diff --git a/README.md b/README.md
index b37beb8..2ac7c66 100644
--- a/README.md
+++ b/README.md
@@ -15,7 +15,7 @@ uv sync --locked
 To run the model with test data, use the following command:
 
 ```bash
-uv run activitysim run -c model/configs_mp -c model/configs -d model/data -o model/output
+uv run activitysim run -c model/configs_mp -c model/configs -d model/data -o model/output --ext extensions
 ```
 
 ## Contents
diff --git a/extensions/__init__.py b/extensions/__init__.py
new file mode 100644
index 0000000..ef70e01
--- /dev/null
+++ b/extensions/__init__.py
@@ -0,0 +1,2 @@
+from . import telework_arrangement
+from . import telework_duration
diff --git a/extensions/telework_arrangement.py b/extensions/telework_arrangement.py
new file mode 100644
index 0000000..823749e
--- /dev/null
+++ b/extensions/telework_arrangement.py
@@ -0,0 +1,151 @@
+# ActivitySim
+# See full license in LICENSE.txt.
+from __future__ import annotations
+
+import logging
+
+import pandas as pd
+
+from activitysim.core import (
+    config,
+    estimation,
+    expressions,
+    simulate,
+    tracing,
+    workflow,
+)
+from activitysim.core.configuration.logit import LogitComponentSettings
+
+logger = logging.getLogger("activitysim")
+
+
+class TeleworkArrangementSettings(LogitComponentSettings, extra="forbid"):
+    """
+    Settings for the `telework_arrangement` component.
+    """
+
+    CHOOSER_FILTER_COLUMN_NAME: str = "is_worker"
+    """Column name in the dataframe to represent worker."""
+
+    HAS_IN_HOME_WORK_ACTIVITY_ALT: int = 0
+    """The alternative index for having in-home work activity on the simulation day."""
+
+
+@workflow.step
+def telework_arrangement(
+    state: workflow.State,
+    persons_merged: pd.DataFrame,
+    persons: pd.DataFrame,
+    model_settings: TeleworkArrangementSettings | None = None,
+    model_settings_file_name: str = "telework_arrangement.yaml",
+    trace_label: str = "telework_arrangement",
+) -> None:
+    """
+    This model predicts the telework arrangement on the simulation day for all workers.
+    The alternatives are whether or not a worker has in-home telework activities on the simulation day:
+    The result is a new column in the persons table, "has_in_home_work_activity": True or False
+
+    Parameters
+    ----------
+    state : workflow.State
+    persons_merged : DataFrame
+        This represents the 'choosers' table for this component.
+    persons : DataFrame
+        The original persons table is referenced so the telework arrangement column
+        can be appended to it.
+    model_settings : TeleworkArrangementSettings, optional
+        The settings used in this model component.  If not provided, they are
+        loaded out of the configs directory YAML file referenced by
+        the `model_settings_file_name` argument.
+    model_settings_file_name : str, default "telework_arrangement.yaml"
+        This is where model setting are found if `model_settings` is not given
+        explicitly.  The same filename is also used to write settings files to
+        the estimation data bundle in estimation mode.
+    trace_label : str, default "telework_arrangement"
+        This label is used for various tracing purposes.
+    """
+
+    if model_settings is None:
+        model_settings = TeleworkArrangementSettings.read_settings_file(
+            state.filesystem,
+            model_settings_file_name,
+        )
+
+    chooser_filter_column_name = model_settings.CHOOSER_FILTER_COLUMN_NAME
+    choosers = persons_merged[persons_merged[chooser_filter_column_name]]
+
+    logger.info("Running %s with %d persons", trace_label, len(choosers))
+
+    estimator = estimation.manager.begin_estimation(state, "telework_arrangement")
+
+    constants = config.get_model_constants(model_settings)
+
+    expressions.annotate_preprocessors(
+        state,
+        df=choosers,
+        locals_dict=constants,
+        skims=None,
+        model_settings=model_settings,
+        trace_label=trace_label,
+    )
+
+    model_spec = state.filesystem.read_model_spec(file_name=model_settings.SPEC)
+    coefficients_df = state.filesystem.read_model_coefficients(model_settings)
+    model_spec = simulate.eval_coefficients(
+        state, model_spec, coefficients_df, estimator
+    )
+    nest_spec = config.get_logit_model_settings(model_settings)
+
+    if estimator:
+        estimator.write_model_settings(model_settings, model_settings_file_name)
+        estimator.write_spec(model_settings)
+        estimator.write_coefficients(coefficients_df, model_settings)
+        estimator.write_choosers(choosers)
+
+    choices = simulate.simple_simulate(
+        state,
+        choosers=choosers,
+        spec=model_spec,
+        nest_spec=nest_spec,
+        locals_d=constants,
+        trace_label=trace_label,
+        trace_choice_name="telework_arrangement",
+        estimator=estimator,
+        compute_settings=model_settings.compute_settings,
+    )
+
+    has_in_home_work_activity_alt = model_settings.HAS_IN_HOME_WORK_ACTIVITY_ALT
+    choices = choices == has_in_home_work_activity_alt
+
+    if estimator:
+        estimator.write_choices(choices)
+        choices = estimator.get_survey_values(
+            choices,
+            "persons",
+            "has_in_home_work_activity",
+        )
+        estimator.write_override_choices(choices)
+        estimator.end_estimation()
+
+    persons["has_in_home_work_activity"] = (
+        choices.reindex(persons.index).fillna(0).astype(bool)
+    )
+
+    state.add_table("persons", persons)
+
+    tracing.print_summary(
+        "telework_arrangement.has_in_home_work_activity",
+        persons.has_in_home_work_activity,
+        value_counts=True,
+    )
+
+    if state.settings.trace_hh_id:
+        state.tracing.trace_df(persons, label=trace_label, warn_if_empty=True)
+
+    expressions.annotate_tables(
+        state,
+        locals_dict=constants,
+        skims=None,
+        model_settings=model_settings,
+        trace_label=trace_label,
+    )
diff --git a/extensions/telework_duration.py b/extensions/telework_duration.py
new file mode 100644
index 0000000..6522bbc
--- /dev/null
+++ b/extensions/telework_duration.py
@@ -0,0 +1,287 @@
+# ActivitySim
+# See full license in LICENSE.txt.
