diff --git a/changelog.d/hud-fmr-encoding.added.md b/changelog.d/hud-fmr-encoding.added.md new file mode 100644 index 00000000000..675b4c2e44f --- /dev/null +++ b/changelog.d/hud-fmr-encoding.added.md @@ -0,0 +1 @@ +Added the HUD Fair Market Rent CSV data store, a `hud_fair_market_rent` variable, and a `download_hud_fmr` helper script (Phase 1: county-level FY2025 with a placeholder seed row — full data refresh runs via the script with a free HUD API key). diff --git a/policyengine_us/parameters/gov/hud/fmr/README.md b/policyengine_us/parameters/gov/hud/fmr/README.md new file mode 100644 index 00000000000..6923d06d849 --- /dev/null +++ b/policyengine_us/parameters/gov/hud/fmr/README.md @@ -0,0 +1,48 @@ +# HUD Fair Market Rents (FMRs) + +## What + +`fair_market_rents.csv` holds HUD's published Fair Market Rents at the county +level, indexed by `(state, county_fips, year, bedrooms)`. FMRs are the +40th-percentile gross-rent estimates HUD uses to cap rents under the Housing +Choice Voucher program, set Low-Income Housing Tax Credit rent limits, and +size other federal housing subsidies (24 CFR Part 888). + +The model variable `hud_fair_market_rent` reads from this CSV. + +## Scope today (Phase 1) + +- **Year**: FY2025 only. +- **Geography**: county-level FMR area only (no SAFMR ZIP-level resolution + yet — that lands in Phase 1B). +- **Seed data**: a handful of placeholder rows (LA County) so the variable + and tests have something to bind to. Run `tools/download_hud_fmr.py` to + populate the full ~3,000-row county file from HUD's published data. + +## Schema + +| column | type | meaning | +|---|---|---| +| `state` | str | two-letter state abbreviation | +| `county_fips` | str | 5-digit county FIPS code (zero-padded) | +| `year` | int | HUD fiscal year | +| `bedrooms` | int | 0 (efficiency) through 4 | +| `value` | float | monthly FMR in current-year dollars | + +## Refresh + +``` +python -m policyengine_us.tools.download_hud_fmr --year 2025 --output \ + policyengine_us/parameters/gov/hud/fmr/fair_market_rents.csv +``` + +The script reads `HUD_API_TOKEN` from the environment (register a free key +at ). Re-running with a different `--year` +appends rather than overwriting. + +## Source + +- HUD User FY2025 FMR documentation: +- Federal Register notice: +- Regulatory citation: 24 CFR §888 (FMR rules), 24 CFR §982.503 (HCV payment + standards bound to 90-110 percent of FMR). diff --git a/policyengine_us/parameters/gov/hud/fmr/__init__.py b/policyengine_us/parameters/gov/hud/fmr/__init__.py new file mode 100644 index 00000000000..c44e7bad0e9 --- /dev/null +++ b/policyengine_us/parameters/gov/hud/fmr/__init__.py @@ -0,0 +1,10 @@ +import pandas as pd +from pathlib import Path + +FOLDER = Path(__file__).parent + +fair_market_rents = pd.read_csv( + FOLDER / "fair_market_rents.csv", + dtype={"county_fips": str}, +) +fair_market_rents["county_fips"] = fair_market_rents["county_fips"].str.zfill(5) diff --git a/policyengine_us/parameters/gov/hud/fmr/fair_market_rents.csv b/policyengine_us/parameters/gov/hud/fmr/fair_market_rents.csv new file mode 100644 index 00000000000..a23341433b7 --- 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+ input: + county_fips: "06037" + bedrooms: 2 + output: + # CSV value $2,750/mo × 12 = $33,000. Placeholder until + # tools/download_hud_fmr.py refreshes with HUD's published FY2025 + # value. + hud_fair_market_rent: 33_000 + +- name: Unmapped county returns zero + period: 2025 + input: + county_fips: "99999" + bedrooms: 2 + output: + hud_fair_market_rent: 0 + +- name: LA County 0BR FY2025 + period: 2025 + input: + county_fips: "06037" + bedrooms: 0 + output: + hud_fair_market_rent: 22_800 + +- name: LA County FY2024 (no data) returns zero + period: 2024 + input: + county_fips: "06037" + bedrooms: 2 + output: + hud_fair_market_rent: 0 diff --git a/policyengine_us/tools/convert_hud_fmr_xlsx.py b/policyengine_us/tools/convert_hud_fmr_xlsx.py new file mode 100644 index 00000000000..7013f9a7120 --- /dev/null +++ b/policyengine_us/tools/convert_hud_fmr_xlsx.py @@ -0,0 +1,125 @@ +"""Convert HUD's FY-XX FMR Excel workbook into the PolicyEngine CSV schema. + +HUD publishes one Excel file per fiscal year on huduser.gov (browse to +``Datasets > Fair Market Rents`` and download ``FYXX_FMRs.xlsx``). The +sheet structure is stable year to year: one row per FMR area, with +columns for each bedroom count and a 5-digit county