diff --git a/transform/sqlmesh_padelnomics/models/foundation/dim_locations.sql b/transform/sqlmesh_padelnomics/models/foundation/dim_locations.sql
index 015bb28..b0ddbe3 100644
--- a/transform/sqlmesh_padelnomics/models/foundation/dim_locations.sql
+++ b/transform/sqlmesh_padelnomics/models/foundation/dim_locations.sql
@@ -215,7 +215,7 @@ SELECT
   l.location_slug,
   l.lat,
   l.lon,
-  h3_latlng_to_cell(l.lat, l.lon, 4)                                    AS h3_cell_res4,
+  h3_latlng_to_cell(l.lat, l.lon, 5)                                    AS h3_cell_res5,
   l.admin1_code,
   l.admin2_code,
   l.population,
diff --git a/transform/sqlmesh_padelnomics/models/serving/location_profiles.sql b/transform/sqlmesh_padelnomics/models/serving/location_profiles.sql
index be5edb3..e91c97d 100644
--- a/transform/sqlmesh_padelnomics/models/serving/location_profiles.sql
+++ b/transform/sqlmesh_padelnomics/models/serving/location_profiles.sql
@@ -20,7 +20,7 @@
 --   "Where should I build a padel court?"
 --   Computed for ALL locations — zero-court locations score highest on supply gap.
 --   H3 catchment methodology: addressable market and supply gap use a regional
---   H3 catchment (res-4 cell + 6 neighbours, ~462km², ~15-18km radius).
+--   H3 catchment (res-5 cell + 6 neighbours, ~24km radius).
 --
 --   25 pts  addressable market — log-scaled catchment population, ceiling 500K
 --   20 pts  economic power     — income PPS, normalised to 35,000
@@ -63,30 +63,30 @@ base AS (
     l.padel_venues_per_100k,
     l.nearest_padel_court_km,
     l.tennis_courts_within_25km,
-    l.h3_cell_res4
+    l.h3_cell_res5
   FROM foundation.dim_locations l
 ),
--- Aggregate population and court counts per H3 cell (res 4, ~10km edge).
--- Grouping by cell first (~30-50K distinct cells vs 140K locations) keeps the
+-- Aggregate population and court counts per H3 cell (res 5, ~8.5km edge).
+-- Grouping by cell first (~50-80K distinct cells vs 140K locations) keeps the
 -- subsequent lateral join small.
 hex_stats AS (
   SELECT
-    h3_cell_res4,
+    h3_cell_res5,
     SUM(population)        AS hex_population,
     SUM(padel_venue_count) AS hex_padel_courts
   FROM foundation.dim_locations
-  GROUP BY h3_cell_res4
+  GROUP BY h3_cell_res5
 ),
 -- For each location, sum hex_stats across the cell + 6 neighbours (k_ring=1).
--- Effective catchment: ~462km², ~15-18km radius — realistic driving distance.
+-- Effective catchment: ~24km radius — realistic driving distance.
 catchment AS (
   SELECT
     l.geoname_id,
     SUM(hs.hex_population)   AS catchment_population,
     SUM(hs.hex_padel_courts) AS catchment_padel_courts
   FROM base l,
-  LATERAL (SELECT UNNEST(h3_grid_disk(l.h3_cell_res4, 1)) AS cell) ring
-  JOIN hex_stats hs ON hs.h3_cell_res4 = ring.cell
+  LATERAL (SELECT UNNEST(h3_grid_disk(l.h3_cell_res5, 1)) AS cell) ring
+  JOIN hex_stats hs ON hs.h3_cell_res5 = ring.cell
   GROUP BY l.geoname_id
 ),
 -- Match dim_cities via (country_code, geoname_id) to get city_slug + exact venue count.