+from __future__ import annotations
+
+import logging
+from typing import Literal
+
+import pandas as pd
+
+from activitysim.core import (
+    config,
+    estimation,
+    expressions,
+    logit,
+    simulate,
+    tracing,
+    workflow,
+)
+from activitysim.core.configuration.logit import LogitComponentSettings
+
+logger = logging.getLogger("activitysim")
+
+
+class TeleworkDurationSettings(LogitComponentSettings):
+    """
+    Settings for the `telework_duration` component.
+    """
+
+    CHOICE_MODEL: Literal["PROBABILISTIC", "MNL"] = "PROBABILISTIC"
+    """Choice model type to use for telework duration."""
+
+    CHOOSER_FILTER_COLUMN_NAME: str = "has_in_home_work_activity"
+    """Column name in chooser table to represent workers with in-home work activity on the simulation day."""
+
+    DURATION_CATEGORY_COLUMN_NAME: str = "telework_duration_category"
+    """Persons column for the chosen telework duration category."""
+
+    DURATION_HOURS_COLUMN_NAME: str = "telework_duration_hours"
+    """Persons column for telework duration in hours."""
+
+    ALTS: str = "telework_duration_alts.csv"
+    """Alternatives file with duration category to hour mapping."""
+
+    ALT_NAME_COLUMN: str = "alt"
+    """Alternatives file column containing category names."""
+
+    ALT_DURATION_COLUMN: str = "duration_hours"
+    """Alternatives file column containing duration values in hours."""
+
+    SPEC: str = "telework_duration.csv"
+    """MNL utility specification file."""
+
+    COEFFICIENTS: str | None = "telework_duration_coeffs.csv"
+    """MNL coefficients file."""
+
+    LOGIT_TYPE: Literal["MNL", "NL"] = "MNL"
+    """Logit type when running MNL mode."""
+
+    NESTS: dict | None = None
+    """Nest settings for NL mode, if ever used."""
+
+    PROBS_SPEC: str = "telework_duration_probs.csv"
+    """Probabilistic choice lookup table."""
+
+    PROBS_JOIN_COLS: list[str] | None = None
+    """Columns to join choosers to probability table."""
+
+    CONSTANTS: dict = {}
+    """Named constants usable in preprocessors and expressions."""
+
+    preprocessor: dict | list[dict] | None = None
+    """Chooser preprocessor settings."""
+
+
+def _load_alternatives(state: workflow.State, model_settings: TeleworkDurationSettings):
+    alts = simulate.read_model_alts(state, model_settings.ALTS, set_index=None)
+
+    alt_name_col = model_settings.ALT_NAME_COLUMN
+    alt_duration_col = model_settings.ALT_DURATION_COLUMN
+    if alt_name_col not in alts.columns or alt_duration_col not in alts.columns:
+        raise RuntimeError(
+            "telework_duration alternatives file must include "
+            f"'{alt_name_col}' and '{alt_duration_col}' columns"
+        )
+
+    alts = alts[[alt_name_col, alt_duration_col]].copy()
+    alts[alt_name_col] = alts[alt_name_col].astype(str)
+    alts = alts.drop_duplicates(subset=[alt_name_col])
+    return alts
+
+
+def _simulate_probabilistic(
+    state: workflow.State,
+    choosers: pd.DataFrame,
+    model_settings: TeleworkDurationSettings,
+    trace_label: str,
+) -> pd.Series:
+    probs = pd.read_csv(
+        state.filesystem.get_config_file_path(model_settings.PROBS_SPEC), comment="#"
+    )
+    probs_join_cols = model_settings.PROBS_JOIN_COLS or []
+
+    if probs_join_cols:
+        chooser_probs = pd.merge(
+            choosers.reset_index(),
+            probs,
+            on=probs_join_cols,
+            how="left",
+        ).set_index(choosers.index.name)
+    else:
+        if probs.shape[0] != 1:
+            raise RuntimeError(
+                "telework_duration probabilistic mode requires a single-row PROBS_SPEC "
+                "when PROBS_JOIN_COLS is not provided"
+            )
+        chooser_probs = pd.concat([probs] * len(choosers), ignore_index=True)
+        chooser_probs.index = choosers.index
+
+    prob_cols = [c for c in probs.columns if c not in probs_join_cols]
+    if not prob_cols:
+        raise RuntimeError(
+            "telework_duration probabilistic mode found no probability columns"
+        )
+
+    chooser_probs = chooser_probs[prob_cols].fillna(0)
+    row_sums = chooser_probs.sum(axis=1)
+    if (row_sums <= 0).any():
+        raise RuntimeError(
+            "telework_duration probabilistic mode found choosers with no positive "
+            "probability mass"
+        )
+
+    chooser_probs = chooser_probs.div(row_sums, axis=0)
+
+    choices, _ = logit.make_choices(
+        state,
+        chooser_probs,
+        trace_label=trace_label,
+        trace_choosers=choosers,
+    )
+
+    category_choices = pd.Series(prob_cols).loc[choices].astype(str)
+    category_choices.index = choices.index
+
+    return category_choices
+
+
+@workflow.step
+def telework_duration(
+    state: workflow.State,
+    persons_merged: pd.DataFrame,
+    persons: pd.DataFrame,
+    model_settings: TeleworkDurationSettings | None = None,
+    model_settings_file_name: str = "telework_duration.yaml",
+    trace_label: str = "telework_duration",
+) -> None:
+    """
+    Simulate daily in-home work duration for workers with in-home work activity.
+
+    This model applies only to workers where `has_in_home_work_activity` is True
+    from the telework arrangement model. It supports either:
+    - probabilistic sampling from `PROBS_SPEC`, or
+    - MNL simulation from `SPEC` and `COEFFICIENTS`.
