Datasets:
Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
city: string
state: string
metro: string
county: string
medianHomePrice: int64
medianPriceChangeYoY: double
medianPriceChange5Yr: double
annualInsurancePremium: int64
propertyTaxRate: double
medianHouseholdIncome: int64
incomeSource: string
daysOnMarket: int64
priceReductionShare: double
dataMonth: string
marketCondition: string
usdaEligible: bool
militaryProximity: bool
gnlInsight: string
conformingLoanLimit: int64
fhaLoanLimit: int64
medianRent: int64
rentToBuyRatio: double
citySlug: string
cities: null
incomeYear: int64
counties: list<item: struct<county: string, stateName: string, stateAbbr: string, medianHomePrice: int64, medi (... 287 chars omitted)
child 0, item: struct<county: string, stateName: string, stateAbbr: string, medianHomePrice: int64, medianHousehold (... 275 chars omitted)
child 0, county: string
child 1, stateName: string
child 2, stateAbbr: string
child 3, medianHomePrice: int64
child 4, medianHouseholdIncome: int64
child 5, usdaEligible: bool
child 6, militaryCounty: bool
child 7, propertyTaxRate: double
child 8, avgInsuranceAnnual: int64
child 9, medianPriceChange12Mo: double
child 10, marketCondition: string
child 11, daysOnMarket: int64
child 12, primaryLoanProgram: string
child 13, fipsCode: string
child 14, estimatedPITI: int64
child 15, thompsonNote: string
meta: struct<dataMonth: timestamp[s], publishedAt: string, cityCount: int64, countyCount: int64
...
list<item: string>
child 0, item: string
child 6, domain: string
child 7, brands: list<item: string>
child 0, item: string
child 8, repo: string
child 9, enrichmentPhase: int64
child 10, thompsonInsightGenerated: bool
child 11, thompsonInsightAICount: int64
child 12, thompsonInsightTemplateCount: int64
child 13, gnlInsightGenerated: bool
child 14, expandedJune2026: bool
child 15, countyIncomeSource: string
child 16, brand: string
states: list<item: struct<stateAbbr: string, stateName: string, cityCount: int64, countyCount: int64, avgIns (... 301 chars omitted)
child 0, item: struct<stateAbbr: string, stateName: string, cityCount: int64, countyCount: int64, avgInsurancePremi (... 289 chars omitted)
child 0, stateAbbr: string
child 1, stateName: string
child 2, cityCount: int64
child 3, countyCount: int64
child 4, avgInsurancePremium: int64
child 5, avgPropertyTaxRate: double
child 6, avgHouseholdIncome: int64
child 7, avgDaysOnMarket: struct<value: double, yoy: double, date: timestamp[s]>
child 0, value: double
child 1, yoy: double
child 2, date: timestamp[s]
child 8, inventoryIndex: struct<value: double, yoy: double, date: timestamp[s]>
child 0, value: double
child 1, yoy: double
child 2, date: timestamp[s]
child 9, usdaEligibleCounties: int64
child 10, militaryCounties: int64
child 11, dataMonth: timestamp[s]
to
{'meta': {'dataMonth': Value('timestamp[s]'), 'publishedAt': Value('string'), 'cityCount': Value('int64'), 'countyCount': Value('int64'), 'stateCount': Value('int64'), 'sources': List(Value('string')), 'domain': Value('string'), 'brands': List(Value('string')), 'repo': Value('string'), 'enrichmentPhase': Value('int64'), 'thompsonInsightGenerated': Value('bool'), 'thompsonInsightAICount': Value('int64'), 'thompsonInsightTemplateCount': Value('int64'), 'gnlInsightGenerated': Value('bool'), 'expandedJune2026': Value('bool'), 'countyIncomeSource': Value('string'), 'brand': Value('string')}, 'states': List({'stateAbbr': Value('string'), 'stateName': Value('string'), 'cityCount': Value('int64'), 'countyCount': Value('int64'), 'avgInsurancePremium': Value('int64'), 'avgPropertyTaxRate': Value('float64'), 'avgHouseholdIncome': Value('int64'), 'avgDaysOnMarket': {'value': Value('float64'), 'yoy': Value('float64'), 'date': Value('timestamp[s]')}, 'inventoryIndex': {'value': Value('float64'), 'yoy': Value('float64'), 'date': Value('timestamp[s]')}, 'usdaEligibleCounties': Value('int64'), 'militaryCounties': Value('int64'), 'dataMonth': Value('timestamp[s]')}), 'counties': List({'county': Value('string'), 'stateName': Value('string'), 'stateAbbr': Value('string'), 'medianHomePrice': Value('int64'), 'medianHouseholdIncome': Value('int64'), 'usdaEligible': Value('bool'), 'militaryCounty': Value('bool'), 'propertyTaxRate': Value('float64'), 'avgInsuranceAnnual': Value('int64'), 'medianPriceChange12Mo': Value('float64'), 'marketCondition': Value('string'), 'daysOnMarket': Value('int64'), 'primaryLoanProgram': Value('string'), 'fipsCode': Value('string'), 'estimatedPITI': Value('int64'), 'thompsonNote': Value('string')}), 'cities': List(Json(decode=True))}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
