Parcel Enrichment API v0 · Sonoma live · 189,239 parcels

Source-cited parcel intelligence
for real estate AI.

Lotmark turns parcel, hazard, terrain, building, and environmental data into structured, source-cited features — built for the AI products being built in proptech, insurance, and mortgage today.

PARCEL · APN 079-020-011
10275 Loch Haven Dr, Santa Rosa, CA
FHSZ High
Burned 2019
640 ft 620 600 580 560 LOCH HAVEN DR N 40°24'E · 210.0' S 78°59'W · 232.5' 100' Zone 2 30' Z-1 X Y Z building.primary 1979 · 2,839 sqft terrain.elevation 584 ft · USGS 3DEP relief 154 ft · slope 6.7° aspect 158° SSE risk.fire.burn inside perimeter · 0 m C. HANLY 2019 · 7 yrs since risk.fire.fhsz 0 50 100 High · CAL FIRE SRA 38.5606° N · 122.7111° W · NAD83(2011) EPSG:2226 · CA-II · ortho 2025-Q3 · sheet 1 of 1 N 0 50 100 ft
elevation surface · 600 m window · USGS 3DEP parcel 584 ft
3D elevation surface around 10275 Loch Haven Dr from USGS 3DEP
source layers on this parcel 9 agencies · 47 cited fields
Sonoma Countyparcel1.02 ac
Sonoma Countybuilding1979 · 2,839 sf
USGS 3DEPterrain584 ft · 6.7°
CAL FIREfhszHigh · SRA
CAL FIREburn2019 · 0 m
USFSwhphigh · 4/5
FEMA NFHLfloodZone X
USDA NRCSsoilLaniger · grp C
USFS NLCDcanopy29% · 2023
accessed 2026-07-24 · freshness 2 days min confidence 0.85
GET /v1/parcels/lm_ca_079-020-011
200 OK
source citation
every field ships with the agency, product, and as-of date that produced it
{
  "parcel_id": "lm_ca_079-020-011",
  "freshness_days": 2,

  "parcel": {
    "apn": "079-020-011",
    "address": "10275 Loch Haven Dr, Santa Rosa, CA",
    "land_acres": 1.02,
    "source": {
      "agency": "County of Sonoma",
      "product": "Parcels Public",
      "accessed": "2026-07-24",
      "record_count": 189239
    }
  },

  "risk_features": {
    "fire": {
      "fhsz_zone": "High",
      "responsibility_area": "SRA",
      "wildfire_hazard_potential_class": "high",
      "wildfire_hazard_potential_value": 4,
      "distance_to_burn_perimeter_m": 0,
      "nearest_burn_name": "C. HANLY",
      "last_burn_year_within_5km": 2019,
      "years_since_last_fire": 7,
      "source": {
        "agency": "CAL FIRE",
        "product": "FHSZ SRA 2024 point intersect",
        "accessed": "2026-07-24"
      },
      "confidence": 0.9
    },
    "flood": {
      "fema_zone": "X",
      "in_sfha": false,
      "source": {
        "agency": "FEMA NFHL",
        "panel_id": "06097C0735F"
      }
    }
  },

  "building": {
    "primary_year_built": 1979,
    "primary_size_sqft": 2839,
    "source": {
      "agency": "County of Sonoma",
      "product": "Parcels Public",
      "as_of": "2026-05-02"
    }
  },

  "terrain": {
    "elevation_ft": 583.9,
    "local_relief_150m_ft": 153.66,
    "slope_degrees": 6.7,
    "aspect_degrees": 157.7,
    "source": {
      "agency": "USGS",
      "product": "3DEP 30m raster sample"
    }
  },

  "soil": {
    "mukey": "459807",
    "mapunit_name": "Laniger loam, 15 to 30 percent slopes",
    "hydrologic_group": "C",
    "drainage_class": "Well drained",
    "source": {
      "agency": "USDA NRCS",
      "product": "SSURGO/SDA"
    }
  },

  "vegetation": {
    "canopy": {
      "canopy_percent": 29,
      "year": 2023,
      "source": {
        "agency": "USDA Forest Service",
        "product": "NLCD Tree Canopy Cover 2023"
      }
    }
  },

  "narrative": {
    "text": "1.02 acre parcel, in High fire hazard zone, with a 1979 primary structure, at 583.9 ft elevation.",
    "grounded_in": ["parcel", "building", "terrain", "risk.fire", "risk.flood"]
  },

  "embedding": {
    "dim": 1536,
    "model": "text-embedding-3-small",
    "status": "metadata_only"
  }
}

Every field carries a source object with agency, product, and date. Narratives ship with a grounded_in array so your RAG pipeline can verify them. Embeddings included.

