diff --git a/backend/pricing-engine/src/analysis/regression.ts b/backend/pricing-engine/src/analysis/regression.ts
index b23629f9..2b22e023 100644
--- a/backend/pricing-engine/src/analysis/regression.ts
+++ b/backend/pricing-engine/src/analysis/regression.ts
@@ -2,6 +2,7 @@ import { PricingTier, RideSample, RegressionResult } from './types';
 import {
   twoStageRegression,
   robustMultipleLinearRegression,
+  simpleDistanceModel,
   calcRMSE,
   calcRSquared,
   detectMinimumFare,
@@ -78,17 +79,29 @@ export function analyzeTier(tier: PricingTier): PricingTier {
       price:        s.price,
     }));
 
-  // Run both models
-  const modelA = twoStageRegression(input);        // Stage 1: flag fall | Stage 2: km + min
-  const modelB = robustMultipleLinearRegression(input);  // Current robust approach
+  // Run all three models
+  const modelA = twoStageRegression(input);              // Stage 1: flag fall | Stage 2: km + min
+  const modelB = robustMultipleLinearRegression(input);  // Iterative outlier removal
+  const modelC = simpleDistanceModel(input);             // distance-only: price = k × dist
 
   const best = selectBestModel(modelA, modelB, samples);
-  if (!best) {
+
+  // Distance-only fallback: if it's within 10% of the best model, prefer it
+  let finalModel = best;
+  if (finalModel && modelC) {
+    const rmseBest = evalRMSE(finalModel.model, samples);
+    const rmseDist = evalRMSE(modelC, samples);
+    if (rmseDist <= rmseBest * 1.10) {
+      finalModel = { model: modelC, name: 'distance-only' };
+    }
+  }
+
+  if (!finalModel) {
     tier.regression = null;
     return tier;
   }
 
-  const { model: mlrResult, name: modelName } = best;
+  const { model: mlrResult, name: modelName } = finalModel;
 
   // Compute final metrics using the winning model
   const actualPrices    = samples.map(s => s.price);
diff --git a/backend/pricing-engine/src/utils/math.ts b/backend/pricing-engine/src/utils/math.ts
index 3fd1cfb9..a2c55625 100644
--- a/backend/pricing-engine/src/utils/math.ts
+++ b/backend/pricing-engine/src/utils/math.ts
@@ -209,6 +209,30 @@ export function twoStageRegression(
   return { baseFare, kmRate: rates.kmRate, minRate: rates.minRate };
 }
 
+/**
+ * Simple distance-only model: price = kmRate × dist
+ * Returns null if data is degenerate.
+ */
+export function simpleDistanceModel(
+  samples: Array<{ distance_km: number; duration_min: number; price: number }>
+): { baseFare: number; kmRate: number; minRate: number } | null {
+  const dists  = samples.map(s => s.distance_km);
+  const prices = samples.map(s => s.price);
+
+  // Mean of price/distance ratios, weighted by distance
+  let sumRatio = 0, count = 0;
+  for (let i = 0; i < dists.length; i++) {
+    if (dists[i] > 0 && prices[i] > 0) {
+      sumRatio += prices[i] / dists[i];
+      count++;
+    }
+  }
+  if (count < 3) return null;
+
+  const kmRate = Math.round((sumRatio / count) * 1000) / 1000;
+  return { baseFare: 0, kmRate, minRate: 0 };
+}
+
 // ─────────────────────────────────────────────
 // Robust regression (iterative outlier removal)
 // ─────────────────────────────────────────────