diff --git a/backend/app.py b/backend/app.py
index d0f5dcd..49368b2 100644
--- a/backend/app.py
+++ b/backend/app.py
@@ -8,6 +8,7 @@ import re
 from contextlib import asynccontextmanager
 from typing import Optional
 
+import numpy as np
 import pandas as pd
 from fastapi import FastAPI, HTTPException, Query, Request, Depends, Header
 from fastapi.middleware.cors import CORSMiddleware
@@ -21,7 +22,6 @@ from starlette.middleware.base import BaseHTTPMiddleware
 from .config import settings
 from .data_loader import (
     clear_cache,
-    haversine_distance,
     load_school_data,
     geocode_single_postcode,
 )
@@ -276,17 +276,29 @@ async def get_schools(
             schools_df = schools_df.copy()
 
             # Filter by distance using pre-geocoded lat/long from database
-            def calc_distance(row):
-                if pd.isna(row["latitude"]) or pd.isna(row["longitude"]):
-                    return float("inf")
-                return haversine_distance(
-                    search_coords[0],
-                    search_coords[1],
-                    row["latitude"],
-                    row["longitude"],
-                )
+            # Use vectorized haversine calculation for better performance
+            lat1, lon1 = search_coords
+            lat2 = schools_df["latitude"].values
+            lon2 = schools_df["longitude"].values
 
-            schools_df["distance"] = schools_df.apply(calc_distance, axis=1)
+            # Vectorized haversine formula
+            R = 3959  # Earth's radius in miles
+            lat1_rad = np.radians(lat1)
+            lat2_rad = np.radians(lat2)
+            dlat = np.radians(lat2 - lat1)
+            dlon = np.radians(lon2 - lon1)
+
+            a = np.sin(dlat / 2) ** 2 + np.cos(lat1_rad) * np.cos(lat2_rad) * np.sin(dlon / 2) ** 2
+            c = 2 * np.arctan2(np.sqrt(a), np.sqrt(1 - a))
+            distances = R * c
+
+            # Handle missing coordinates
+            distances = np.where(
+                pd.isna(schools_df["latitude"]) | pd.isna(schools_df["longitude"]),
+                float("inf"),
+                distances
+            )
+            schools_df["distance"] = distances
             schools_df = schools_df[schools_df["distance"] <= radius]
             schools_df = schools_df.sort_values("distance")