feat: add relocation runway calculator

Made-with: Cursor

agent/evals/test_relocation_runway.py

@@ -0,0 +1,62 @@
+import sys
+import os
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', 'tools'))
+from relocation_runway import calculate_relocation_runway
+
+
+def test_runway_seattle_vs_austin():
+    result = calculate_relocation_runway(
+        current_salary=120000, offer_salary=180000,
+        current_city="Austin", destination_city="Seattle",
+        portfolio_value=94000
+    )
+    assert result["destination_monthly"]["monthly_surplus"] is not None
+    assert "months_to_6mo_emergency_fund" in result["milestones_if_you_move"]
+    assert "verdict" in result
+    assert "key_insight" in result
+    assert result["scenario"]["offer"]["city"] == "Seattle"
+
+
+def test_runway_impossible_offer():
+    result = calculate_relocation_runway(
+        current_salary=120000, offer_salary=40000,
+        current_city="Austin", destination_city="San Francisco",
+        portfolio_value=94000
+    )
+    assert result is not None
+    assert "destination_monthly" in result
+    surplus = result["destination_monthly"]["monthly_surplus"]
+    warning = result["destination_monthly"].get("monthly_surplus_warning", False)
+    assert surplus <= 0 or warning is True
+
+
+def test_runway_moving_to_affordable_city():
+    result = calculate_relocation_runway(
+        current_salary=120000, offer_salary=110000,
+        current_city="San Francisco", destination_city="Austin",
+        portfolio_value=50000
+    )
+    assert "verdict" in result
+    assert result["destination_monthly"]["housing_cost"] < 3000
+
+
+def test_runway_global_city():
+    result = calculate_relocation_runway(
+        current_salary=100000, offer_salary=150000,
+        current_city="Austin", destination_city="Berlin",
+        portfolio_value=75000
+    )
+    assert "verdict" in result
+    assert result["scenario"]["offer"]["city"] == "Berlin"
+
+
+def test_runway_returns_comparison():
+    result = calculate_relocation_runway(
+        current_salary=120000, offer_salary=180000,
+        current_city="Austin", destination_city="Denver",
+        portfolio_value=94000
+    )
+    assert "milestones_if_you_move" in result
+    assert "milestones_if_you_stay" in result
+    assert "months_to_down_payment_20pct" in result["milestones_if_you_move"]
+    assert "months_to_down_payment_20pct" in result["milestones_if_you_stay"]

agent/graph.py

@@ -411,6 +411,52 @@ async def classify_node(state: AgentState) -> AgentState:
             return {**state, "query_type": "compliance+tax"}
         return {**state, "query_type": "tax"}
 
+    # --- Relocation Runway Calculator ---
+    relocation_runway_kws = [
+        "how long until", "runway", "financially stable",
+        "if i move", "relocation timeline", "stable if",
+        "how long to feel stable", "feel stable after",
+    ]
+    if any(kw in query for kw in relocation_runway_kws):
+        return {**state, "query_type": "relocation_runway"}
+
+    # --- Wealth Gap Visualizer ---
+    wealth_gap_kws = [
+        "am i behind", "am i on track", "wealth gap",
+        "how am i doing financially", "ahead or behind",
+        "net worth compared", "am i ahead",
+        "am i behind for my age", "retirement on track",
+    ]
+    if any(kw in query for kw in wealth_gap_kws):
+        return {**state, "query_type": "wealth_gap"}
+
+    # --- Life Decision Advisor ---
+    life_decision_kws = [
+        "should i take", "help me decide", "what should i do",
+        "is it worth it", "advise me", "what do you think",
+        "should i move", "should i accept",
+    ]
+    if any(kw in query for kw in life_decision_kws):
+        return {**state, "query_type": "life_decision"}
+
+    # --- Equity Unlock Advisor ---
+    equity_unlock_kws = [
+        "home equity", "refinance", "cash out",
+        "equity options", "what should i do with my equity",
+    ]
+    if any(kw in query for kw in equity_unlock_kws):
+        return {**state, "query_type": "equity_unlock"}
+
+    # --- Family Financial Planner ---
+    family_planner_kws = [
+        "afford a family", "afford a baby", "afford kids",
+        "childcare costs", "financial impact of children",
+        "can i afford to have", "family planning",
+        "having kids",
+    ]
+    if any(kw in query for kw in family_planner_kws):
+        return {**state, "query_type": "family_planner"}
+
     # --- Wealth Bridge — down payment, job offer COL, global city data ---
     # Checked before real estate so "can I afford" doesn't fall through to snapshot
     if is_real_estate_enabled():