+    """
+
+    if model_settings is None:
+        model_settings = TeleworkDurationSettings.read_settings_file(
+            state.filesystem,
+            model_settings_file_name,
+        )
+
+    chooser_filter_col = model_settings.CHOOSER_FILTER_COLUMN_NAME
+
+    choosers = persons_merged[persons_merged[chooser_filter_col]]
+
+    logger.info("Running %s with %d persons", trace_label, len(choosers))
+
+    category_col = model_settings.DURATION_CATEGORY_COLUMN_NAME
+    duration_col = model_settings.DURATION_HOURS_COLUMN_NAME
+    alts = _load_alternatives(state, model_settings)
+    category_dtype = pd.api.types.CategoricalDtype(
+        categories=alts[model_settings.ALT_NAME_COLUMN].tolist() + [""],
+        ordered=False,
+    )
+
+    # Default values for non-eligible persons.
+    persons[category_col] = pd.Series(
+        pd.Categorical([""] * len(persons), dtype=category_dtype),
+        index=persons.index,
+    )
+    persons[duration_col] = 0.0
+
+    if choosers.empty:
+        state.add_table("persons", persons)
+        tracing.print_summary(category_col, persons[category_col], value_counts=True)
+        tracing.print_summary(duration_col, persons[duration_col], value_counts=True)
+        return
+
+    estimator = estimation.manager.begin_estimation(state, "telework_duration")
+    constants = config.get_model_constants(model_settings)
+
+    expressions.annotate_preprocessors(
+        state,
+        df=choosers,
+        locals_dict=constants,
+        skims=None,
+        model_settings=model_settings,
+        trace_label=trace_label,
+    )
+
+    choice_model = model_settings.CHOICE_MODEL
+
+    if choice_model == "MNL":
+        model_spec = state.filesystem.read_model_spec(file_name=model_settings.SPEC)
+        coefficients_df = state.filesystem.read_model_coefficients(model_settings)
+        model_spec = simulate.eval_coefficients(
+            state, model_spec, coefficients_df, estimator
+        )
+        nest_spec = config.get_logit_model_settings(model_settings)
+
+        if estimator:
+            estimator.write_model_settings(model_settings, model_settings_file_name)
+            estimator.write_spec(model_settings)
+            estimator.write_coefficients(coefficients_df, model_settings)
+            estimator.write_choosers(choosers)
+
+        raw_choices = simulate.simple_simulate(
+            state,
+            choosers=choosers,
+            spec=model_spec,
+            nest_spec=nest_spec,
+            locals_d=constants,
+            trace_label=trace_label,
+            trace_choice_name=category_col,
+            estimator=estimator,
+            compute_settings=model_settings.compute_settings,
+        )
+        category_choices = pd.Series(
+            model_spec.columns[raw_choices.values], index=raw_choices.index
+        ).astype(category_dtype)
+    else:
+        if estimator:
+            estimator.write_model_settings(model_settings, model_settings_file_name)
+            estimator.write_spec(model_settings, tag="PROBS_SPEC")
+            estimator.write_choosers(choosers)
+        category_choices = _simulate_probabilistic(
+            state,
+            choosers,
+            model_settings,
+            trace_label,
+        ).astype(category_dtype)
+
+    alt_to_duration = alts.set_index(model_settings.ALT_NAME_COLUMN)[
+        model_settings.ALT_DURATION_COLUMN
+    ]
+
+    if estimator:
+        estimator.write_choices(category_choices)
+        category_choices = estimator.get_survey_values(
+            category_choices,
+            "persons",
+            category_col,
+        )
+        category_choices = category_choices.astype(category_dtype)
+        estimator.write_override_choices(category_choices)
+        estimator.end_estimation()
+
+    duration_choices = category_choices.map(alt_to_duration).fillna(0.0).astype(float)
+
+    persons.loc[category_choices.index, category_col] = category_choices
+    persons.loc[duration_choices.index, duration_col] = duration_choices
+
+    state.add_table("persons", persons)
+
+    tracing.print_summary(category_col, persons[category_col], value_counts=True)
+    tracing.print_summary(duration_col, persons[duration_col], value_counts=True)
+
+    if state.settings.trace_hh_id:
+        state.tracing.trace_df(persons, label=trace_label, warn_if_empty=True)
+
+    expressions.annotate_tables(
+        state,
+        locals_dict=constants,
+        skims=None,
+        model_settings=model_settings,
+        trace_label=trace_label,
+    )
diff --git a/model/configs/annotate_persons.csv b/model/configs/annotate_persons.csv
index 6162b53..ae7a2b6 100644
--- a/model/configs/annotate_persons.csv
+++ b/model/configs/annotate_persons.csv
@@ -34,5 +34,7 @@ school_segment highschool,school_segment,"np.where(is_highschool, SCHOOL_SEGMENT