FIPS. + +Dependency: ``python-calamine`` (``pip install python-calamine``). HUD's +workbooks ship with malformed metadata timestamps that openpyxl rejects; +calamine reads them cleanly. Not a runtime dependency of the model — only +needed when refreshing the bundled CSV. + +Usage: + python -m policyengine_us.tools.convert_hud_fmr_xlsx \ + --input ~/Downloads/FY25_FMRs.xlsx \ + --year 2025 \ + --output policyengine_us/parameters/gov/hud/fmr/fair_market_rents.csv + +The script merges into the existing CSV (de-duplicates on +``(state, county_fips, year, bedrooms)``) so re-running across multiple +year files appends rather than overwrites. +""" + +import argparse +import sys +from pathlib import Path + +import pandas as pd + +# HUD's column naming has drifted slightly over the years. The script +# tolerates the common variants for each field. +STATE_COLS = ("stusps", "state_alpha", "state_code") +COUNTY_COLS = ("fips", "fips2020", "fips2010", "fips_county", "cntycode") +BEDROOM_COLS = { + 0: ("fmr_0", "fmr0", "FMR_0BR", "Efficiency"), + 1: ("fmr_1", "fmr1", "FMR_1BR", "One-Bedroom"), + 2: ("fmr_2", "fmr2", "FMR_2BR", "Two-Bedroom"), + 3: ("fmr_3", "fmr3", "FMR_3BR", "Three-Bedroom"), + 4: ("fmr_4", "fmr4", "FMR_4BR", "Four-Bedroom"), +} + + +def pick(df: pd.DataFrame, candidates: tuple[str, ...], required: bool = True) -> str: + for name in candidates: + if name in df.columns: + return name + if required: + raise KeyError( + f"None of {candidates} present. Workbook columns: {list(df.columns)}" + ) + return "" + + +def read_sheet(path: Path) -> pd.DataFrame: + """Load the first sheet that contains an FMR column. + + Uses the ``calamine`` engine because HUD's published workbooks ship with + malformed timestamps in their metadata that openpyxl rejects. + """ + workbook = pd.ExcelFile(path, engine="calamine") + for sheet in workbook.sheet_names: + df = workbook.parse(sheet) + df.columns = [c.lower() if isinstance(c, str) else c for c in df.columns] + if any( + any(col == c for col in df.columns) + for variants in BEDROOM_COLS.values() + for c in variants + ): + return df + raise ValueError(f"No sheet in {path} contains an fmr_0..fmr_4 column.") + + +def reshape(df: pd.DataFrame, year: int) -> pd.DataFrame: + state_col = pick(df, STATE_COLS) + county_col = pick(df, COUNTY_COLS) + rows = [] + for bedrooms, candidates in BEDROOM_COLS.items(): + col = pick(df, tuple(c.lower() for c in candidates), required=False) + if not col: + print(f" bedrooms={bedrooms}: no column found, skipping", file=sys.stderr) + continue + slice_ = df[[state_col, county_col, col]].dropna() + slice_ = slice_.rename( + columns={state_col: "state", county_col: "county_fips", col: "value"} + ) + slice_["bedrooms"] = bedrooms + slice_["year"] = year + rows.append(slice_) + out = pd.concat(rows, ignore_index=True) + out["county_fips"] = out["county_fips"].astype(int).astype(str).str.zfill(5) + out["value"] = out["value"].astype(float) + return out[["state", "county_fips", "year", "bedrooms", "value"]].sort_values( + ["state", "county_fips", "year", "bedrooms"] + ) + + +def merge_with_existing(new: pd.DataFrame, output: Path) -> pd.DataFrame: + if not output.exists(): + return new + existing = pd.read_csv(output, dtype={"county_fips": str}) + existing["county_fips"] = existing["county_fips"].str.zfill(5) + combined = pd.concat([existing, new], ignore_index=True) + return combined.drop_duplicates( + subset=["state", "county_fips", "year", "bedrooms"], keep="last" + ).sort_values(["state", "county_fips", "year", "bedrooms"]) + + +def main() -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--input", type=Path, required=True) + parser.add_argument("--year", type=int, required=True) + parser.add_argument("--output", type=Path, required=True) + args = parser.parse_args() + + raw = read_sheet(args.input) + new = reshape(raw, args.year) + combined = merge_with_existing(new, args.output) + args.output.parent.mkdir(parents=True, exist_ok=True) + combined.to_csv(args.output, index=False) + print(f"Wrote {len(combined):,} rows to {args.output}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/policyengine_us/tools/download_hud_fmr.py b/policyengine_us/tools/download_hud_fmr.py new file mode 100644 index 00000000000..151d988300d --- /dev/null +++ b/policyengine_us/tools/download_hud_fmr.py @@ -0,0 +1,116 @@ +"""Fetch county-level Fair Market Rents from the HUD User API. + +Requires a free API