diff --git a/activitysim/abm/models/location_choice.py b/activitysim/abm/models/location_choice.py
index dd5e279b39..490df8688a 100644
--- a/activitysim/abm/models/location_choice.py
+++ b/activitysim/abm/models/location_choice.py
@@ -138,15 +138,8 @@ def _location_sample(
     logger.info("Running %s with %d persons" % (trace_label, len(choosers.index)))
 
     sample_size = model_settings["SAMPLE_SIZE"]
-    if config.setting("disable_destination_sampling", False) or (
-        estimator and estimator.want_unsampled_alternatives
-    ):
-        # FIXME interaction_sample will return unsampled complete alternatives with probs and pick_count
-        logger.info(
-            "Estimation mode for %s using unsampled alternatives short_circuit_choices"
-            % (trace_label,)
-        )
-        sample_size = 0
+    if estimator:
+        sample_size = model_settings.get("ESTIMATION_SAMPLE_SIZE", 0)
 
     locals_d = {
         "skims": skims,
@@ -470,6 +463,37 @@ def run_location_sample(
             trace_label=trace_label,
         )
 
+    # Hack to get shorter run times when you don't care about creating EDB for location choice models
+    if estimator:
+        # grabbing survey values
+        survey_persons = estimation.manager.get_survey_table("persons")
+        if "school_location" in trace_label:
+            survey_choices = survey_persons["school_zone_id"].reset_index()
+        elif ("workplace_location" in trace_label) and ("external" not in trace_label):
+            survey_choices = survey_persons["workplace_zone_id"].reset_index()
+        else:
+            return choices
+        survey_choices.columns = ["person_id", "alt_dest"]
+        survey_choices = survey_choices[
+            survey_choices["person_id"].isin(choices.index)
+            & (survey_choices.alt_dest > 0)
+        ]
+        # merging survey destination into table if not available
+        joined_data = survey_choices.merge(
+            choices, on=["person_id", "alt_dest"], how="left", indicator=True
+        )
+        missing_rows = joined_data[joined_data["_merge"] == "left_only"]
+        missing_rows["pick_count"] = 1
+        if len(missing_rows) > 0:
+            new_choices = missing_rows[
+                ["person_id", "alt_dest", "prob", "pick_count"]
+            ].set_index("person_id")
+            choices = choices.append(new_choices, ignore_index=False).sort_index()
+            # making probability the mean of all other sampled destinations by person
+            choices["prob"] = choices["prob"].fillna(
+                choices.groupby("person_id")["prob"].transform("mean")
+            )
+
     return choices