diff --git a/changelog.d/need_gross_income.fixed.md b/changelog.d/need_gross_income.fixed.md
new file mode 100644
index 00000000..e44c3b92
--- /dev/null
+++ b/changelog.d/need_gross_income.fixed.md
@@ -0,0 +1 @@
+Use gross household income (LCFS P344p / FRS household_gross_income) instead of HBAI net income when assigning households to NEED 2023 income bands for energy consumption calibration. NEED uses Experian modelled gross income, so the previous use of net income misallocated households across bands.
diff --git a/policyengine_uk_data/datasets/imputations/consumption.py b/policyengine_uk_data/datasets/imputations/consumption.py
index bd77973e..d662f110 100644
--- a/policyengine_uk_data/datasets/imputations/consumption.py
+++ b/policyengine_uk_data/datasets/imputations/consumption.py
@@ -13,6 +13,9 @@
   and demographics, matching the strong drivers in NEED admin data.
 - Imputed totals are calibrated to NEED 2023 mean kWh targets by income band,
   converted to spend using Ofgem Q4 2023 unit rates (Oct 2023 price cap).
+  NEED income bands use Experian modelled gross household income, so calibration
+  matches against gross income (LCFS P344p / FRS household_gross_income) rather
+  than HBAI net income.
 """
 
 import pandas as pd
@@ -75,6 +78,7 @@
     "G019": "is_child",
     "Gorx": "region",
     "P389p": "hbai_household_net_income",
+    "p344p": "household_gross_income",
     "weighta": "household_weight",
 }
 PERSON_LCF_RENAMES = {
@@ -146,6 +150,7 @@
 OFGEM_Q4_2023_GAS_RATE = 6.89 / 100  # £/kWh (Oct 2023 price cap)
 
 # NEED 2023 mean kWh by income band (Table 11b gas, Table 12b electricity)
+# Income bands are gross household income (Experian modelled data)
 NEED_INCOME_BANDS = [
     (0, 15_000, "under_15k", 7_755, 2_412),  # gas kWh, elec kWh
     (15_000, 20_000, "15k_20k", 9_196, 2_700),
@@ -336,11 +341,14 @@ def _derive_energy_from_lcfs(household: pd.DataFrame) -> pd.DataFrame:
 
 
 def _calibrate_energy_to_need(
-    household: pd.DataFrame, income_col: str = "hbai_household_net_income"
+    household: pd.DataFrame, income_col: str = "household_gross_income"
 ) -> pd.DataFrame:
     """
     Rescale imputed electricity and gas spend to match NEED 2023 income-band means.
 
+    NEED 2023 income bands use Experian modelled gross household income, so we
+    match against gross income rather than HBAI net income.
+
     For each NEED income band, computes the ratio of the NEED-implied mean spend
     to the LCFS-derived mean spend and applies it multiplicatively. This preserves
     within-band distributional shape while anchoring the level to admin data.
@@ -471,6 +479,7 @@ def generate_lcfs_table(lcfs_person: pd.DataFrame, lcfs_household: pd.DataFrame)
     # Annualise weekly LCFS values (× 52)
     annualise = list(CONSUMPTION_VARIABLE_RENAMES.values()) + [
         "hbai_household_net_income",
+        "household_gross_income",
         "electricity_consumption",
         "gas_consumption",
     ]
@@ -516,6 +525,7 @@ def uprate_lcfs_table(household: pd.DataFrame, time_period: str) -> pd.DataFrame
     # Uprate income predictor so training distribution matches FRS target year
     for col in [
         "hbai_household_net_income",
+        "household_gross_income",
         "employment_income",
         "self_employment_income",
         "private_pension_income",
@@ -584,7 +594,9 @@ def impute_consumption(dataset: UKSingleYearDataset) -> UKSingleYearDataset:
     # This is a 4-dimensional raking (vs the 1D income-band calibration on LCFS
     # training data in _calibrate_energy_to_need) because the FRS has the full
     # set of housing/demographic variables needed for multi-margin calibration.
-    income = input_df["hbai_household_net_income"].values
+    # NEED income bands use Experian modelled gross income, so we use
+    # household_gross_income rather than hbai_household_net_income.
+    income = sim.calculate("household_gross_income", map_to="household").values
     tenure = sim.calculate("tenure_type", map_to="household").values
     accomm = sim.calculate("accommodation_type", map_to="household").values
     region = sim.calculate("region", map_to="household").values
diff --git a/policyengine_uk_data/tests/test_energy_calibration.py b/policyengine_uk_data/tests/test_energy_calibration.py
index 0664f25e..5d254a22 100644
--- a/policyengine_uk_data/tests/test_energy_calibration.py
+++ b/policyengine_uk_data/tests/test_energy_calibration.py
@@ -50,7 +50,7 @@ def arrays(imputed):
     sim = Microsimulation(dataset=imputed)
     return dict(
         income=sim.calculate(
-            "hbai_household_net_income", map_to="household", period=2023
+            "household_gross_income", map_to="household", period=2023
         ).values,
         tenure=sim.calculate("tenure_type", map_to="household", period=2023).values,
         accomm=sim.calculate(