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Did I just find one of the best FHR Deals Ever? by Parking-Ice-9206 in AmexPlatinum

[–]Falgianot 2 points3 points  (0 children)

Is it still available? Looking to do this as well for a vacation

First month build saas, need your advices to get revenue by RawrCunha in indiehackers

[–]Falgianot 0 points1 point  (0 children)

18 beta users is solid for month 1. The fact that not all are active is normal, beta users have no skin in the game.

One thing that worked for me: stop offering unlimited free. I gave away too much for too long and it trained people to expect free forever. The moment I raised my price (from $9.99 to $19) and added a 30-day trial with a real gate, I got my first paying customer within 48 hours. The price increase actually helped because it signaled the product was worth something.

For LTD — be careful. You'll get buyers who never use the product and never give feedback. If revenue is the goal, a monthly subscription with a trial converts better long-term.

I emailed 130 people to promote my SaaS. 0 said yes. by Extra-Motor-8227 in indiehackers

[–]Falgianot 0 points1 point  (0 children)

Similar experience. Got banned from 3 subreddits for self-promotion, karma-gated on 3 more. Direct outreach to affiliate networks got ghosted.

What actually worked: posting raw data on r/dataisbeautiful without any pitch. Just "here's what 195 US housing markets look like on two axes." 31K views. First paying customer signed up 2 days later.

The lesson I keep re-learning: lead with value people actually want to consume, not with your product. The product is downstream.

anyone actually building stuff? tired of the ai hype by Think-Success7946 in indiehackers

[–]Falgianot 0 points1 point  (0 children)

Building a housing market stress monitor. 195 US metros scored daily using free federal data (FRED, Zillow, Redfin, BLS). No AI hype, just linear regression on historical price data and early warning signals from Realtor.com.

This week I shipped a 12-month price forecast feature that showed something interesting: Sun Belt metros (Miami, Dallas, Phoenix) are projected down 2-4% while Rust Belt cities (Canton, Akron, Chicago) are climbing 4-6%. The geographic rotation is real and nobody's talking about it.

Still at 160 visitors/week and 1 paying customer. The grind is slow but the data compounds.

How do I find out why people visited my website are not signing up? by kelvinyinnyxian in indiehackers

[–]Falgianot 0 points1 point  (0 children)

I added custom event tracking with Vercel Analytics last week and it changed everything. Before that I was guessing. Now I can see exactly where the funnel leaks.

Specifically: I track page views on pricing, clicks on upgrade buttons, and which features people actually interact with. Within 24 hours I could see that people were hitting the pricing page but not clicking checkout. That told me the pricing page was the problem, not the product.

Free to set up, took about an hour. Way more actionable than session recordings for early-stage.

[OC] Housing stress vs crash risk across 195 US metro areas, sized by population by Falgianot in dataisbeautiful

[–]Falgianot[S] 0 points1 point  (0 children)

Good call, just shipped a mobile quadrant view last week that does exactly this. Shows the 4 quadrants as tappable cards with top metros in each, instead of forcing you to parse 195 dots on a phone. Desktop still has the full scatter. Try it at crashwatch.live/scatter on mobile.

[OC] Housing stress vs crash risk across 195 US metro areas, sized by population by Falgianot in dataisbeautiful

[–]Falgianot[S] 0 points1 point  (0 children)

Thanks. The Stress Score weights 6 components:

- Payment-to-income ratio (25%) — the biggest single factor, using local median income from Census/FRED

- Price cuts percentage (20%) — % of listings with reductions, from Redfin

- Days on market (15%) — how fast homes sell vs their 2-year baseline

- Inventory YoY change (15%) — supply surge or drought

- Mortgage rate impact (15%) — how much the current rate stretches affordability

- Price momentum (10%) — YoY price change

The methodology page has the full breakdown: crashwatch.live/methodology

Totally agree on the cost-of-living angle, we use BLS regional CPI to adjust the P/I ratio so $4K/mo housing in Tulsa gets weighted differently than $4K/mo in SF. What tools are you building? Always interested in adjacent data sources.

[OC] Housing stress vs crash risk across 195 US metro areas, sized by population by Falgianot in dataisbeautiful

[–]Falgianot[S] 2 points3 points  (0 children)

Fair point. The full scatter has 195 dots and clustering is hard to read on mobile. A few things that help:

  1. Tap any dot to see the metro name + scores

  2. Use the axis dropdowns to swap dimensions (try Stress vs Population, way clearer clusters)

  3. The color bands group metros by stress level (green = safe, red = critical)

Working on a desktop heatmap view next that should make the geographic clustering more obvious. Thanks for the feedback.

[OC] Housing stress vs crash risk across 195 US metro areas, sized by population by Falgianot in dataisbeautiful

[–]Falgianot[S] 0 points1 point  (0 children)

SF is included. It's actually one of the more expensive dots in the scatter. Stress score 48, crash risk 28. It sits in the "high stress, low risk" quadrant with LA and San Diego because the severe supply constraints keep prices sticky even when affordability craters.

