Af

AffordWhere – Affordability by City

Hacker News

AffordWhere – Affordability by City

Hi HN, I built AffordWhere to answer a question I kept running into: “If I earn X, where can I actually afford to live comfortably?” The site lets you compare cities based on income, rent, taxes, and day-to-day costs, and see how far a salary realistically goes in each place. It’s intentionally simple: Focused on affordability rather than averages Designed for relocation and remote-work decisions No account required I’d appreciate feedback on: Whether the affordability framing is useful Missing cost factors you’d expect Data accuracy or methodology concerns Link: https://affordwhere.com Thanks for taking a look.

Visit product View on Hacker NewsJan 26, 2026

Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

42%42% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

24%24% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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