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Rentalytics – Find rental property deals that actually make money

Hacker News

Rentalytics – Find rental property deals that actually make money

Hi HN, I’m a software engineer and real estate investor. I grew tired of scrolling through property listings and manually analyzing each one just to figure out which of them are actually profitable as rental properties. This was especially tiring as only a few properties ever end up being worth the trouble. So I built Rentalytics — a system that automatically pulls active listings in a market, runs the numbers, and ranks them by: - Cash flow - Cash-on-Cash Return - Debt Service Coverage Ratio (DSCR) For the proof of concept, I started with Columbia, SC. Last week: out of 1152 active listings, only 85 were worth considering. The idea is to expand city by city (Charlotte, Raleigh, Greenville are next), while improving the metrics and UX. Right now: Free = weekly “Top 5 Deals” email Pro = weekly full ranked spreadsheet https://www.rentalytics.fyi/demo Would love feedback from HN on: - Other metrics worth automating? - CSVs vs dashboards — which is more useful to you? - How should I prioritize which cities to add next? - Any cities you'd especially like to see as an investor? Thanks!

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best fitHighest predicted score across all platforms for this description.
TrustMRRLess likely to generate early MRR · Strong signals: profitable · 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
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
37%37% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Strong signals: email · Missing: mac, agents, macos
29%29% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: profit, profitable, active · Missing: arr, mrr, revenue
25%25% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: make money · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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