AI Deal Screening | Real Estate Automation

Commercial Real Estate

First-Pass Underwriting with AI (CRE Quick Qualifier)

AI-assisted deal screening that reduced analyst workload by 70% and cut screening time from 45 minutes to under 3 minutes.

Real Estate automation case study featuring document processing and ai implementation.

70%

Analyst Workload Reduction

<3 min

Screening Time per Deal

70+

Deals Processed Monthly

Overview

A commercial real estate investment firm reviewing over 70 deals per month was drowning in the volume of incoming deal packets. Each deal arrived as an email with attached PDFs — offering memorandums, rent rolls, T12 operating statements, unit mix spreadsheets — all in different formats from different brokers. Analysts spent 45 minutes or more on initial screening of each deal, just to determine if it warranted deeper analysis. Many deals were obvious non-starters based on location, cap rate, or property type, but the manual review process treated every deal the same. The inconsistency across analysts meant some promising deals were incorrectly passed over while time was wasted on properties that didn't meet basic criteria.

Business Context

The firm's deal flow had outpaced their capacity. They were either missing good deals buried in the queue or burning out analysts with repetitive screening work. Hiring more analysts was expensive and created training overhead. The partners recognized that the first-pass screening — determining if a deal warrants deeper analysis — could be systematized with clear criteria. Properties that failed on location, cap rate, rent growth potential, or NOI margins could be quickly filtered, allowing analysts to focus their time on deals that actually mattered. ZapWizards was engaged to build an AI-powered Quick Qualifier system that would automate initial screening while maintaining the firm's investment criteria.

How We Built It

We built an AI extraction engine that handles the complexity of broker deal packets regardless of format. The system ingests documents from email, Dropbox, or direct upload and uses Google Vision OCR combined with OpenAI to extract structured data from Rent Rolls, Offering Memorandums, T12 statements, and unit mix documents. The extraction handles the variety of formats brokers use — some send Excel files, others PDFs, some include everything in the OM, others send separate files for each document type. Extracted data feeds into a Quick Qualifier scoring model that evaluates deals against the firm's investment criteria: location quality, cap rate ranges, rent growth potential relative to market, and NOI margins. Deals that score above threshold trigger automated Slack alerts to the acquisition team and CRM updates that flag them for analyst review. A central dashboard shows all deals in the screening pipeline with their qualification status. Promising deals are automatically assigned to analysts based on property type specialization and current workload.

Challenges

Analysts overwhelmed reviewing raw data

Completely inconsistent deal packets from brokers

No quick way to identify non-starters

Email-based flow impossible to scale

What We Delivered

AI engine to extract Rent Roll, OM, T12, unit mix

Quick Qualifier scoring model: location, cap rate, rent growth, NOI

Automated Slack & CRM alerts for recommended deals

Central dashboard showing all deals in screening

Workflow to assign promising deals to analysts automatically

Tech Stack

OpenAI, Make.com, HubSpot, Dropbox, Google Vision, Retool

Results

70%

Analyst Workload Reduction

<3 min

Screening Time per Deal

70+

Deals Processed Monthly

Strategic Impact

The 70% reduction in analyst workload transformed the firm's capacity for growth. Screening time dropped from 45 minutes to under 3 minutes per deal, and analysts now spend their valuable expertise on deals that actually fit the firm's criteria rather than weeding through obvious non-starters. The consistency of AI screening eliminated analyst-to-analyst variation that previously caused promising deals to slip through or excessive time to be wasted on properties that didn't meet basic investment parameters. The firm can now handle significantly increased deal flow without hiring additional analysts, and existing team members report higher job satisfaction because they're doing meaningful analytical work rather than repetitive document extraction. The Quick Qualifier has become a genuine competitive advantage — the firm responds to promising deals faster than competitors still relying on manual screening, often getting first meetings with brokers while others are still reading through documents. Broker relationships have improved as the firm has developed a reputation for fast, decisive responses.