Dwellsy TotalIQ

Overview

Dwellsy TotalIQ is a rental market dataset built from operational systems of record where rents are set, updated, and finalized. The dataset originates from direct integrations with property management systems and first-party feeds, providing rental listings data that reflects how the rental market actually operates. The data is sourced from over 25,000 property managers through 30+ direct property management system integrations, covering both single-family rentals and multifamily properties. Built on more than 17 million real rental listings, the dataset represents approximately 70% of professionally managed U.S. rental housing across 16,000+ ZIP codes and 800+ MSAs. All data is public, owner-disclosed, and designed to minimize legal and regulatory risk, with no scraped content, survey data, or personally identifiable information (PII).

The dataset provides real-time asking rents, rent changes, and lease execution signals through continuous updates throughout the day. Historical data coverage extends from 2019 to the present, with multi-year historical depth. Dwellsy TotalIQ applies a structured data preparation and quality pipeline to ensure accuracy and consistency across markets, including de-duplication, validation, normalization, and fraud prevention safeguards.

Data Description

The dataset contains unit-level rental listing information with standardized attributes across single-family rental (SFR) and multifamily properties. Key data elements include:

Rent values (asking rents, rent changes, monthly effective values)
Address and geographic information (address, city, state, ZIP code, geolocation coordinates, MSA)
Unit characteristics (bedrooms, bathrooms, square footage, year built)
Property and community identifiers
Listing temporal information (list dates, end dates, time-on-market)
Property management company information
Listing descriptions and visual images

Geographic coverage includes national, regional, and local views with consistent definitions across ZIPs, MSAs, states, and national aggregations. The dataset provides both unit-level precision and aggregated market-level statistics, including median rent by asset type and bedroom count for specified time periods and geographies, as well as year-over-year rent change calculations.

Coverage

Geographic Coverage:

Coverage across 16,000+ ZIP codes
800+ Metropolitan Statistical Areas (MSAs)
National, regional, state, city, and local market views

Universe of Entities:

Built on more than 17 million real rental listings
Approximately 70% of professionally managed U.S. rental housing
Data sourced from over 25,000 property managers
Includes single-family rentals, small multifamily, and institutional portfolios

Temporal Coverage:

Historical data from 2019 to present
Multi-year historical depth
Continuous real-time updates throughout the day

Methodology

Dwellsy TotalIQ applies a rigorous, end-to-end methodology that transforms raw rental data into reliable analytical outputs. The data processing pipeline consists of twelve sequential stages:

1. Raw Data Ingestion Rental listings are ingested directly from property management systems and first-party feeds representing operational systems of record where rents are created, updated, finalized, and published. Source systems are systems of record, not aggregators. Data reflects active pricing and availability states with continuous updates as listings change.

2. Schema Validation Incoming records are validated against a strict schema to ensure structural integrity before processing. Validation includes required field presence (rent, unit type, location), data type enforcement (numeric, categorical, temporal), and referential consistency across unit and property identifiers. Records failing validation are excluded rather than repaired.

3. Data Normalization Listings from different systems are standardized into consistent analytical representations through unit type harmonization across SFR and multifamily, bedroom and bathroom standardization, geographic normalization to ZIP, city, MSA, and state hierarchies, reconciliation to actual U.S. Postal Service addresses, and rent normalization to monthly effective values.

4. Duplicate Resolution Duplicate listings arising when the same unit appears multiple times across feeds, refresh cycles, or syndication paths are identified using property identifiers, geospatial proximity, and unit attributes. Duplicates are collapsed into a single unit representation, preserving the most reliable observation for pricing and availability.

5. Listing Reconciliation When multiple observations exist for the same unit with minor differences (small rent changes within short time windows, slight timing offsets between system updates, overlapping listing states), listings are reconciled rather than removed.

Data Quality Controls:

De-duplication of listings and leasing office addresses
Validation of unit-level attributes
Removal of per-bed and non-standard listings
Address verification and geolocation checks
Fraud prevention safeguards
Standardized rent, unit, and property attributes
Consistent definitions across SFR and multifamily

Additional Notes

Data Source Characteristics: All Dwellsy TotalIQ data is public, owner-disclosed, and sourced from operational systems of record. The dataset contains no scraped content, no terms of service risk, no survey responses, and no private or regulated data. The data is first-party, owner-disclosed listings with no PII, designed to minimize legal and regulatory risk.

Integration Partners: Data integrations include 30+ property management systems and registered API interface partnerships for marketing Internet Listing Services (ILS). Once enabled, feeds are continuous with a "set it and forget it" implementation approach.