agent/tools/relocation_runway.py

@@ -0,0 +1,287 @@
+"""
+Relocation Runway Calculator
+Answers: "How long until I feel financially stable if I move?"
+"""
+
+import sys
+import os
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+
+try:
+    from real_estate import MOCK_DATA as AUSTIN_DATA
+except ImportError:
+    AUSTIN_DATA = {}
+
+try:
+    from teleport_api import get_city_housing_data
+    TELEPORT_AVAILABLE = True
+except ImportError:
+    TELEPORT_AVAILABLE = False
+
+    def get_city_housing_data(city):
+        return {"MedianRentMonthly": 2000, "median_price": 400000}
+
+
+def estimate_take_home(annual_salary: float, city_name: str = "") -> float:
+    if annual_salary <= 44725:
+        federal_rate = 0.12
+    elif annual_salary <= 95375:
+        federal_rate = 0.22
+    elif annual_salary <= 200000:
+        federal_rate = 0.24
+    else:
+        federal_rate = 0.32
+
+    fica = 0.0765
+
+    no_income_tax = [
+        "tx", "wa", "fl", "nv", "tn", "wy", "sd", "ak",
+        "texas", "washington", "florida", "nevada",
+        "tennessee", "wyoming", "south dakota", "alaska",
+        "austin", "seattle", "miami", "nashville",
+        "dallas", "houston", "san antonio",
+    ]
+
+    city_lower = city_name.lower()
+    has_state_tax = not any(s in city_lower for s in no_income_tax)
+    state_rate = 0.05 if has_state_tax else 0.0
+
+    total_rate = federal_rate + fica + state_rate
+    annual_take_home = annual_salary * (1 - total_rate)
+    return annual_take_home / 12
+
+
+def get_city_data_safe(city_name: str) -> dict:
+    """Gets city data from ACTRIS for Austin areas, Teleport for everything else.
+    Never crashes."""
+    austin_keywords = [
+        "austin", "travis", "williamson", "hays", "bastrop",
+        "caldwell", "round rock", "cedar park", "georgetown",
+        "kyle", "buda", "san marcos", "lockhart", "bastrop",
+        "elgin", "leander", "pflugerville", "manor", "del valle",
+    ]
+
+    city_lower = city_name.lower()
+
+    for keyword in austin_keywords:
+        if keyword in city_lower:
+            if any(k in city_lower for k in [
+                "round rock", "cedar park", "georgetown", "leander", "williamson"
+            ]):
+                return AUSTIN_DATA.get(
+                    "williamson_county",
+                    {"MedianRentMonthly": 1995, "median_price": 403500},
+                )
+            elif any(k in city_lower for k in ["kyle", "buda", "san marcos", "wimberley", "hays"]):
+                return AUSTIN_DATA.get(
+                    "hays_county",
+                    {"MedianRentMonthly": 1937, "median_price": 344500},
+                )
+            elif any(k in city_lower for k in ["bastrop", "elgin"]):
+                return AUSTIN_DATA.get(
+                    "bastrop_county",
+                    {"MedianRentMonthly": 1860, "median_price": 335970},
+                )
+            elif any(k in city_lower for k in ["lockhart", "caldwell"]):
+                return AUSTIN_DATA.get(
+                    "caldwell_county",
+                    {"MedianRentMonthly": 1750, "median_price": 237491},
+                )
+            else:
+                return AUSTIN_DATA.get(
+                    "austin",
+                    {"MedianRentMonthly": 2100, "median_price": 522500},
+                )
+
+    if TELEPORT_AVAILABLE:
+        try:
+            import asyncio
+            # get_city_housing_data is async — run it synchronously
+            data = asyncio.run(get_city_housing_data(city_name))
+            if data and "MedianRentMonthly" in data:
+                return data
+        except Exception:
+            pass
+
+    FALLBACK_RENTS = {
+        "san francisco": {"MedianRentMonthly": 3200, "median_price": 1350000},
+        "seattle": {"MedianRentMonthly": 2400, "median_price": 850000},
+        "new york": {"MedianRentMonthly": 3800, "median_price": 750000},