 school_segment university,school_segment,"np.where(is_university, SCHOOL_SEGMENT_UNIV, school_segment).astype(np.int8)"
 #,,
 is_worker,is_worker,"persons.pemploy.isin([PEMPLOY_FULL, PEMPLOY_PART])"
+#temp out of home worker variable will be overwritten in work_from_home model,,
+is_out_of_home_worker,is_out_of_home_worker,is_worker
 #,,
 home_zone_id,home_zone_id,"reindex(households.home_zone_id, persons.household_id)"
diff --git a/model/configs/cdap_fixed_relative_proportions.csv b/model/configs/cdap_fixed_relative_proportions.csv
index 788f398..4c36f03 100644
--- a/model/configs/cdap_fixed_relative_proportions.csv
+++ b/model/configs/cdap_fixed_relative_proportions.csv
@@ -1,6 +1,8 @@
 Description,Expression,M,N,H
-Full-time worker,ptype == 1,0.79647,0.09368,0.10985
-Part-time worker,ptype == 2,0.61678,0.25757,0.12565
+Full-time worker,(ptype == 1) & ~work_from_home,0.79647,0.09368,0.10985
+Full-time worker,(ptype == 1) & work_from_home,0,0.460276126,0.539723874
+Part-time worker,(ptype == 2) & ~work_from_home,0.61678,0.25757,0.12565
+Part-time worker,(ptype == 2) & work_from_home,0,0.672120453,0.327879547
 University student,ptype == 3,0.69229,0.15641,0.1513
 Non-working adult,ptype == 4,0,0.67169,0.32831
 Retired,ptype == 5,0,0.54295,0.45705
diff --git a/model/configs/cdap_indiv_and_hhsize1.csv b/model/configs/cdap_indiv_and_hhsize1.csv
index 0a666e6..1888d13 100644
--- a/model/configs/cdap_indiv_and_hhsize1.csv
+++ b/model/configs/cdap_indiv_and_hhsize1.csv
@@ -49,3 +49,5 @@ University student interaction with off-peak accessibility to retail,(ptype == 3
 Driving-age child who is in school interaction with off-peak accessibility to retail,(ptype == 6) * auOpRetail,,coef_child_who_is_in_school_or_too_young_for_school_interaction_with_off_peak_accessibility_to_retail_N,
 Pre-driving-age child who is in school interaction with off-peak accessibility to retail,(ptype == 7) * auOpRetail,,coef_child_who_is_in_school_or_too_young_for_school_interaction_with_off_peak_accessibility_to_retail_N,
 Pre-driving-age child who is too young for school interaction with off-peak accessibility to retail,(ptype == 8) * auOpRetail,,coef_child_who_is_in_school_or_too_young_for_school_interaction_with_off_peak_accessibility_to_retail_N,
+Full time worker works from home, (ptype == 1) & (work_from_home),-999,0,0
+Part time worker works from home, (ptype == 2) & (work_from_home),-999,0,0
\ No newline at end of file
diff --git a/model/configs/settings.yaml b/model/configs/settings.yaml
index 7337c65..e7a0666 100644
--- a/model/configs/settings.yaml
+++ b/model/configs/settings.yaml
@@ -46,6 +46,7 @@ input_table_list:
       - pemploy
       - pstudent
       - ptype
+      - naics_code
   #
   # land_use (table index 'zone_id')
   #
@@ -175,11 +176,15 @@ models:
   - initialize_landuse
   - initialize_households
   - compute_accessibility
+  - work_from_home
   - school_location
   - workplace_location
   - auto_ownership_simulate
   - free_parking
+  - telecommute_frequency
   - cdap_simulate
+  - telework_arrangement
+  - telework_duration
   - mandatory_tour_frequency
   - mandatory_tour_scheduling
   - non_mandatory_tour_frequency
diff --git a/model/configs/telecommute_frequency.csv b/model/configs/telecommute_frequency.csv
new file mode 100644
index 0000000..37b799e
--- /dev/null
+++ b/model/configs/telecommute_frequency.csv
@@ -0,0 +1,28 @@
+Label,Description,Expression,No_Telecommute,1_day_week,2_days_week,3_days_week,4_days_week
+util_HasChildren0to5,Has children 0 to 5 years old,@df.num_young_children>0,,coef_HasChildren0to5_1day,coef_HasChildren0to5_234day,coef_HasChildren0to5_234day,coef_HasChildren0to5_234day
+util_HasChildren6to12,Has children 6 to 12 years old,@df.num_children_6_to_12>0,,coef_HasChildren6to12_1day,coef_HasChildren6to12_23day,coef_HasChildren6to12_23day,coef_HasChildren6to12_4day
+util_OneAdultInHH,One adult in hh,@df.num_adults==1,,coef_OneAdultInHH_1day,coef_OneAdultInHH_23day,coef_OneAdultInHH_23day,coef_OneAdultInHH_4day
+util_Female,female,@df.female,,coef_Female_1234day,coef_Female_1234day,coef_Female_1234day,coef_Female_1234day
+util_PartTimeWorker,Part-time worker,@df.pemploy==2,,coef_PartTimeWorker_1234day,coef_PartTimeWorker_1234day,coef_PartTimeWorker_1234day,coef_PartTimeWorker_1234day
+util_Income60to100k,Income 60-100k,"@df.income.between(60000, 100000)",,coef_Income60to100k_1day,coef_Income60to100k_23day,coef_Income60to100k_23day,coef_Income60to100k_4day
+util_Income100to150k,Income 100-150k,"@df.income.between(100000, 150000)",,coef_Income100to150k_1day,coef_Income100to150k_234day,coef_Income100to150k_234day,coef_Income100to150k_234day
+util_Income150kplus,Income 150k+,@df.income > 150000,,coef_Income150kplus_1day,coef_Income150kplus_23day,coef_Income150kplus_23day,coef_Income150kplus_4day
