Investment decisions require more than a list price. You need price history, tax assessment trends, three independent AVM estimates, days-on-market behavior, listing and delisting patterns, and risk indicators. Most data providers give you one or two of these. Specrom gives you all of them in a single API response, without enterprise contracts.
// Investment-focused property record { "list_price": 3650000, "days_on_market": 47, "last_price_change_amount": -150000, "estimates": { "current_values": [ { "source": "quantarium", "estimate": 3480000 }, { "source": "cotality", "estimate": 3510000 }, { "source": "collateral_analytics", "estimate": 3440000 } ] }, "local": { "flood": { "flood_factor_score": 1, "fema_zone": ["X (unshaded)"] } }, "flags": { "is_foreclosure": false, "is_price_reduced": true } }
The gap between what a property is listed for and what it's actually worth is where investment opportunities live. Finding that gap at scale requires data you can query, model, and update continuously. Specrom's feed includes every field that matters for that workflow.
// AVM estimates — three providers per property "estimates": { "current_values": [ { "source": "quantarium", "estimate": 3480000 }, { "source": "cotality", "estimate": 3510000 }, { "source": "collateral_analytics", "estimate": 3440000 } ], "historical_values": [...], "forecast_values": [...] }, // Tax history — multi-year series "tax_history": [ { "year": 2025, "amount": 7381 }, { "year": 2024, "amount": 7102 }, { "year": 2023, "amount": 6890 } ], // Full listing event history "property_history": [...]
The same feed supports very different investment workflows depending on how you query it, what fields you prioritize, and how you model the output.
Pull bulk active listings filtered by geography, property type, and price band. Score each property using the AVM estimates and price history. Automate initial offer pricing logic. Update models daily as new listings come in and existing ones change status.
Use the FIPS code and coordinate data to join the listings feed to your internal datasets — census data, employment data, permit data. Build ZIP-code-level market indicators using aggregated days-on-market, price reduction rates, and list-to-AVM gaps.
Filter for properties with high days on market, recent price reductions, and a meaningful gap between list price and AVM estimates. Use the property history to see how many times a property has been listed and relisted. Tax history gives you carrying cost context.
Embed the data feed into your platform's analysis tools. Give your users access to tax history, three AVM estimates, and flood risk data alongside the standard listing fields they already expect.
If you're at a large institution with a dedicated data team and a $30K+ annual data budget, ATTOM and CoreLogic are legitimate options. They have deep data and established enterprise contracts.
If you're a startup, a smaller fund, an independent analyst, or a PropTech company that needs investor-grade data at a reasonable price, those providers are the wrong fit. The sales process alone can take months, and minimum contract terms are often prohibitive for teams that haven't yet validated what data they actually need.
Specrom gives you the same depth of data, transparent usage-based pricing, and no contract requirements. You can start with a sample dataset, evaluate the fields you care about, and integrate in days rather than months.
// Query by ZIP + filters — no enterprise contract GET /listings ?zip=11223 &status=for_sale &days_on_market_min=30 &is_price_reduced=true // Returns properties with full investment fields: // list_price, estimates, tax_history, // property_history, flood risk, flags { "total": 23, "listings": [ { // ...50+ fields including 3 AVMs } ] }
Each property record includes AVM estimates from Quantarium, Cotality, and Collateral Analytics. Having three matters for investment modeling because a single AVM can be wrong. Three estimates let you triangulate, build confidence intervals, and flag properties where estimates diverge significantly — which is itself an investment signal worth investigating.
Tax history includes a multi-year annual assessment series per property. The depth varies by county and how far back the MLS source data extends, but typically covers at least 3–5 years of assessment amounts and rate data.
Yes. Every property record includes the county FIPS code, which makes it straightforward to join to census data, employment data, permit data, or any other external dataset keyed by county. Lat/lon coordinates are also included for spatial joins.
The property_history field includes every time the property was listed, delisted, relisted, or had a price change. This includes list price at each event, status at each event, and dates. The last_price_change_date and last_price_change_amount fields give you the most recent change directly on the top-level record for easy filtering.
The data refreshes daily. Delta feeds are available for teams running daily ingestion — these deliver only changed records (new listings, price changes, status updates, delistings) rather than requiring a full dataset pull on every refresh.
US-wide MLS coverage across major metro areas and regions, refreshed daily. Coverage is not uniform across every small regional MLS. For coverage confirmation across specific ZIP codes or metro areas relevant to your investment thesis, reach out and we'll verify before you integrate.
Yes — this is specifically who the product is built for. If you're a startup, a smaller fund, an independent analyst, or a PropTech company that needs investor-grade data without a $30K+ annual contract, Specrom gives you the same field depth with usage-based pricing, no minimum terms, and a free sample to evaluate before you commit.
Tell us your use case and target markets. We'll respond with a free sample and pricing within 24 hours.