Ertas for Real Estate AI

The Challenge

The real estate industry generates enormous volumes of unstructured text: property listings, lease agreements, appraisal reports, market analyses, inspection reports, and tenant communications. Extracting actionable intelligence from this data requires understanding property-specific terminology (cap rates, NOI, gross leasable area, build-to-suit), local market knowledge (neighborhood names, zoning designations, comparable properties), and the nuanced language that differentiates a value-add opportunity from a stabilized asset.

Generic AI tools produce property descriptions that sound generic, miss critical deal points in lease abstractions, and generate market reports that lack the specific data points and local context that brokers and investors rely on. A listing description needs to highlight the features that matter for the specific property type and market. The features that sell a Class A office building are different from those that sell a suburban retail center. Generic models cannot make these distinctions because they lack training on real estate-specific language patterns and deal evaluation frameworks.

The Solution

Ertas lets real estate firms fine-tune models on their own portfolio data, historical listings and market reports. The resulting models learn the terminology, valuation language and market context of the firm's focus areas, whether that is multifamily in the Southeast, industrial in the Midwest or mixed-use urban development. In Ertas Studio, teams train on their best-performing listing descriptions, most accurate lease abstractions and highest-rated market reports, so the model writes in the style of their strongest analysts and brokers.

One fine-tuned model can serve several parts of the workflow. It drafts property descriptions that highlight the right features for each property type and market. It abstracts lease terms from complex multi-tenant agreements, pulling rent schedules, escalation clauses and tenant improvement allowances into structured data. It drafts market analysis sections for investment memos in the firm's own framework and terminology. Training runs on Ertas's cloud GPUs, so the training examples are uploaded to Ertas. Once trained, the model downloads from Hub and runs on the firm's own servers, so the deal data sent to it day to day stays inside the firm.

Key Features

Example Workflow

A commercial real estate brokerage manages 200 active listings and processes 50 new leases a month. The marketing team collects 5,000 high-performing listing descriptions (measured by engagement) and 3,000 completed lease abstractions, removes tenant contact details and anything under confidentiality agreements, and uploads them to Ertas Studio as JSONL. Senior abstractors review a sample of the lease examples in Data Craft and approve them. The team fine-tunes a model that writes listing descriptions in the brokerage's voice and fills its standard 35-field lease abstraction template, runs two base models in parallel, and compares both against a held-out set of listings and leases the model has not seen. They export the stronger run as GGUF and serve it with Ollama on an internal server. Brokers enter property details and get a draft description to edit. Paralegals paste lease text and get a structured abstraction with unusual clauses flagged for attorney review. Before widening use, the team tracks edit time and abstraction errors against its own baseline.