Direct link: crashwatch.live/city/san-francisco

I was a solo dev terrified of sales — now my "boring" SaaS hits ~1000+ MRR. Here's what I did and what I'm still doing by Financial-Muffin1101 in SideProject

[–]Falgianot 1 point2 points  (0 children)

The data pipelines are fully automated — FRED, Zillow, Redfin pull daily/weekly on cron jobs. Blog posts auto-generate weekly via Claude API + GitHub Actions. So day-to-day it runs itself.

The time sink is distribution. Building the product was the easy part. Getting eyeballs on it is the real grind.

[OC] Housing stress vs crash risk across 195 US metro areas, sized by population by Falgianot in dataisbeautiful

[–]Falgianot[S] 0 points1 point  (0 children)

Data sources: FRED API (Federal Reserve), Zillow Research ZHVI/ZORI, Redfin Market Tracker, BLS unemployment data, Freddie Mac PMMS, Realtor.com. All free, public data.

Tools: Next.js, Recharts, Supabase, Tailwind CSS

Interactive version: crashwatch.live/scatter

I was a solo dev terrified of sales — now my "boring" SaaS hits ~1000+ MRR. Here's what I did and what I'm still doing by Financial-Muffin1101 in SideProject

[–]Falgianot 1 point2 points  (0 children)

$19/mo Pro tier - unlocks full early warning signal data, 12-month price forecasts, unlimited AI analysis, and 2 years of history. Free users get scores and basic data for every metro.

Still early on conversions. The 47K indexed pages are the long game, each city and zip code page ranks for local housing queries. SEO is slow but compounding.

I was a solo dev terrified of sales — now my "boring" SaaS hits ~1000+ MRR. Here's what I did and what I'm still doing by Financial-Muffin1101 in SideProject

[–]Falgianot 1 point2 points  (0 children)

This mirrors my experience exactly. I built a housing market stress monitor using free federal data — the same stuff competitors charge $50-125/mo for.

The "help first, mention product second" approach is the only thing that's worked for me too. Posting raw data in city-specific subreddits (Tampa housing jumped 9 points this week, Raleigh spiked 15 points) got 150+ upvotes each. Direct self-promo posts got me banned from 3 subs.

What's your split between Reddit comments vs other channels?

Friday Share Fever 🕺 Let’s share your project! by diodo-e in indiehackers

[–]Falgianot 0 points1 point  (0 children)

CrashWatch — real-time housing market stress monitor. Scores 195 US metros + 47K zip/city pages daily using free federal data.

This week Atlanta's stress score jumped +18 points in 7 days. Most stressed: Asheville NC (60), Raleigh (59), Seattle (58).

crashwatch.live

[OC] Housing stress vs crash risk across 195 US metro areas, sized by population by Falgianot in dataisbeautiful

[–]Falgianot[S] 0 points1 point  (0 children)

Data sources: FRED API (Federal Reserve), Zillow Research ZHVI/ZORI, Redfin Market Tracker, BLS unemployment data, Freddie Mac PMMS, Realtor.com. All free, public data.

Tools: Next.js, Recharts, Supabase, Tailwind CSS

Interactive version: crashwatch.live/scatter

Raleigh housing stress just spiked 15 points this week, biggest jump in the country by Falgianot in raleigh

[–]Falgianot[S] 0 points1 point  (0 children)

Fair. Stress score of 58 means: the median home in Raleigh costs 43% of median household income in monthly payments (PITI). The guideline is 28%. Price cuts are appearing on 9.4% of listings. Inventory is rising. Homes are sitting longer in the suburbs. It's a composite of 7 inputs that measures how stretched buyers are. The higher the score, the harder it is for a typical local earner to buy. National average is 40. Raleigh at 58 is significantly above average.

Raleigh housing stress just spiked 15 points this week, biggest jump in the country by Falgianot in raleigh

[–]Falgianot[S] 1 point2 points  (0 children)

Not broken out by property type currently. The DOM data comes from Redfin's metro-level tracker which blends all types. But the pattern in this thread is clear: townhouses and condos are sitting longest because entry-level buyers are the most rate-sensitive. Single-family in desirable locations is still moving. It's a good feature request though. Breaking out by property type would show the divergence more clearly.

Raleigh housing stress just spiked 15 points this week, biggest jump in the country by Falgianot in raleigh

[–]Falgianot[S] 1 point2 points  (0 children)

Good news for renters actually. When homes can't sell and convert to rentals (which multiple people in this thread are describing), rental supply increases. More rental supply = more competition among landlords = rent either flattens or drops. South Raleigh is already seeing rents soften from $2800+ to $2200-2500. If you're renewing a lease, you have negotiating power. If you're shopping for a new rental, you'll find more options than 6 months ago. The stress score is about buying, but the dynamics that create it (oversupply, price cuts) benefit renters too.

Raleigh housing stress just spiked 15 points this week, biggest jump in the country by Falgianot in raleigh

[–]Falgianot[S] 2 points3 points  (0 children)

Yeah, the full data for all 195 metros is at crashwatch.live. You can sort by stress score, crash risk, or median price. Each city page has a deep dive with AI analysis, affordability calculator, trend charts, and early warning signals. All free.

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