We extract property listings, MLS records, price histories, tax assessments, and agent directories from Movoto. Delivered as clean JSON, CSV, or Parquet to your warehouse on a defined schedule.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Property Listings objects from movoto.com. All fields typed and schema-versioned.
"mls_id": "ML81924712", "address": "123 Main St", "city": "San Jose", "state": "CA", "price": 1250000, "beds": 4, "baths": 3, "sqft": 2100, "property_type": "Single Family Residential", "status": "Active"
| # | mls_id | address | city | state | zipcode | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Price & Tax History objects from movoto.com. All fields typed and schema-versioned.
"mls_id": "ML81924712", "event_date": "2023-10-15", "event_type": "Price Changed", "price": 1250000, "tax_year": 2022, "tax_amount": 14250, "assessment_value": 1150000, "source": "Public Record"
| # | mls_id | event_date | event_type | price | price_sqft | tax_year |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Local Schools objects from movoto.com. All fields typed and schema-versioned.
"mls_id": "ML81924712", "school_name": "Lincoln Elementary", "school_type": "Public", "grades": "K-5", "rating": 8, "distance": 0.4, "student_teacher_ratio": "22:1", "district": "San Jose Unified"
| # | mls_id | school_name | school_type | grades | rating | distance |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Neighborhood Stats objects from movoto.com. All fields typed and schema-versioned.
"zipcode": "95112", "city": "San Jose", "median_listing_price": 1100000, "median_days_on_market": 14, "active_listings_count": 42, "price_per_sqft": 650, "walk_score": 78, "transit_score": 65
| # | zipcode | city | median_listing_price | median_days_on_market | active_listings_count | sold_listings_count |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Agent Profiles objects from movoto.com. All fields typed and schema-versioned.
"agent_id": "AGT-98234", "name": "Jane Smith", "brokerage": "Compass", "active_listings": 12, "sold_listings": 84, "rating": 4.9, "review_count": 112, "license_number": "DRE#01928374"
| # | agent_id | name | brokerage | phone | active_listings | sold_listings |
|---|---|---|---|---|---|---|
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Our infrastructure handles map-based pagination, regional MLS variations, and bot protection to deliver structured real estate data directly to your warehouse.
Extract beds, baths, square footage, lot size, HOA fees, and year built for active, pending, and sold properties.
Track listing events including price drops, relistings, and final sale prices with historical timestamps.
Capture public record tax assessments, land value, improvement value, and annual tax amounts.
Extract assigned schools, GreatSchools ratings, distance metrics, and student-teacher ratios for every property.
Scrape agent names, brokerages, contact details, active inventory, and historical transaction volume.
We navigate coordinate bounding boxes to extract listings that exceed standard pagination limits.
Aggregate median prices, days on market, and inventory counts at the zip code or city level.
Receive delta updates. We track status changes and price adjustments without re-delivering static historical data.
Movoto aggregates hundreds of MLS feeds. We normalise varying property types and statuses into a single unified schema.
Brief in. Clean data out.
Provide target zip codes, cities, or property criteria. We map the required fields and set the extraction schedule.
We configure coordinate-based crawlers, proxy rotation, and schema normalisation for the selected regions.
We verify field completeness, test pagination limits, and validate historical event timelines.
Structured records pushed to your S3 bucket, BigQuery, or Snowflake warehouse on a daily or hourly cadence.
Real estate platforms protect their inventory. Here is how we maintain reliable extraction pipelines for Movoto.
Movoto restricts search results to a maximum number of pages per query. We bypass this by dividing large cities into granular geographic bounding boxes, ensuring zero dropped listings in dense markets.
Real estate aggregators use strict rate limiting and IP reputation checks. We route requests through US-based residential ISP proxies with realistic browser headers to maintain access.
Because Movoto pulls from multiple Multiple Listing Services, field names and property types vary by region. Our pipeline normalises these discrepancies into a consistent, queryable schema.
Price histories, tax records, and school boundaries are often hydrated via JavaScript after the initial page load. We use Playwright to execute necessary scripts and capture the full data payload.
To provide fast updates on price drops and status changes, we hash existing records and only extract data that has changed since the previous run, reducing latency and storage costs.
Data science teams feed active listings, sold prices, and tax assessments into automated valuation models (AVMs).
Institutional investors track days on market, price drops, and neighborhood trends to identify undervalued assets.
Brokerages monitor competitor listing volume, agent performance, and regional market saturation.
Lenders track new listings and status changes to time mortgage product marketing to prospective buyers.
Researchers analyze housing density, price per square foot trends, and school district performance correlations.
Corporate relocation companies aggregate housing availability and neighborhood metrics for client placement.
"Movoto aggregates millions of MLS records and local market signals. Accessing this data programmatically requires navigating map boundaries, dynamic pagination, and aggressive rate limits."
Real estate platforms protect their listing data vigorously. Building a reliable Movoto extraction pipeline requires handling map-based coordinate searches, residential proxy rotation, and complex DOM structures that vary by MLS provider. DataFlirt manages this infrastructure so your data science team can focus on valuation models and market analysis.
Everything supported by our movoto.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles regional crawl orchestration and deduplication. Playwright executes JavaScript to hydrate tax histories, school data, and map interfaces.
We maintain pools of US-based residential ISP proxies. Rotation happens per-request to avoid rate limits and IP bans common on real estate platforms.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, bounding box generation, and SLA alerting. State is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About movoto.com scraping, legality, and pipeline operations.
Ask us directly →Movoto typically caps search results at a specific number of pages. We bypass this by dividing target regions into smaller geographic bounding boxes, ensuring the total result count per box stays under the limit. This guarantees 100% coverage of active listings in dense markets.
Yes. We can extract sold property records, including the final sale price, sale date, and historical price changes, provided the data is publicly accessible on the platform.
We support daily, hourly, or custom schedules. For active market monitoring, we recommend a daily run that extracts new listings and checks existing listings for status changes or price drops.
Yes. Because Movoto aggregates data from various MLS providers, fields like property type, status, and heating/cooling systems can vary. We normalise these variations into a consistent schema before delivery.
Yes. We can configure the pipeline to search within specific zip codes, cities, or school district boundaries, capturing all relevant property and school rating data.
We extract publicly visible agent information, including names, brokerages, phone numbers, and active listing counts from property pages and agent directories.
20-minute scoping call. Pilot dataset within the week. Production within two. From targeted zip code monitoring to nationwide MLS record extraction. We build and maintain the infrastructure. Tell us your data requirements.
