Pricing

from $8.00 / 1,000 map layer queries

Realtor.com Market Data Scraper — Hotness, Forecast, Schools

Extract US housing market data from Realtor.com map layers: ZIP market hotness scores, 12-month price forecasts, days-on-market and home-value heatmaps, parcel-level estimates, flood and wildfire climate risk, school ratings. One query per metro returns hundreds of analyst-ready rows.

Pricing

from $8.00 / 1,000 map layer queries

Rating

0.0

(0)

Developer

SIÁN OÜ

Actor stats

Bookmarked

Total users

Monthly active users

7 days ago

Last modified

Changelog

All notable changes to Realtor.com Market Data Scraper will be documented in this file.

[2026-07-02]

🎉 Realtor.com Market Data Scraper - Launch!

ZIP-Level Market Hotness - Score and label every ZIP in a metro (345 hotness-scored ZIPs per Austin query) for instant submarket screening
12-Month Housing Forecast - Forecast home value in dollars and percent change per ZIP, ready for buy-box models and market-timing dashboards
Home-Value & Days-on-Market Heatmaps - ~4,500 heatmap cells per ZIP query across 10 layers: estimates, $/sqft, sqft, year built, lot acres, lot slope, noise, DOM, sold-vs-list, sold $/sqft
Parcel-Resolution Mode - Zoom 15–16 returns per-parcel value estimates with real property IDs for lead sourcing and detail enrichment
Climate-Risk Overlays - Flood, wildfire, heat, wind, and air-quality raster tiles with 0/15/30-year projections, stored as ready-to-use PNGs
Schools & Neighborhoods Layers - School ratings, parent ratings, student-teacher ratios and contacts, plus neighborhood names with stable join IDs

💎 User Benefits

One query returns a whole metro's market data — hundreds to thousands of analyst-ready rows per charge
Priced per layer query, not per row: metro-wide housing market data for about a cent
Flat, typed dataset fields drop straight into BigQuery, Snowflake, pandas, or Sheets
Free-form locations (ZIP, city, neighborhood, county, address) resolved automatically

🎯 Use Cases

Real-estate investors screening ZIPs by market hotness before running comps
Acquisition analysts feeding 12-month price forecasts into buy-box models
PropTech teams rendering home-value heatmaps without building a data pipeline
Underwriters overlaying 30-year flood and wildfire projections for portfolio risk
Relocation platforms attaching school ratings and contacts to every listing

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