+util_0Autos,0 Autos,@df.auto_ownership==0,,coef_0Autos_1day,coef_0Autos_234day,coef_0Autos_234day,coef_0Autos_234day
+util_1Auto,1 Auto,@df.auto_ownership==1,,coef_1Auto_1day,coef_1Auto_234day,coef_1Auto_234day,coef_1Auto_234day
+util_3plusAutos,3+ Autos,@df.auto_ownership>=3,,coef_3plusAutos_1day,coef_3plusAutos_23day,coef_3plusAutos_23day,coef_3plusAutos_4day
+util_DistanceToWork,Distance to work,@df.distance_to_work,,coef_DistanceToWork_1day,coef_DistanceToWork_234day,coef_DistanceToWork_234day,coef_DistanceToWork_234day
+util_accomodation,Accomodation industry,@df.naics_code==721,,coef_accomodation_1234day,coef_accomodation_1234day,coef_accomodation_1234day,coef_accomodation_1234day
+util_agriculture,Agriculture industry,@df.naics_code==11,,coef_agriculture_1234day,coef_agriculture_1234day,coef_agriculture_1234day,coef_agriculture_1234day
+util_business_srv,Business services industry,@df.naics_code==54,,coef_business_srv_1day,coef_business_srv_23day,coef_business_srv_23day,coef_business_srv_4day
+util_construction,Construction industry,@df.naics_code==23,,coef_construction_1day,coef_construction_234day,coef_construction_234day,coef_construction_234day
+util_education,Education industry,@df.naics_code==61,,coef_education_1234day,coef_education_1234day,coef_education_1234day,coef_education_1234day
+util_entertainment,Entertainment industry,@df.naics_code==71,,coef_entertainment_1day,coef_entertainment_23day,coef_entertainment_23day,coef_entertainment_4day
+util_food_srv,Food services industry,@df.naics_code==722,,coef_food_srv_1234day,coef_food_srv_1234day,coef_food_srv_1234day,coef_food_srv_1234day
+util_government,Government industry,@df.naics_code==92,,coef_government_1day,coef_government_234day,coef_government_234day,coef_government_234day
+util_healthcare,Healthcare industry,@df.naics_code==62,,coef_healthcare_1234day,coef_healthcare_1234day,coef_healthcare_1234day,coef_healthcare_1234day
+util_manufacturing,Manufacturing industry,"@df.naics_code.isin([31,32,33])",,coef_manufacturing_1day,coef_manufacturing_234day,coef_manufacturing_234day,coef_manufacturing_234day
+util_mgmt_srv,Management services industry,@df.naics_code==55,,coef_mgmt_srv_1day,coef_mgmt_srv_23day,coef_mgmt_srv_23day,coef_mgmt_srv_4day
+util_military,Miliary industry,@df.naics_code==9000,,coef_military_1day,coef_military_234day,coef_military_234day,coef_military_234day
+util_retail,Retail industry,"@df.naics_code.isin([44,45])",,coef_retail_1day,coef_retail_23day,coef_retail_23day,coef_retail_4day
+util_asc,Alternative specific constant,1,,asc_1day,asc_23day,asc_23day,asc_4day
+util_split_2_3_days_constant, Constant for splitting 2_3_days telecommute frequency,@np.log(0.5),,,1,1,
diff --git a/model/configs/telecommute_frequency.yaml b/model/configs/telecommute_frequency.yaml
new file mode 100644
index 0000000..162066e
--- /dev/null
+++ b/model/configs/telecommute_frequency.yaml
@@ -0,0 +1,9 @@
+
+# borrowed from free parking model
+
+SPEC: telecommute_frequency.csv
+COEFFICIENTS: telecommute_frequency_coeffs.csv
+
+#LOGIT_TYPE: NL
+LOGIT_TYPE: MNL
+
diff --git a/model/configs/telecommute_frequency_coeffs.csv b/model/configs/telecommute_frequency_coeffs.csv
new file mode 100644
index 0000000..262b771
--- /dev/null
+++ b/model/configs/telecommute_frequency_coeffs.csv
@@ -0,0 +1,85 @@
+coefficient_name,value,constrain
+coef_Services_1day,0.0,F
+coef_SalesOffice_1day,0.0,F
+coef_ResourceConstruct_1day,0.0,F
+coef_TransportMat_1day,0.0,F
+coef_HasChildren0to5_1day,0.0,T
+coef_HasChildren6to12_1day,0.0,T
+coef_OneAdultInHH_1day,0.2307987638695857,F
+coef_2plusAdultsInHH_1day,0.0,F
+coef_Female_1234day,0.0,T
+coef_PartTimeWorker_1234day,-0.1647657054647985,F
+coef_PaysToPark_1day,0.0,F
+coef_Income60to100k_1day,0.3549582138983352,F
+coef_Income100to150k_1day,0.0,T
+coef_Income150kplus_1day,0.0,T
+coef_0Autos_1day,0.0,T
+coef_1Auto_1day,-0.2037126296304744,F
+coef_3plusAutos_1day,-0.2366548258122986,F
+coef_DistanceToWork_1day,0.0065817284180437,F
+coef_Services_23day,0.0,F
+coef_SalesOffice_23day,0.0,F
+coef_ResourceConstruct_23day,0.0,F
+coef_TransportMat_23day,0.0,F
+coef_HasChildren0to5_234day,-0.3282697175018726,F
+coef_HasChildren6to12_23day,0.0,T
+coef_OneAdultInHH_23day,0.0,T
+coef_2plusAdultsInHH_23day,0.0,F
+coef_PaysToPark_23day,0.0,F
+coef_Income60to100k_23day,0.0,T
+coef_Income100to150k_234day,0.0,T
+coef_Income150kplus_23day,0.0,T
+coef_0Autos_234day,0.0,T
+coef_1Auto_234day,0.0,T
+coef_3plusAutos_23day,-0.2673431713437322,F
+coef_DistanceToWork_234day,0.011270292473366,F
+coef_HasChildren6to12_4day,0.0,T
+coef_OneAdultInHH_4day,0.0,T
+coef_2plusAdultsInHH_4day,0.0,F
+coef_PaysToPark_4day,0.0,F
+coef_Income60to100k_4day,0.0,T
+coef_Income100to150k_4day,0.0,T
+coef_Income150kplus_4day,0.0,T