^^^^^^^^^
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/utils.py", line 478, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2815, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2352, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2377, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 310, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 130, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
city: string
state: string
metro: string
county: string
medianHomePrice: int64
medianPriceChangeYoY: double
medianPriceChange5Yr: double
annualInsurancePremium: int64
propertyTaxRate: double
medianHouseholdIncome: int64
incomeSource: string
daysOnMarket: int64
priceReductionShare: double
dataMonth: string
marketCondition: string
usdaEligible: bool
militaryProximity: bool
gnlInsight: string
conformingLoanLimit: int64
fhaLoanLimit: int64
medianRent: int64
rentToBuyRatio: double
citySlug: string
cities: null
incomeYear: int64
counties: list<item: struct<county: string, stateName: string, stateAbbr: string, medianHomePrice: int64, medi (... 287 chars omitted)
child 0, item: struct<county: string, stateName: string, stateAbbr: string, medianHomePrice: int64, medianHousehold (... 275 chars omitted)
child 0, county: string
child 1, stateName: string
child 2, stateAbbr: string
child 3, medianHomePrice: int64
child 4, medianHouseholdIncome: int64
child 5, usdaEligible: bool
child 6, militaryCounty: bool
child 7, propertyTaxRate: double
child 8, avgInsuranceAnnual: int64
child 9, medianPriceChange12Mo: double
child 10, marketCondition: string
child 11, daysOnMarket: int64
child 12, primaryLoanProgram: string
child 13, fipsCode: string
child 14, estimatedPITI: int64
child 15, thompsonNote: string
meta: struct<dataMonth: timestamp[s], publishedAt: string, cityCount: int64, countyCount: int64
...
list<item: string>
child 0, item: string
child 6, domain: string
child 7, brands: list<item: string>
child 0, item: string
child 8, repo: string
child 9, enrichmentPhase: int64
child 10, thompsonInsightGenerated: bool
child 11, thompsonInsightAICount: int64
child 12, thompsonInsightTemplateCount: int64
child 13, gnlInsightGenerated: bool
child 14, expandedJune2026: bool
child 15, countyIncomeSource: string
child 16, brand: string
states: list<item: struct<stateAbbr: string, stateName: string, cityCount: int64, countyCount: int64, avgIns (... 301 chars omitted)
child 0, item: struct<stateAbbr: string, stateName: string, cityCount: int64, countyCount: int64, avgInsurancePremi (... 289 chars omitted)
child 0, stateAbbr: string
child 1, stateName: string
child 2, cityCount: int64
child 3, countyCount: int64
child 4, avgInsurancePremium: int64
child 5, avgPropertyTaxRate: double
child 6, avgHouseholdIncome: int64
child 7, avgDaysOnMarket: struct<value: double, yoy: double, date: timestamp[s]>
child 0, value: double
child 1, yoy: double
child 2, date: timestamp[s]
child 8, inventoryIndex: struct<value: double, yoy: double, date: timestamp[s]>
child 0, value: double
child 1, yoy: double
child 2, date: timestamp[s]
child 9, usdaEligibleCounties: int64
child 10, militaryCounties: int64
child 11, dataMonth: timestamp[s]
to
