AFFORDABLE (AFFORDABLE) Fair Value & Analysis
Industrials · Market cap ₹2.0B
Fair value as of: Jul 3, 2026
From 12 valuation models · updated today
Share price −7.3% over the past month.
Price vs Fair Value (12 months)
12‑month range ₹120.87 – ₹523.15 · fair‑value band ₹70.50 – ₹121.49 · the ₹172.36 price screens above the ₹93.46 fair value. As of Jul 3, 2026.
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AFFORDABLE (AFFORDABLE) currently trades at ₹172.36, while our model-based Fair Value estimate is ₹93.46 — implying the stock looks roughly 45.8% overvalued today. We read business quality at 35/100 (below-average quality), in the Industrials sector. Bear case: priced above our estimate, the market already discounts strong expectations. Bull case: above-average quality can justify a premium — the entry price still matters most (evidence: medium).
Over the trailing twelve months, AFFORDABLE generated revenue of ₹1.2B at a net margin of 5.9%. Revenue declined 39.7% year over year. It earns a return on equity of 6.6%. Net debt stands at ₹618M. Fundamentals as of Jul 3, 2026
Key figures & financial health
More key figures
Figures from reported company fundamentals (EODHD) · as of Jul 3, 2026. TTM = trailing twelve months.
Revenue & earnings trend
FY2022 – FY2026 · reported fiscal years
AFFORDABLE reported revenue of ₹1.2B in FY2026 versus ₹807M in FY2022, a compound +9.9%/yr. Reported net income was ₹69.7M in FY2026, compounding +31.0%/yr from FY2022.
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Similar stocks
6 more Specialty Industrial Machinery stocks, each showing price versus our Fair Value estimate (as of Jul 3, 2026).
| Stock | Price | Fair Value | vs Fair Value |
|---|---|---|---|
| GE Vernova Inc GEV | $1,128 | $185.61 | -84% |
| SIEGY SIEGY | $155.10 | $88.93 | -43% |
| SMAWF SMAWF | $307.12 | $176.28 | -43% |
| SMNS SMNS | C$31.76 | C$22.91 | -28% |
| SIE SIE | €258.40 | €144.67 | -44% |
| Schneider Electric S.E. SBGSF | $334.00 | $148.44 | -56% |
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How we calculate Fair Value
Each company is valued through a stack of independent intrinsic-value models (DCF variants, residual-income, multiples and more), blended into one family-balanced consensus and weighted by how much trustworthy data backs it. A separate quality layer scores the fundamentals. Every input is real reported data — nothing guessed.
Educational research only · not financial advice · no buy/sell recommendation. Model-based estimates are not certainties; their reliability depends on data quality and assumptions.