diff --git a/activitysim/abm/models/location_choice.py b/activitysim/abm/models/location_choice.py
index faaa50863..951a5411c 100644
--- a/activitysim/abm/models/location_choice.py
+++ b/activitysim/abm/models/location_choice.py
@@ -950,34 +950,6 @@ def run_location_choice(
             )
             estimator.write_override_choices(choices_df.choice)
 
-            if want_logsums:
-                # if we override choices, we need to to replace choice logsum with ologsim for override location
-                # fortunately, as long as we aren't sampling dest alts, the logsum will be in location_sample_df
-
-                # if we start sampling dest alts, we will need code below to compute override location logsum
-                assert estimator.want_unsampled_alternatives
-
-                # merge mode_choice_logsum for the overridden location
-                # alt_logsums columns: ['person_id', 'choice', 'logsum']
-                alt_dest_col = model_settings.ALT_DEST_COL_NAME
-                alt_logsums = (
-                    location_sample_df[[alt_dest_col, ALT_LOGSUM]]
-                    .rename(columns={alt_dest_col: "choice", ALT_LOGSUM: "logsum"})
-                    .reset_index()
-                )
-
-                # choices_df columns: ['person_id', 'choice']
-                choices_df = choices_df[["choice"]].reset_index()
-
-                # choices_df columns: ['person_id', 'choice', 'logsum']
-                choices_df = pd.merge(choices_df, alt_logsums, how="left").set_index(
-                    "person_id"
-                )
-
-                logger.debug(
-                    f"{trace_label} segment {segment_name} estimation: override logsums"
-                )
-
             if state.settings.trace_hh_id:
                 estimation_trace_label = tracing.extend_trace_label(
                     trace_label, f"estimation.{segment_name}.survey_choices"
diff --git a/activitysim/abm/test/test_location_choice.py b/activitysim/abm/test/test_location_choice.py
new file mode 100644
index 000000000..37c375ccc
--- /dev/null
+++ b/activitysim/abm/test/test_location_choice.py
@@ -0,0 +1,90 @@
+from __future__ import annotations
+
+from types import SimpleNamespace
+from unittest.mock import Mock
+
+import pandas as pd
+import pandas.testing as pdt
+
+from activitysim.abm.models import location_choice
+
+
+def test_estimation_override_preserves_destination_choice_logsum(monkeypatch):
+    """Survey overrides should only change the chosen destination's mode logsum."""
+    person_index = pd.Index([1], name="person_id")
+    location_sample = pd.DataFrame(
+        {
+            "alt_dest": [101, 202],
+            location_choice.ALT_LOGSUM: [5.0, 7.0],
+        },
+        index=pd.Index([1, 1], name="person_id"),
+    )
+    modeled_choices = pd.DataFrame(
+        {"choice": [101], "logsum": [-1.5]}, index=person_index
+    )
+
+    # Keep the test focused on the estimation override and final logsum merge.
+    monkeypatch.setattr(
+        location_choice,
+        "run_location_sample",
+        lambda *args, **kwargs: location_sample.copy(),
+    )
+    monkeypatch.setattr(
+        location_choice,
+        "run_location_logsums",
+        lambda *args, **kwargs: location_sample.copy(),
+    )
+    monkeypatch.setattr(
+        location_choice,
+        "run_location_simulate",
+        lambda *args, **kwargs: modeled_choices.copy(),
+    )
+
+    estimator = Mock()
+    estimator.get_survey_values.return_value = pd.Series(
+        [202], index=person_index, name="choice"
+    )
+    shadow_price_calculator = Mock()
+    shadow_price_calculator.dest_size_terms.return_value = pd.Series(
+        [1.0, 1.0], index=[101, 202]
+    )
+    model_settings = SimpleNamespace(
+        ALT_DEST_COL_NAME="alt_dest",
+        CHOOSER_SEGMENT_COLUMN_NAME="segment",
+        DEST_CHOICE_COLUMN_NAME="workplace_zone_id",
+        LOGSUM_SETTINGS="tour_mode_choice.yaml",
+        SEGMENT_IDS={"workers": 1},
+    )
+    state = SimpleNamespace(
+        settings=SimpleNamespace(
+            sample_method="monte_carlo",
+            trace_hh_id=None,
+            use_explicit_error_terms=False,
+        )
+    )
+    persons = pd.DataFrame({"segment": [1]}, index=person_index)
+
+    choices, sample = location_choice.run_location_choice(
+        state=state,
+        persons_merged_df=persons,
+        network_los=Mock(),
+        shadow_price_calculator=shadow_price_calculator,
+        want_logsums=True,
+        want_sample_table=False,
+        estimator=estimator,
+        model_settings=model_settings,
+        chunk_size=0,
+        chunk_tag="workplace_location",
+        trace_label="workplace_location",
+    )
+
+    expected = pd.DataFrame(
+        {
+            "choice": [202],
+            "logsum": [-1.5],
+            location_choice.ALT_LOGSUM: [7.0],
+        },
+        index=person_index,
+    )
+    pdt.assert_frame_equal(choices, expected)
+    assert sample is None