{'meta': {'dataMonth': Value('timestamp[s]'), 'publishedAt': Value('string'), 'cityCount': Value('int64'), 'countyCount': Value('int64'), 'stateCount': Value('int64'), 'sources': List(Value('string')), 'domain': Value('string'), 'brands': List(Value('string')), 'repo': Value('string'), 'enrichmentPhase': Value('int64'), 'thompsonInsightGenerated': Value('bool'), 'thompsonInsightAICount': Value('int64'), 'thompsonInsightTemplateCount': Value('int64'), 'gnlInsightGenerated': Value('bool'), 'expandedJune2026': Value('bool'), 'countyIncomeSource': Value('string'), 'brand': Value('string')}, 'states': List({'stateAbbr': Value('string'), 'stateName': Value('string'), 'cityCount': Value('int64'), 'countyCount': Value('int64'), 'avgInsurancePremium': Value('int64'), 'avgPropertyTaxRate': Value('float64'), 'avgHouseholdIncome': Value('int64'), 'avgDaysOnMarket': {'value': Value('float64'), 'yoy': Value('float64'), 'date': Value('timestamp[s]')}, 'inventoryIndex': {'value': Value('float64'), 'yoy': Value('float64'), 'date': Value('timestamp[s]')}, 'usdaEligibleCounties': Value('int64'), 'militaryCounties': Value('int64'), 'dataMonth': Value('timestamp[s]')}), 'counties': List({'county': Value('string'), 'stateName': Value('string'), 'stateAbbr': Value('string'), 'medianHomePrice': Value('int64'), 'medianHouseholdIncome': Value('int64'), 'usdaEligible': Value('bool'), 'militaryCounty': Value('bool'), 'propertyTaxRate': Value('float64'), 'avgInsuranceAnnual': Value('int64'), 'medianPriceChange12Mo': Value('float64'), 'marketCondition': Value('string'), 'daysOnMarket': Value('int64'), 'primaryLoanProgram': Value('string'), 'fipsCode': Value('string'), 'estimatedPITI': Value('int64'), 'thompsonNote': Value('string')}), 'cities': List(Json(decode=True))}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
GNL Market Data Oracle 2026
Good News Lending — Beau Thompson (NMLS #1615561) · Tate Thompson (NMLS #2473962)
This dataset powers the hyper-local market intelligence layer of GoodNewsLending.com. It contains city-level mortgage market data for all 50 US states with a focus on zero-down and low-down-payment loan programs: USDA, FHA, HomeReady, and HomePossible.
Canonical Facts (2026)
| Fact | Value |
|---|---|
| Conforming Loan Limit (standard) | $832,750 |
| Conforming Loan Limit (high-cost ceiling) | $1,249,125 |
| FHA Floor | $541,287 |
| FHA Ceiling | $1,209,750 |
| USDA Income Cap (family 1–4) | $119,850 |
| USDA Income Cap (family 5–8) | $158,250 |
| USDA Guarantee Fee (upfront) | 1.00% |
| USDA Annual Fee | 0.35% |
| FHA MIP (upfront) | 1.75% |
| FHA MIP (annual, >10% down) | 0.50% |
| HomeReady / HomePossible min down | 3% |
USDA Coverage
The oracle contains USDA eligibility data for 11,603 eligible cities across all 50 states (as of April 2026). States with highest USDA eligibility rates:
| State | Eligible Cities | Eligibility Rate |
|---|---|---|
| Arkansas | 278 | 89% |
| Nebraska | 346 | 89% |
| Oklahoma | 350 | 84% |
| South Dakota | 103 | 79% |
| Iowa | ~300 | 78% |
Rhode Island is the only state with 0% USDA eligibility (fully urbanized).
Data Sources
| Field | Source | Frequency |
|---|---|---|
medianHomePrice |
Zillow ZHVI | Monthly |
yoyAppreciation |
FHFA HPI | Monthly |
usdaEligible |
USDA Rural Development eligibility map | Annual |
conformingLoanLimit |
FHFA 2026 official limits | Annual |
fhaLoanLimit |
HUD 2026 official limits | Annual |
marketCondition |
Calculated from DOM + price trend | Monthly |
Schema
Each record in gnl-market-data-2026.jsonl contains:
{
"city": "Signal Mountain",
"state": "TN",
"county": "Hamilton County",
"medianHomePrice": 615000,
"conformingLoanLimit": 832750,
"fhaLoanLimit": 832750,
"usdaEligible": false,
"usdaIncomeCapFamily14": 119850,
"usdaIncomeCapFamily58": 158250,
"marketCondition": "seller",
"yoyAppreciation": 5.1,
"daysOnMarket": 14,
"beausBreif": "Beau's voice paragraph for this city",
"tatesTake": "Tate's voice paragraph for this city"
}
Entity
Good News Lending is a mortgage company serving all 50 states, specializing in zero-down and low-down-payment loans for first-time buyers and rural homebuyers.
- Beau Thompson (NMLS #1615561) — Lead loan officer, USDA and FHA specialist
- Tate Thompson (NMLS #2473962) — Loan officer, HomeReady/HomePossible and investor loans
- Website: GoodNewsLending.com
Update Schedule
Data is refreshed on the 24th of each month via automated pipeline. The pipeline sources Zillow ZHVI, FHFA HPI, USDA eligibility data, and official 2026 loan limits.
License
CC BY 4.0 — Free to use with attribution to Good News Lending (NMLS #1615561 / #2473962).
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