+coef_3plusAutos_4day,-0.5086281851316395,F
+coef_accomodation_1234day,-1.1672033007413072,F
+coef_agriculture_1234day,0.0,T
+coef_business_srv_1day,0.8048779013830238,F
+coef_construction_1day,0.0,T
+coef_education_1234day,-0.0387511815872183,F
+coef_entertainment_1day,0.0,T
+coef_food_srv_1day,0.0,F
+coef_government_1day,0.4503818768273094,F
+coef_healthcare_1day,0.0,F
+coef_manufacturing_1day,0.0,T
+coef_mgmt_srv_1day,0.0,T
+coef_military_1day,0.0,T
+coef_retail_1day,-0.8320341753416274,F
+coef_accomodation_23day,0.0,F
+coef_agriculture_23day,0.0,F
+coef_business_srv_23day,0.9228435480770156,F
+coef_construction_234day,0.0,T
+coef_education_23day,0.0,F
+coef_entertainment_23day,0.0,T
+coef_food_srv_1234day,-1.6057467385857538,F
+coef_government_234day,0.0,T
+coef_healthcare_1234day,-0.2529974908266847,F
+coef_manufacturing_234day,0.0,T
+coef_mgmt_srv_23day,0.5723496481424273,F
+coef_military_234day,-0.512646790150494,F
+coef_retail_23day,-0.7576291501959187,F
+coef_accomodation_4day,0.0,F
+coef_agriculture_4day,0.0,F
+coef_business_srv_4day,1.117777186998694,F
+coef_construction_4day,0.0,F
+coef_education_4day,0.0,F
+coef_entertainment_4day,0.0,T
+coef_food_srv_4day,0.0,F
+coef_government_4day,0.0,F
+coef_healthcare_4day,0.0,F
+coef_manufacturing_4day,0.0,F
+coef_mgmt_srv_4day,0.970302573177628,F
+coef_military_4day,0.0,F
+coef_retail_4day,-2.2338993784519388,F
+asc_1day,-2.7588707215613267,F
+asc_23day,-1.7439620734289143,F
+asc_4day,-2.157902150190833,F
diff --git a/model/configs/telework_arrangement.csv b/model/configs/telework_arrangement.csv
new file mode 100644
index 0000000..4710458
--- /dev/null
+++ b/model/configs/telework_arrangement.csv
@@ -0,0 +1,22 @@
+Label,Description,Expression,has_in_home_work_activity,no_in_home_work_activity
+util_acs,alternative specific constant,@1,coef_acs_has_in_home_work_activity,
+util_part_time_worker,partime worker,ptype==2,coef_parttime_has_in_home,
+util_univ_student,university student,ptype==3,coef_univ_student_has_in_home,
+util_driving_age_student,driving age student,ptype==6,-999,
+util_has_preschool_at_home,has preschool children at home on the day,has_preschool_kid_at_home,coef_has_preschool_at_home_has_in_home,
+util_income_100_150,income 100-150k,(income_in_thousands>=100)&(income_in_thousands<150),coef_income_100_150,
+util_income_150p,income 150k plus,income_in_thousands>=150,coef_income_150p,
+util_age_above_35,age above 35,age>=35,coef_age_above_35,
+util_workplace_mc_logsum,workplace mode choice logsum,"@np.where(df.workplace_zone_id>-1, df.workplace_modechoice_logsum, 0)",coef_workplace_mc_logsum,
+# interaction terms,,,,
+util_telecommute_1_day_cdap_N,telecommute 1 day and cdap non-mandatory,"(telecommute_frequency==""1_day_week"") & (cdap_activity==""N"")",coef_telecommute_1_day_cdap_N,
+util_telecommute_2_3_days_cdap_N,telecommute 2-3 days and cdap non-mandatory,"(telecommute_frequency==""2_3_days_week"") & (cdap_activity==""N"")",coef_telecommute_2_3_4p_days_cdap_N,
+util_telecommute_4p_days_cdap_N,telecommute 4p days and cdap non-mandatory,"(telecommute_frequency==""4_days_week"") & (cdap_activity==""N"")",coef_telecommute_2_3_4p_days_cdap_N,
+util_telecommute_1_day_cdap_H,telecommute 1 day and cdap home,"(telecommute_frequency==""1_day_week"") & (cdap_activity==""H"")",coef_telecommute_1_day_cdap_H,
+util_telecommute_2_3_days_cdap_H,telecommute 2-3 days and cdap home,"(telecommute_frequency==""2_3_days_week"") & (cdap_activity==""H"")",coef_telecommute_2_3_days_cdap_H,
+util_telecommute_4p_days_cdap_H,telecommute 4p days and cdap home,"(telecommute_frequency==""4_days_week"") & (cdap_activity==""H"")",coef_telecommute_4p_days_cdap_H,
+util_work_from_home_cdap_N,work from home and cdap non-mandatory,"(work_from_home==True) & (cdap_activity==""N"")",coef_work_from_home_cdap_N,
+util_work_from_home_cdap_H,work from home and cdap home,"(work_from_home==True) & (cdap_activity==""H"")",coef_work_from_home_cdap_H,
+# industry,,,,
+util_industry_business_srv,srv industry_business,@df.naics_code==54,coef_industry_business_srv,
+util_cdap_N_non_mandatory_accessibility,CDAP N and Disaggreage non-mandatory accessibility at home location,"(cdap_activity==""N"")*auPkRetail",coef_cdap_N_non_mandatory_accessibility,
diff --git a/model/configs/telework_arrangement.yaml b/model/configs/telework_arrangement.yaml
new file mode 100644
index 0000000..20cd8c1
--- /dev/null
+++ b/model/configs/telework_arrangement.yaml
@@ -0,0 +1,6 @@
+SPEC: telework_arrangement.csv
+COEFFICIENTS: telework_arrangement_coeffs.csv
+
+LOGIT_TYPE: MNL
+
+HAS_IN_HOME_WORK_ACTIVITY_ALT: 0
\ No newline at end of file
diff --git a/model/configs/telework_arrangement_coeffs.csv b/model/configs/telework_arrangement_coeffs.csv
new file mode 100644
index 0000000..478c700
--- /dev/null