01 · How it works

Public data, rigorously turned into AI-ready features.

County parcel, hazard, terrain, soil, canopy, and building sources normalized to a single parcel schema, enriched with LLM narratives and embeddings, and exposed through one API. Each step is auditable; each output is cited.

STEP_1

Ingest

County assessor parcels and building attributes, USGS 3DEP terrain, FEMA NFHL, USFS WHP, CAL FIRE FHSZ and burn perimeters, USDA SSURGO, and NLCD tree canopy.

STEP_2

Enrich

Geospatial joins, zonal stats, distance features, slope/elevation derivation, LLM-generated narratives, 1536-dim embeddings, quality gates.

STEP_3

Serve

REST endpoints. Tool-call-shaped JSON. Bulk download for Growth tier. Webhook refresh signals. Source provenance on every field.

02 · Why Lotmark

Built for the products being built, not the dashboards we've already seen.

Source-cited, every field.

Every datapoint ships with the agency, product version, and as-of date that produced it. Your RAG pipeline can verify claims. Your underwriters can defend them.

Features, not opaque scores.

Distance to burn perimeter, WHP class, FHSZ zone, slope, canopy, soil — composable inputs your model can reason over. No black-box 1-10 number you can't audit.

Shaped for AI tool calls.

Structured JSON, pre-generated narratives, included embeddings. Drop into your agent's tool definitions in minutes — not weeks of feature engineering.

03 · Use cases

For the teams shipping AI products in climate-exposed markets.

Insurance

Pre-quote risk enrichment

Surface fire, flood, and roof signals at quote-time. Catch hazard mismatches before the binder issues.

Mortgage

Pre-underwriting diligence

Climate-exposed properties surface their risks at application, not at appraisal. Fewer surprises late in close.

Proptech AI

Real estate agents & copilots

Give your agent factual property data with citations instead of hallucinations from training data.

Diligence

Site feasibility & investing

Slope, soil, flood, hazard, and distance features for development, solar siting, and portfolio risk.

04 · Coverage

Starting where climate exposure is highest.

Lotmark launches with four states where wildfire, flood, and wind exposure are reshaping property economics — and where the data is publicly available and rigorously source-able.

CA
California
LIVE · SONOMA
FL
Florida
Q3 2026
TX
Texas
Q3 2026
CO
Colorado
Q4 2026
CA CO TX FL Live in production Coming Q3/Q4 2026
05 · Pricing

Start free. Scale by successful parcel enrichment.

Priced by successful parcel enrichment, not generic API calls. Every included unit returns structured features, citations, narratives, and embeddings.

Developer
$0 /mo

For testing the API shape before production.

Request access
  • 100 enrichments / mo
  • Testing access only
  • Source citations included
  • No bulk export
  • No production SLA
Builder
$99 /mo

For solo founders and AI prototypes.

Start Builder
  • 5,000 enrichments / mo
  • Full enrichment surface
  • LLM narratives included
  • Email support
  • Fixed tier; custom overages on request
Most popular
Startup
$399 /mo

For production apps and small teams.

Start Startup
  • 25,000 enrichments / mo
  • LLM narratives included
  • Embeddings (1536-dim)
  • Semantic search endpoint
  • Webhooks
  • Fixed tier; custom overages on request
Growth
$999 /mo

For scaling teams in proptech, insurance, mortgage.

Start Growth
  • 100,000 enrichments / mo
  • Bulk parquet download
  • Refresh webhooks
  • Priority refresh
  • Slack support
  • Custom overage terms on request

Enterprise starts at $3k-$10k+/mo for annual contracts, redistribution rights, SLAs, and custom coverage. Talk to us about Enterprise.

06 · Built by

Built by Marfinetz Labs.

Public methodology, open dataset cards, no resold feeds.

Talk to us

Enterprise, custom coverage, or a walkthrough?

For instant API access, grab a key from pricing above. Use this form for Enterprise, developer access, demo bookings, or anything else.

Replies from a human, usually same day
No resold lead forms or third-party CRM
Email instead