About AddressIntel
AddressIntel tracks building permits, off-market teardowns, and zoning changes across the San Francisco Peninsula, then scores properties for redevelopment potential so investors and developers can spot opportunities before they reach the open market.
Founder
Andrew
Andrew is the founder of AddressIntel. He focuses on San Francisco Peninsula real estate: the high-value corridor from San Mateo through Palo Alto where land routinely outweighs the structure standing on it. His background is in data: building and maintaining the pipelines that pull municipal permits, listings, and county records into a single, queryable picture of the market.
AddressIntel grew out of that work as a way to make the same permit and parcel data that professional developers rely on legible to a wider audience, without the manual portal scraping it normally takes.
Where Our Data Comes From
Municipal permit portals
We ingest building, demolition, and planning permits directly from the systems cities publish them on, primarily Accela, eTRAKiT, and Tyler EnerGov. This is what lets us see a teardown coming at the permit stage rather than after the sale.
Listing & MLS feeds
Active and sold listings come from RESO-compliant real estate feeds and syndicated listing APIs. We use listing price, days on market, sale-to-list ratios, and listing language as redevelopment signals, not as a consumer search portal.
County assessor & public records
Parcel data, assessed land and improvement values, lot size, year built, and transaction history come from county assessor and recorder records, covering San Mateo, Santa Clara, and Alameda counties on the Peninsula. AddressIntel aggregates this public data and does not claim ownership over it.
Building footprints & GIS overlays
Structure footprints used to estimate lot coverage and buildable headroom come from city GIS layers where cities publish them, supplemented by Overture Maps Foundation building data (© OpenStreetMap contributors, Overture Maps Foundation), available under the Open Database License. Flood, fire, and seismic overlays come from FEMA, CAL FIRE/OSFM, and the California Geological Survey.
Update cadence: sources are fetched, reconciled, and re-scored on a recurring schedule so that permit and listing changes surface within hours, not weeks.
How Teardown Scores Are Calculated
The Teardown Score is a 0–100 estimate of how likely a property is a candidate for demolition and new construction. It is highest when the land value dominates the structure: a large or well-located lot carrying a small, old, or low-coverage house. We compute it from the following inputs:
- Land vs. structure: lot size relative to the building footprint and assessed land value relative to improvement value.
- Structure age & condition: year built, any recorded renovation year (a recent remodel counts against teardown likelihood), and lot coverage; low coverage signals room to build.
- Listing language:phrases like “value in the land,” “build your dream,” or “contractor special” raise the score; “renovated” or “move-in ready” lower it.
- Development levers: SB 9 lot-split eligibility, ADU/JADU feasibility, and zoning or hazard blockers that would undercut a redevelopment play.
These signals are weighed by a large language model that produces the numeric score and a short written rationale. Scores are estimates derived from public data and listing text, intended to prioritize research, not to substitute for a site visit, a contractor’s assessment, or municipal confirmation of what can be built.
Related scores
- Flippability Score: likelihood a property is a prime renovation target, based on neighborhood comps and local permit velocity.
- Condition Score: estimated property condition from listing description and price relative to after-repair value.
- Bidding-war probability: a gauge of hyper-local demand for a given property profile.
Data Usage
AddressIntel aggregates publicly available civic and market data to provide transparency into housing-stock turnover and municipal planning activity. We believe access to high-fidelity, well-organized property data benefits not only investors and developers but also civic planners and community advocates.