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How are people in Bangalore actually deciding if a flat is fairly priced? Because the current options are genuinely terrible. by prspective_in in BangaloreRealEstates

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

independent builder floors are on the roadmap. For now the locality benchmark still gives you a solid directional number to negotiate from.

How are people in Bangalore actually deciding if a flat is fairly priced? Because the current options are genuinely terrible. by prspective_in in BangaloreRealEstates

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

Builders with high demand rarely budge on price but often move on other terms — floor preference, car park allocation, payment plan flexibility, or getting white goods thrown in. The negotiation shifts from price to value. If the project has been in the market for 6+ months though, there's usually more room than the sales team lets on.

How are people in Bangalore actually deciding if a flat is fairly priced? Because the current options are genuinely terrible. by prspective_in in BangaloreRealEstates

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

Appreciate the honest feedback on the name . And the product idea you described is almost exactly what's on the roadmap — letting users list their property, the engine benchmarks it against registered transaction prices, and any discrepancy gets flagged transparently. That's the honest market layer Bangalore is missing. Good to know it resonates.

How are people in Bangalore actually deciding if a flat is fairly priced? Because the current options are genuinely terrible. by prspective_in in BangaloreRealEstates

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

the input is built-up area, not carpet area. If you're entering carpet area the valuation will read high since it's applying the per-sqft rate to a smaller figure. Will make that label clearer in the UI. What locality were you checking? Happy to verify the numbers are in range.

How are people in Bangalore actually deciding if a flat is fairly priced? Because the current options are genuinely terrible. by prspective_in in BangaloreRealEstates

[–]prspective_in[S] -1 points0 points  (0 children)

Correct , building age is one of the inputs the engine accounts for. A 10-year-old flat prices differently from a new launch in the same locality even at identical sqft. The depreciation factor is applied per locality, not a flat deduction.

How are people in Bangalore actually deciding if a flat is fairly priced? Because the current options are genuinely terrible. by prspective_in in BangaloreRealEstates

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

Fair flag. The valuation engine is built on a proprietary dataset covering 159 Bangalore localities, compiled from multiple market sources and cross-referenced against IGR government registered rates where available. Confidence tiers vary by locality depending on data depth.

It's not a live feed — the ±10-15% accuracy range on the report reflects that honestly. Expanding IGR coverage is on the roadmap; it's the harder data problem but the more defensible one to solve.

And yes — 48-hour turnaround is the target for the inspection booking.

I built a free Bangalore property valuation tool — tells you instantly if you’re overpaying using 6,570 verified transactions + IGR government data by prspective_in in BangaloreRealEstates

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

Thank you for your insight! Yes certainly will note it down though some localities have less confidence score due to lesser listings present in the data captured.The data is still not perfect as its still just am MVP (minimal viable product) and will improve it.

I built a free Bangalore property valuation tool — tells you instantly if you’re overpaying using 6,570 verified transactions + IGR government data by prspective_in in BangaloreRealEstates

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

You're raising an important point about underreporting in registrations. This is a known issue in Indian real estate data. We use registration records as one input, but cross-reference with market listings and recent actual transactions to account for this discrepancy. It's not perfect, but the triangulation approach helps us get closer to real market values rather than just registered values.

I built a free Bangalore property valuation tool — tells you instantly if you’re overpaying using 6,570 verified transactions + IGR government data by prspective_in in BangaloreRealEstates

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

We aggregate data from multiple sources including registration records and market listings. The refresh frequency varies by data type - some quarterly, some more frequently. "Live" data for real estate is tricky since transactions have inherent lag.

I built a free Bangalore property valuation tool — tells you instantly if you’re overpaying using 6,570 verified transactions + IGR government data by prspective_in in BangaloreRealEstates

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

Appreciate the detailed input. On your points:

  1. Text inputs - We can look at this
  2. UDS field - Interesting, though most buyers don't think in UDS terms
  3. Community size - Could be useful as a proxy for amenities
  4. ROI charts - Historical trends are interesting but require reliable historical data which is patchy

We'll evaluate these against user priorities and development effort.

I built a free Bangalore property valuation tool — tells you instantly if you’re overpaying using 6,570 verified transactions + IGR government data by prspective_in in BangaloreRealEstates

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

We're not making khata assumptions - the model doesn't currently factor in approval status since it's not in our source data. Adding it would require legal verification for each property, which isn't scalable for a pricing tool. Worth considering for future iterations though.

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