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Calculate mortgage repayments on the fly while browsing Zillow/Trulia

Hacker News

Calculate mortgage repayments on the fly while browsing Zillow/Trulia

Hi HN, I made Chrome/Firefox/Edge extension that calculates your (potential) mortgage repayments while browsing listings on Zillow/Trulia/Realestate.com.au/Domain.com.au The Firefox extension can be found here: https://addons.mozilla.org/sv-SE/firefox/addon/real-estate-b... I realize most people go to their banks first to get a pre-approval and see how much they can borrow and afford but not everyone is like that. Personally, a big number such as $500000 or $700000 doesn't really speak to me. However if I see something like $634/wk then I can clearly see if I can afford a certain property or not. Sometimes I like to browse real estate websites and see how much a particular home would cost me on a weekly/bi-weekly/monthly basis. Most real estate websites allow you to simulate your repayments but it's a manual process and I really wanted to be able to browse while seeing the potential loan repayments. For the Australian users out there, there are often prices missing on the listings of https://domain.com.au and https://realestate.com.au , in this case I use the marketing range that the websites expose to calculate the repayments. The extension also tries to highlight which property would be ideal for you based on your deposit and loan interest rate. The extension stores your data on local storage so nothing is ever sent or stored anywhere else. No need to sign up or create an account either. No analytics whatsoever. I hope you enjoy it and any feedback is welcome.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
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best fitHighest predicted score across all platforms for this description.
TrustMRRFits verified-revenue profile · Strong signals: personal, month, monthly · Missing: mobile apps, ios, entrepreneurs
64%64% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
43%43% 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, 000, io · Missing: https docs, excited, just released
36%36% 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: user · Missing: mac, agents, macos
22%22% 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
16%16% 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.

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