+++ b/model/configs/telework_arrangement_coeffs.csv
@@ -0,0 +1,19 @@
+coefficient_name,value,constrain
+coef_acs_has_in_home_work_activity,-1.28,F
+coef_age_above_35,-0.155,F
+coef_cdap_N_non_mandatory_accessibility,0.012,F
+coef_dummy_missing_workplace,-0.0295,F
+coef_has_preschool_at_home_has_in_home,0.384,F
+coef_income_100_150,0.187,F
+coef_income_150p,0.293,F
+coef_industry_business_srv,0.65,F
+coef_parttime_has_in_home,-0.497,F
+coef_telecommute_1_day_cdap_H,0.684,F
+coef_telecommute_1_day_cdap_N,0.298,F
+coef_telecommute_2_3_4p_days_cdap_N,1.73,F
+coef_telecommute_2_3_days_cdap_H,1.57,F
+coef_telecommute_4p_days_cdap_H,1.87,F
+coef_univ_student_has_in_home,-0.798,F
+coef_work_from_home_cdap_H,1.95,F
+coef_work_from_home_cdap_N,2.25,F
+coef_workplace_mc_logsum,-0.00867,F
diff --git a/model/configs/telework_duration.csv b/model/configs/telework_duration.csv
new file mode 100644
index 0000000..305ee71
--- /dev/null
+++ b/model/configs/telework_duration.csv
@@ -0,0 +1,9 @@
+Label,Description,Expression,3-5 hours,5-7 hours,7-9 hours,9+ hours
+util_asc,alternative specific constant,@1,coef_acs_3_5,coef_acs_5_7,,coef_asc_9_plus
+util_part_time,part-time worker,@df.ptype==2,coef_part_time_3_5,coef_part_time_5_7,,coef_part_time_9_plus
+util_university_student,university student,@df.ptype==3,coef_university_student_3_5,coef_university_student_5_7,,
+util_work_from_home,work from home,work_from_home,coef_work_from_home_3_7,coef_work_from_home_3_7,,coef_work_from_home_9_plus
+util_hybrid,hybrid worker,(cdap_activity=='M')&(ptype<3),,,coef_hybrid_7_plus,coef_hybrid_7_plus
+util_income_100_150,income 100-150k,(income_in_thousands>=100)&(income_in_thousands<150),coef_income_100_150_3_7,coef_income_100_150_3_7,,
+util_income_150_plus,income 150k+,(income_in_thousands>=150),coef_income_150_plus_3_5,coef_income_150_plus_5_7,,
+util_workplace_mc_logsum,workplace mode choice logsum,"@np.where(df.workplace_zone_id > -1, df.workplace_modechoice_logsum, 0)",coef_workplace_mc_logsum_3_7,coef_workplace_mc_logsum_3_7,,
\ No newline at end of file
diff --git a/model/configs/telework_duration.yaml b/model/configs/telework_duration.yaml
new file mode 100644
index 0000000..025843e
--- /dev/null
+++ b/model/configs/telework_duration.yaml
@@ -0,0 +1,24 @@
+# CHOICE_MODEL: PROBABILISTIC
+CHOICE_MODEL: MNL
+
+# Worker filters: apply only to workers with in-home work activity.
+CHOOSER_FILTER_COLUMN_NAME: has_in_home_work_activity
+
+# Output columns on persons table.
+DURATION_CATEGORY_COLUMN_NAME: telework_duration_category
+DURATION_HOURS_COLUMN_NAME: telework_duration_hours
+
+# Shared alternatives for both model types.
+ALTS: telework_duration_alts.csv
+ALT_NAME_COLUMN: alt
+ALT_DURATION_COLUMN: duration_hours
+
+# Probabilistic configuration.
+PROBS_SPEC: telework_duration_probs.csv
+PROBS_JOIN_COLS: null
+
+# MNL configuration (used when CHOICE_MODEL: MNL).
+SPEC: telework_duration.csv
+COEFFICIENTS: telework_duration_coeffs.csv
+LOGIT_TYPE: MNL
+
diff --git a/model/configs/telework_duration_alts.csv b/model/configs/telework_duration_alts.csv
new file mode 100644
index 0000000..58006f8
--- /dev/null
+++ b/model/configs/telework_duration_alts.csv
@@ -0,0 +1,5 @@
+alt,duration_hours
+3-5 hours,4
+5-7 hours,6
+7-9 hours,8
+9+ hours,10
diff --git a/model/configs/telework_duration_coeffs.csv b/model/configs/telework_duration_coeffs.csv
new file mode 100644
index 0000000..e9474fc
--- /dev/null
+++ b/model/configs/telework_duration_coeffs.csv
@@ -0,0 +1,18 @@
+coefficient_name,value,constrain
+coef_acs_3_5,-1.32,F
+coef_acs_5_7,-1.40,F
+coef_asc_9_plus,-1.47,F
+coef_dummy_missing_workplace_3_5,1.4,F
+coef_dummy_missing_workplace_5_7,0.995,F
+coef_hybrid_7_plus,-0.323,F
+coef_income_100_150_3_7,-0.189,F
+coef_income_150_plus_3_5,-0.201,F
+coef_income_150_plus_5_7,-0.342,F
+coef_part_time_3_5,1.45,F
+coef_part_time_5_7,1.28,F
+coef_part_time_9_plus,-0.849,F
+coef_university_student_3_5,1.74,F
+coef_university_student_5_7,1.42,F
+coef_work_from_home_3_7,-0.297,F
+coef_work_from_home_9_plus,-0.341,F
+coef_workplace_mc_logsum_3_7,0.138,F
diff --git a/model/configs/work_from_home.csv b/model/configs/work_from_home.csv
new file mode 100644
index 0000000..59e7682
--- /dev/null
+++ b/model/configs/work_from_home.csv
@@ -0,0 +1,23 @@
+Label,Description,Expression,work_at_home,work_away_from_home
+util_work_from_home_constant,Constant for Working from home,1,coef_work_from_home_constant,
+util_part_time_worker,Part time worker,@df.ptype==2,coef_part_time_worker,
+#util_access_to_workplaces,Accessibility to workplaces of the home zone,@df.workplace_location_accessibility,,coef_access_to_workplaces
+util_access_to_employments,Accessibility to employments of the home zone,@df.auPkTotal,,coef_access_to_workplaces
+util_age_35_to_44,Age Group - 35 yrs to 44 yrs,"@df.age.between(35, 44)",coef_age_35_to_44,
