it completely falls apart for me.

    I’m not interested in rerouting $1,500 a month away from cards I already like just to preserve a rent reward setup that used to be effortless. Five token transactions was annoying but manageable. Rebuilding my entire spending strategy around one card is not.

    And honestly, that’s fine. This feels very intentional. Bilt doesn’t want people who do the bare minimum, harvest rent points, and disappear. I was exactly that person. I don’t blame them for tightening things up.

    But it does mean I’m done.

    The new cards might be great for people who want a primary spending card and like the Bilt ecosystem. I’m not that person. I just wanted the rent thing to keep quietly working in the background, and it no longer does.

    So I’ll take the small loss and go back to paying rent the old-fashioned way. No points, no Lyft credits, no Wells Fargo relationship I didn’t really want in the first place. It was fun while it lasted.

    Adiós, viejo amigo.

  • The relationship between college enrollment and tuition

    I spent my evening determining a reasonable expected growth rate for future college tuition. The graph below displays the correlation between college attendance rates and tuition. While I’m not concluding causation from the data, it seems plausible that increased enrollment rates have led to higher tuition costs. With enrollment now peaking at around 100% and appearing to decline, I anticipate a lower future growth rate. The historical growth rate was approximately 6%, but I’m considering a rate closer to 2% going forward.

    In the data attendance rate is defined as the number of people enrolled in college divided by the number of people aged 18-22 in the United States.