Credit models in real estate lending depend on the quality of the data feeding them. Whether you are underwriting a mortgage, pricing a commercial loan, or building an automated risk engine, the accuracy of your property valuations, rental income assumptions, borrower signals, and market indicators shapes every downstream decision. For lenders, proptech founders, and data teams, choosing the right data provider is a foundational step.
This list covers nine data providers that are commonly used to support credit models for real estate, from rental and property-level records to loan performance and macro market indicators. We focus on what each provider offers, why it matters for credit modeling, and how teams typically put the data to work. The goal is to help you match a data source to the specific signals your model needs.
Quick Overview List
- Dwellsy IQ: A first-party rental data provider sourcing unit-level rents directly from property management systems, used for rental income and rent trend inputs in SFR and multifamily credit models.
- Cotality (formerly CoreLogic): A broad property data provider offering valuations, property records, and risk analytics widely used in mortgage underwriting.
- ATTOM Data: A nationwide property database combining tax, deed, mortgage, and foreclosure data for risk and valuation signals.
- ICE (formerly Black Knight): A mortgage data and analytics provider covering loan-level performance, home prices, and servicing data.
- CoStar: A commercial real estate data provider with detailed property, lease, and market fundamentals for CRE credit models.
- Moody’s Analytics: A provider of economic forecasts and CRE risk analytics that help stress-test credit exposures.
- HouseCanary: An analytics firm offering automated valuations and forecasts aimed at residential lending and investment.
- First American: A title and property data provider with ownership, lien, and transaction records relevant to credit risk.
- Trepp: A provider of CMBS, commercial mortgage, and bank loan data used for commercial credit and default analysis.
1. Dwellsy IQ
What it is
Dwellsy IQ is a rental data infrastructure provider built on first-party data sourced directly from property management systems. Its foundation is the Dwellsy marketplace, the largest rental listing platform in the U.S. Because property managers list through Dwellsy directly, rents are captured where they are set and updated, not scraped, surveyed, or estimated. The dataset includes 17M+ listings from 25,000+ property managers across 30+ PMS integrations. It covers about 70% of professionally managed U.S. rental housing, across both SFR and multifamily.
Why it matters
- Provides unit-level asking rents that support rental income assumptions, DSCR calculations, and NOI projections for investor and multifamily loans.
- Offers rent indices across 800+ MSAs and 16,000+ ZIP codes, with history back to January 2020, for stress-testing rental income across market cycles.
- Assigns every unit a permanent identifier (URU), so teams can track rents for the same unit over time without reconciliation work. This helps with backtesting and longitudinal risk models.
- Uses direct sourcing from property management systems, which avoids the legal exposure and duplication that come with scraped listing data. That matters for institutional lenders and regulated model environments.
- Includes 250+ standardized attributes per listing, which can be used as model features beyond rent alone.
How to use it / Key details
- Use Trends IQ rent indices as a defensible benchmark for underwritten rental income and forward rent assumptions.
- Pull unit-level comps to validate borrower-reported or appraiser-provided rents on SFR, BTR, and multifamily collateral.
- Feed the full dataset (Total IQ) or API IQ into internal models to build rent-based risk features at ZIP, MSA, or portfolio level.
- Connect AI underwriting agents to live rental data through the Dwellsy MCP Server.
Data is delivered via API, AWS S3, spreadsheets, or MCP. Enterprise products are licensed directly. Comp IQ, the self-service comp tool for smaller owners and operators, is priced per lookup.
2. Cotality (formerly CoreLogic)
What it is
Cotality, the company previously known as CoreLogic, is a property data and analytics provider with extensive coverage of residential and commercial records across the United States. It combines public records, valuation models, and risk datasets into products that lenders use throughout the loan lifecycle. Many mortgage workflows already touch Cotality data in some form.
Why it matters
- Offers automated valuation models (AVMs) that can feed collateral risk assessments.
- Provides property characteristics and tax records that support loan-to-value calculations.
- Includes natural hazard and climate risk data relevant to collateral exposure.