token from https://www.huduser.gov/hudapi/. Set the +token in the environment as ``HUD_API_TOKEN`` before running. + +Usage: + python -m policyengine_us.tools.download_hud_fmr \ + --year 2025 \ + --output policyengine_us/parameters/gov/hud/fmr/fair_market_rents.csv + +The output CSV matches the schema documented in +``policyengine_us/parameters/gov/hud/fmr/README.md``. Re-running for a new +year appends rather than overwriting; the script de-duplicates on +``(state, county_fips, year, bedrooms)``. +""" + +import argparse +import os +import sys +import time +from pathlib import Path + +import pandas as pd +import requests + +API_ROOT = "https://www.huduser.gov/hudapi/public/fmr" + +# Bedroom keys in the HUD API response, mapped to integer counts. +BEDROOM_FIELDS = { + 0: "Efficiency", + 1: "One-Bedroom", + 2: "Two-Bedroom", + 3: "Three-Bedroom", + 4: "Four-Bedroom", +} + + +def get(url: str, token: str) -> dict: + headers = {"Authorization": f"Bearer {token}"} + response = requests.get(url, headers=headers, timeout=60) + response.raise_for_status() + return response.json() + + +def list_states(token: str) -> list[str]: + data = get(f"{API_ROOT}/listStates", token) + return [row["state_code"] for row in data] + + +def state_rows(state_code: str, year: int, token: str) -> pd.DataFrame: + """Return one row per (county_fips, bedrooms) for the state and year.""" + payload = get(f"{API_ROOT}/data/{state_code}?year={year}", token) + rows = [] + for county in payload.get("data", {}).get("counties", []): + fips = str(county.get("fips_code", "")).zfill(5) + for bedrooms, key in BEDROOM_FIELDS.items(): + value = county.get(key) + if value is None: + continue + rows.append( + { + "state": state_code, + "county_fips": fips, + "year": year, + "bedrooms": bedrooms, + "value": float(value), + } + ) + return pd.DataFrame(rows) + + +def fetch_year(year: int, token: str) -> pd.DataFrame: + frames = [] + for state in list_states(token): + try: + frames.append(state_rows(state, year, token)) + except requests.HTTPError as err: + print(f" {state}: skipped ({err})", file=sys.stderr) + time.sleep(0.1) # polite rate-limit + return pd.concat(frames, ignore_index=True) + + +def merge_with_existing(new: pd.DataFrame, output: Path) -> pd.DataFrame: + if not output.exists(): + return new + existing = pd.read_csv(output, dtype={"county_fips": str}) + combined = pd.concat([existing, new], ignore_index=True) + return combined.drop_duplicates( + subset=["state", "county_fips", "year", "bedrooms"], keep="last" + ).sort_values(["state", "county_fips", "year", "bedrooms"]) + + +def main() -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--year", type=int, required=True) + parser.add_argument("--output", type=Path, required=True) + args = parser.parse_args() + + token = os.environ.get("HUD_API_TOKEN") + if not token: + print( + "ERROR: set HUD_API_TOKEN (register at https://www.huduser.gov/hudapi/)", + file=sys.stderr, + ) + return 2 + + fetched = fetch_year(args.year, token) + combined = merge_with_existing(fetched, args.output) + args.output.parent.mkdir(parents=True, exist_ok=True) + combined.to_csv(args.output, index=False) + print(f"Wrote {len(combined):,} rows to {args.output}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/policyengine_us/variables/gov/hud/hud_fair_market_rent.py b/policyengine_us/variables/gov/hud/hud_fair_market_rent.py new file mode 100644 index 00000000000..378d6dd0ca3 --- /dev/null +++ b/policyengine_us/variables/gov/hud/hud_fair_market_rent.py @@ -0,0 +1,30 @@ +from policyengine_us.model_api import * +from policyengine_us.parameters.gov.hud.fmr import fair_market_rents + + +class hud_fair_market_rent(Variable): + value_type = float + entity = Household + label = "HUD Fair Market Rent" + unit = USD + definition_period = YEAR + reference = "https://www.huduser.gov/portal/datasets/fmr.html" + + def formula(household, period, parameters): + county_fips = household("county_fips", period) + bedrooms = household("bedrooms", period) + year = period.start.year + df = pd.DataFrame( + { + "county_fips": county_fips, + "bedrooms": bedrooms.astype(int), + "year": year, + } + ) + df["county_fips"] = df["county_fips"].astype(str).str.zfill(5) + matched = df.merge( + fair_market_rents.astype({"county_fips": str, "bedrooms": int}), + on=["county_fips", "bedrooms", "year"], + how="left", + ) + return matched["value"].fillna(0).to_numpy() * MONTHS_IN_YEAR