+        "denver": {"MedianRentMonthly": 1900, "median_price": 565000},
+        "chicago": {"MedianRentMonthly": 1850, "median_price": 380000},
+        "miami": {"MedianRentMonthly": 2800, "median_price": 620000},
+        "boston": {"MedianRentMonthly": 3100, "median_price": 720000},
+        "los angeles": {"MedianRentMonthly": 2900, "median_price": 950000},
+        "nashville": {"MedianRentMonthly": 1800, "median_price": 450000},
+        "dallas": {"MedianRentMonthly": 1700, "median_price": 380000},
+        "london": {"MedianRentMonthly": 2800, "median_price": 720000},
+        "toronto": {"MedianRentMonthly": 2300, "median_price": 980000},
+        "sydney": {"MedianRentMonthly": 2600, "median_price": 1100000},
+        "berlin": {"MedianRentMonthly": 1600, "median_price": 520000},
+        "tokyo": {"MedianRentMonthly": 1800, "median_price": 650000},
+        "paris": {"MedianRentMonthly": 2200, "median_price": 800000},
+    }
+
+    for key, val in FALLBACK_RENTS.items():
+        if key in city_lower:
+            return val
+
+    return {
+        "MedianRentMonthly": 2000,
+        "median_price": 450000,
+        "city": city_name,
+        "data_source": "estimate",
+    }
+
+
+def calculate_relocation_runway(
+    current_salary: float,
+    offer_salary: float,
+    current_city: str,
+    destination_city: str,
+    portfolio_value: float,
+    current_savings_rate: float = 0.15,
+) -> dict:
+    """Calculate how long until financially stable after relocating."""
+
+    # Step 1: Get city data
+    dest_data = get_city_data_safe(destination_city)
+    curr_data = get_city_data_safe(current_city)
+
+    # Step 2: Monthly take-home
+    dest_take_home = estimate_take_home(offer_salary, destination_city)
+    curr_take_home = estimate_take_home(current_salary, current_city)
+
+    # Step 3: Monthly costs
+    def _monthly_costs(city_data: dict) -> tuple:
+        rent = city_data.get("MedianRentMonthly", 2000)
+        food_transport = rent * 0.8
+        utilities_misc = rent * 0.3
+        total = rent + food_transport + utilities_misc
+        return rent, total
+
+    dest_rent, dest_total_costs = _monthly_costs(dest_data)
+    curr_rent, curr_total_costs = _monthly_costs(curr_data)
+
+    # Step 4: Monthly surplus
+    dest_surplus = dest_take_home - dest_total_costs
+    curr_surplus = curr_take_home - curr_total_costs
+    dest_surplus_warning = dest_surplus <= 0
+
+    # Step 5: Milestones for destination
+    dest_price = dest_data.get("ListPrice", dest_data.get("median_price", 500000))
+    dest_down_payment = dest_price * 0.20
+    dest_emergency_3mo = dest_total_costs * 3
+    dest_emergency_6mo = dest_total_costs * 6
+    portfolio_liquid = portfolio_value * 0.1
+    portfolio_down = portfolio_value * 0.2
+
+    if dest_surplus > 0:
+        months_to_3mo_dest = int(max(0, (dest_emergency_3mo - portfolio_liquid) / dest_surplus))
+        months_to_6mo_dest = int(max(0, (dest_emergency_6mo - portfolio_liquid) / dest_surplus))
+        months_to_down_dest = int(max(0, (dest_down_payment - portfolio_down) / dest_surplus))
+    else:
+        months_to_3mo_dest = months_to_6mo_dest = months_to_down_dest = 9999
+
+    # Step 6: Milestones for current city
+    curr_price = curr_data.get("ListPrice", curr_data.get("median_price", 500000))
+    curr_down_payment = curr_price * 0.20
+    curr_emergency_3mo = curr_total_costs * 3
+    curr_emergency_6mo = curr_total_costs * 6
+
+    if curr_surplus > 0:
+        months_to_3mo_curr = int(max(0, (curr_emergency_3mo - portfolio_liquid) / curr_surplus))
+        months_to_6mo_curr = int(max(0, (curr_emergency_6mo - portfolio_liquid) / curr_surplus))
+        months_to_down_curr = int(max(0, (curr_down_payment - portfolio_down) / curr_surplus))
+    else:
+        months_to_3mo_curr = months_to_6mo_curr = months_to_down_curr = 9999
+
+    # Step 7: Build verdict
+    salary_delta = offer_salary - current_salary