- Covers a large share of U.S. properties, which helps reduce data gaps in national models.
How to use it / Key details
- Pull AVM outputs as a collateral value input alongside appraisal data.
- Use property and transaction history to validate borrower-reported details.
- Layer hazard risk fields into models for geographically concentrated portfolios.
Pricing appears to be custom and quote-based, so exact tiers are not publicly displayed. Access is typically arranged through enterprise agreements or data licensing.
3. ATTOM Data
What it is
ATTOM Data maintains a nationwide property database that blends tax, deed, mortgage, and foreclosure records. It positions itself as a single source for multi-layered property data, which teams can pull via bulk files or APIs. The breadth of record types makes it a flexible input for credit work.
Why it matters
- Combines ownership, lien, and sales data that inform default and recovery assumptions.
- Includes foreclosure and distressed property signals useful for risk flags.
- Provides standardized fields across counties, easing national model builds.
- Supports both property-level and aggregated market views.
How to use it / Key details
- Use foreclosure history as a feature in default-probability models.
- Pull deed and mortgage records to track lien position and encumbrances.
- Aggregate sales data to build local price trend indicators.
Public pricing isn’t listed on their site, so exact tiers aren’t available. Data is generally offered through API access or bulk licensing.
4. ICE (formerly Black Knight)
What it is
ICE (Intercontinental Exchange) acquired Black Knight and now offers its mortgage data and analytics through ICE Mortgage Technology and ICE’s mortgage data solutions. The portfolio includes McDash, a loan-level mortgage performance dataset, along with home price indices and servicing data that are directly relevant to credit risk. Many teams in residential mortgage still know these datasets by the Black Knight name.
Why it matters
- Provides loan-level performance data (McDash) that helps calibrate default and prepayment models.
- Offers home price indices for tracking collateral value trends over time.
- Covers servicing data that reflects delinquency and loss mitigation behavior.
- Supports both portfolio monitoring and new-loan underwriting.
How to use it / Key details
- Use loan-level performance history to train delinquency and default classifiers.
- Integrate home price index data into mark-to-market LTV calculations.
- Monitor servicing signals to update risk scores on existing loans.
We couldn’t find standard public pricing. Access is typically arranged through enterprise data and analytics agreements.
5. CoStar
What it is
CoStar is a commercial real estate data provider offering detailed information on properties, leases, sales, and market fundamentals. It is widely referenced in commercial underwriting and investment analysis. For CRE credit models, it supplies the property and market context that residential sources often lack.
Why it matters
- Provides lease and occupancy data that inform net operating income assumptions.
- Offers market-level vacancy and rent trends relevant to debt service coverage.
- Includes sales comparables for commercial collateral valuation.
- Covers multiple property types, from office to multifamily and industrial.
How to use it / Key details
- Use lease rollover data to stress-test cash flow in CRE credit models.
- Apply market vacancy trends to adjust forward-looking income projections.
- Pull sales comps to support collateral value and LTV estimates.
The pricing details are not available publicly, as access is typically subscription-based and tailored to the client.
6. Moody’s Analytics
What it is
Moody’s Analytics provides economic data, forecasts, and commercial real estate risk analytics. Its tools are often used to stress-test portfolios against macroeconomic scenarios. For credit teams, it bridges property-level data with broader economic conditions.
Why it matters
- Supplies economic forecasts that help model scenario-based credit losses.
- Offers CRE-specific risk analytics for commercial loan portfolios.
- Supports regulatory stress-testing and capital planning workflows.
- Connects local market data with national and regional economic drivers.
How to use it / Key details
- Feed macroeconomic scenarios into expected credit loss models.
- Use CRE analytics to benchmark property risk against market conditions.
- Align stress-testing inputs with recognized economic forecast frameworks.
When researching this for the article, we couldn’t find publicly displayed pricing. Offerings are generally delivered through subscriptions and enterprise licenses.