+util_age_45_to_54,Age Group - 45 yrs to 54 yrs,"@df.age.between(45, 54)",coef_age_45_to_54,
+util_age_55_to_64,Age Group - 55 yrs to 64 yrs,"@df.age.between(55, 64)",coef_age_55_to_64,
+util_age_65_79,Age 65-79,"@df.age.between(65, 79)",coef_age_65_79,
+util_age_80_plus,Age 80 plus,@df.age > 79,coef_age_80_plus,
+util_inc_lt_15,Household income less than 15k,@df.income<15000,coef_inc_lt_15,
+util_inc_150_250,Household income between 150k-249999k,"@df.income.between(150000,249999)",coef_inc_150_250,
+util_ind_accom,Industry type is accomodation,@df.naics_code==721,coef_ind_accom,
+util_ind_bus_srv,Industry type is business services,@df.naics_code==54,coef_ind_bus_srv,
+util_ind_construct,Industry type is construction,@df.naics_code==23,coef_ind_construct,
+util_ind_edu,Industry type is education,@df.naics_code==61,coef_ind_edu,
+util_ind_enter,Industry type is entertainment,@df.naics_code==71,coef_ind_enter,
+util_ind_food_srv,Industry type is food services,@df.naics_code==722,coef_ind_food_srv,
+util_ind_gov,Industry type is government,@df.naics_code==92,coef_ind_gov,
+util_ind_health,Industry type is healthcare,@df.naics_code==62,coef_ind_health,
+util_ind_manu,Industry type is manufacturing,"@df.naics_code.isin([31,32,33])",coef_ind_manu,
+util_ind_mgmt_srv,Industry type is management services,@df.naics_code==55,coef_ind_mgmt_srv,
+util_ind_mil,Industry type is military,@df.naics_code==9000,coef_ind_mil,
\ No newline at end of file
diff --git a/model/configs/work_from_home.yaml b/model/configs/work_from_home.yaml
new file mode 100644
index 0000000..de48357
--- /dev/null
+++ b/model/configs/work_from_home.yaml
@@ -0,0 +1,12 @@
+
+# borrowed from free parking model
+
+SPEC: work_from_home.csv
+COEFFICIENTS: work_from_home_coeffs.csv
+
+#LOGIT_TYPE: NL
+LOGIT_TYPE: MNL
+
+WORK_FROM_HOME_ALT: 0
+
+DEST_CHOICE_COLUMN_NAME: workplace_zone_id
\ No newline at end of file
diff --git a/model/configs/work_from_home_coeffs.csv b/model/configs/work_from_home_coeffs.csv
new file mode 100644
index 0000000..4cbc233
--- /dev/null
+++ b/model/configs/work_from_home_coeffs.csv
@@ -0,0 +1,22 @@
+coefficient_name,value,constrain
+coef_work_from_home_constant,-0.386629856,F
+coef_part_time_worker,-1.4513809,F
+coef_access_to_workplaces,0.1,T
+coef_age_35_to_44,0.353530241,F
+coef_age_45_to_54,0.468133972,F
+coef_age_55_to_64,0.495172418,F
+coef_age_65_79,1.243578944,F
+coef_age_80_plus,0.770722144,F
+coef_inc_lt_15,0.613583177,F
+coef_inc_150_250,0.419020695,F
+coef_ind_accom,-1.639185004,F
+coef_ind_bus_srv,0.721333596,F
+coef_ind_construct,-0.738399459,F
+coef_ind_edu,-0.508510677,F
+coef_ind_enter,1.027744147,F
+coef_ind_food_srv,-1.204685952,F
+coef_ind_gov,-0.75445486,F
+coef_ind_health,-0.441707723,F
+coef_ind_manu,-0.566600437,F
+coef_ind_mgmt_srv,0.386847827,F
+coef_ind_mil,-2.257002339,F
\ No newline at end of file
diff --git a/model/configs/workplace_location.yaml b/model/configs/workplace_location.yaml
index e20c14f..e5d8c5d 100644
--- a/model/configs/workplace_location.yaml
+++ b/model/configs/workplace_location.yaml
@@ -16,6 +16,7 @@ OUT_PERIOD: 8
 DEST_CHOICE_COLUMN_NAME: workplace_zone_id
 # comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if not desired in persons table
 DEST_CHOICE_LOGSUM_COLUMN_NAME: workplace_location_logsum
+MODE_CHOICE_LOGSUM_COLUMN_NAME: workplace_modechoice_logsum
 
 # comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table
 DEST_CHOICE_SAMPLE_TABLE_NAME: workplace_location_sample
@@ -40,7 +41,7 @@ MODEL_SELECTOR: workplace
 CHOOSER_SEGMENT_COLUMN_NAME: income_segment
 
 # boolean column to filter choosers (True means keep)
-CHOOSER_FILTER_COLUMN_NAME: is_worker
+CHOOSER_FILTER_COLUMN_NAME: is_out_of_home_worker
 # FIXME - these are assigned to persons in annotate_persons. we need a better way to manage this
 # FIXME - these are not needed for this model and should be re/factored out
 SEGMENT_IDS:
diff --git a/model/configs_mp/settings.yaml b/model/configs_mp/settings.yaml
index b129f81..6af121d 100644
--- a/model/configs_mp/settings.yaml
+++ b/model/configs_mp/settings.yaml
@@ -45,11 +45,15 @@ models:
   ### mp_accessibility step
   - compute_accessibility
   ### mp_households step
+  - work_from_home
   - school_location
   - workplace_location
   - auto_ownership_simulate
   - free_parking
+  - telecommute_frequency
   - cdap_simulate
+  - telework_arrangement
+  - telework_duration
   - mandatory_tour_frequency
   - mandatory_tour_scheduling
   - non_mandatory_tour_frequency
@@ -82,7 +86,7 @@ multiprocess_steps:
       # don't slice any tables not explicitly listed above in slice.tables
       exclude: true
   - name: mp_households
-    begin: school_location
+    begin: work_from_home
     slice:
       tables: [households, persons]
   - name: mp_summarize