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Compare what your current rent gets you in other cities

Hacker News

Compare what your current rent gets you in other cities

I built a tool to compare your apartment’s rent against your zip code's average and see what that same budget buys you in other U.S. cities. The current version uses 2023 US Census Data (the 2024 one seems incomplete). While the data is a bit dated, it still provides interesting benchmarks for relative cost-of-living comparisons. I’m currently looking into integrating real-time rental forecast APIs to improve accuracy for major metropolitan areas. I had a lot of fun building this and got to use a bunch of new tech doing it! I’d love to hear your feedback and if there are any features you think would make this better. Thank you!! data source: https://data.census.gov/table?q=B25031

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
64%64% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: new, code, apis · Missing: mac, agents, macos
59%59% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, 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.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
13%13% 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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