7. HouseCanary
What it is
HouseCanary is an analytics firm focused on residential valuations and forecasts. It offers automated valuation models and forward-looking price projections aimed at lenders and investors. The provider positions its outputs for use in underwriting and portfolio analysis.
Why it matters
- Delivers AVMs that support collateral value estimates for residential loans.
- Provides forecasts that help model future collateral value under different conditions.
- Offers confidence scores that help teams weigh valuation reliability.
- Supports both single-property and portfolio-level analysis.
How to use it / Key details
- Use AVM outputs with confidence scores to flag uncertain valuations.
- Apply price forecasts to model forward LTV scenarios.
- Integrate portfolio analytics to monitor concentration and collateral risk.
Public pricing isn’t listed on their site, so exact tiers aren’t available. Access is typically arranged through API or enterprise agreements.
8. First American
What it is
First American is a title and property data provider with deep coverage of ownership, lien, and transaction records. Its datasets draw on title and public records that are central to understanding collateral and encumbrances. For credit teams, this provides a view into legal and ownership risk.
Why it matters
- Supplies ownership and title data that clarify lien position and claims.
- Provides transaction history useful for valuation and fraud checks.
- Covers property records that help verify borrower-reported details.
- Supports risk assessment tied to clear title and collateral integrity.
How to use it / Key details
- Use lien and encumbrance data to assess recovery assumptions.
- Cross-check ownership records during underwriting to reduce fraud risk.
- Pull transaction history to validate property value inputs.
The pricing details are not available publicly, as data access is generally handled through licensing arrangements.
9. Trepp
What it is
Trepp is a provider of data and analytics focused on commercial mortgages, CMBS, and bank loans. Its datasets cover loan performance, delinquencies, and structured finance details relevant to commercial credit. For CRE lenders and investors, it offers specialized risk signals.
Why it matters
- Provides CMBS and commercial loan performance data for default modeling.
- Offers delinquency and loss data useful for calibrating credit risk.
- Covers bank loan data that supports commercial portfolio analysis.
- Supplies structured finance detail that informs recovery assumptions.
How to use it / Key details
- Use historical loan performance to train commercial default models.
- Track delinquency trends to update portfolio risk scores.
- Apply loss severity data to refine recovery and LGD estimates.
When researching this for the article, we couldn’t find publicly displayed pricing. Data is generally delivered through subscription and enterprise access.
FAQ
What makes a data provider suitable for real estate credit models?
A provider is suitable when its data is accurate, well-covered, and consistent enough to feed model inputs like collateral value, rental income, borrower signals, and market risk. Coverage breadth and data freshness matter, because gaps and stale records weaken model reliability. How the data is sourced also matters: first-party data carries less legal and accuracy risk than scraped or survey-based inputs. The right fit also depends on whether you are modeling residential or commercial credit.
Can I use more than one provider in a single credit model?
Yes, many teams combine multiple providers to fill gaps and cross-validate signals. For example, you might pair a valuation source with an ownership and lien dataset, add rental income data for investor loans, then blend in loan performance and market indicators. Shared identifiers and a consistent data pipeline make it easier to join these feeds into one model.
Do these providers cover both residential and commercial real estate?
Coverage varies by provider. Some focus on residential, others on commercial, and a few span both. Residential-oriented sources tend to emphasize home values, rents, and loan performance, while commercial sources focus on leases, market fundamentals, and structured finance. Matching the provider to your asset class is an important step.
How often should credit model data be refreshed?
Refresh frequency depends on the use case, but collateral values and market indicators are often updated monthly or quarterly. Rental data sourced directly from property management systems can be updated continuously, which helps when rent assumptions drive debt service coverage. Loan performance and delinquency data may be monitored more frequently for active portfolios. The key is aligning refresh cadence with how quickly your risk signals change.
Is public pricing available for these providers?
Most of these providers do not publish standard pricing, since access is typically custom or subscription-based. Costs generally depend on data volume, coverage, and the specific products you license. Some providers also offer self-service, per-lookup options for smaller users. Reaching out directly is usually the way to get accurate figures for your needs.