+    salary_pct = (salary_delta / current_salary) * 100 if current_salary > 0 else 0
+    surplus_delta = dest_surplus - curr_surplus
+
+    if dest_surplus_warning:
+        verdict = (
+            f"Warning: The ${offer_salary:,.0f} offer in {destination_city} leaves you "
+            f"with negative monthly surplus. The cost of living exceeds take-home pay."
+        )
+        key_insight = (
+            f"You would need at least ${dest_total_costs * 12:,.0f}/yr just to cover "
+            f"basic living costs in {destination_city}."
+        )
+    elif surplus_delta > 500:
+        verdict = (
+            f"Strong move: The {destination_city} offer gives you "
+            f"${dest_surplus:,.0f}/mo surplus vs ${curr_surplus:,.0f}/mo now — "
+            f"${surplus_delta:,.0f}/mo improvement."
+        )
+        key_insight = (
+            f"Despite the higher cost of living, the {salary_pct:+.1f}% salary bump "
+            f"in {destination_city} meaningfully improves your monthly runway."
+        )
+    elif surplus_delta > 0:
+        verdict = (
+            f"Marginal improvement: {destination_city} gives slightly more surplus "
+            f"(${dest_surplus:,.0f}/mo vs ${curr_surplus:,.0f}/mo now)."
+        )
+        key_insight = (
+            f"The salary increase is mostly absorbed by {destination_city}'s higher costs. "
+            f"Negotiate for more before accepting."
+        )
+    elif surplus_delta > -300:
+        verdict = (
+            f"Roughly equivalent: {destination_city} surplus (${dest_surplus:,.0f}/mo) "
+            f"is close to your current (${curr_surplus:,.0f}/mo)."
+        )
+        key_insight = (
+            f"The {salary_pct:+.1f}% salary change is offset by {destination_city}'s cost "
+            f"of living. Non-financial factors should decide this."
+        )
+    else:
+        verdict = (
+            f"Financial step back: Moving to {destination_city} reduces your monthly "
+            f"surplus by ${abs(surplus_delta):,.0f}/mo."
+        )
+        key_insight = (
+            f"The higher salary does not cover {destination_city}'s cost premium. "
+            f"You would need ~${offer_salary - salary_delta + abs(surplus_delta) * 12:,.0f}/yr "
+            f"to maintain your current financial position."
+        )
+
+    return {
+        "scenario": {
+            "current": {"city": current_city, "salary": current_salary},
+            "offer": {"city": destination_city, "salary": offer_salary},
+        },
+        "destination_monthly": {
+            "take_home": round(dest_take_home),
+            "housing_cost": round(dest_rent),
+            "total_living_costs": round(dest_total_costs),
+            "monthly_surplus": round(dest_surplus),
+            "monthly_surplus_warning": dest_surplus_warning,
+        },
+        "current_monthly": {
+            "take_home": round(curr_take_home),
+            "housing_cost": round(curr_rent),
+            "total_living_costs": round(curr_total_costs),
+            "monthly_surplus": round(curr_surplus),
+        },
+        "milestones_if_you_move": {
+            "months_to_3mo_emergency_fund": months_to_3mo_dest,
+            "months_to_6mo_emergency_fund": months_to_6mo_dest,
+            "months_to_down_payment_20pct": months_to_down_dest,
+            "down_payment_target": round(dest_down_payment),
+            "destination_median_home_price": round(dest_price),
+        },
+        "milestones_if_you_stay": {
+            "months_to_3mo_emergency_fund": months_to_3mo_curr,
+            "months_to_6mo_emergency_fund": months_to_6mo_curr,
+            "months_to_down_payment_20pct": months_to_down_curr,
+            "down_payment_target": round(curr_down_payment),
+            "current_median_home_price": round(curr_price),
+        },
+        "verdict": verdict,
+        "key_insight": key_insight,
+        "data_source": "ACTRIS MLS Jan 2026 (Austin) + Teleport API (global) + fallback estimates",
+    }