Commercial Re

Calculate cap rate, NOI, cash-on-cash, and DSCR for commercial real estate deals.

110|18|Updated Mar 25, 2026
One-click install
npx skills add https://github.com/TravisLeeeeee/awesome-openclaw-personas --skill commercial-re
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: Commercial Re
Source: https://github.com/TravisLeeeeee/awesome-openclaw-personas/tree/main/personas/real-estate/commercial-re
Command: npx skills add https://github.com/TravisLeeeeee/awesome-openclaw-personas --skill commercial-re

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Commercial Re solves the problem of uncertain, assumption-heavy commercial real estate evaluation by producing structured investment analysis, comparable property comparisons, and diligence-ready underwriting outputs.

Core Features & Use Cases

  • Investment Underwriting With Guardrails: Calculates key metrics (cap rate, NOI, cash-on-cash, DSCR) and runs downside/sensitivity scenarios instead of presenting projections as certainties, helping you decide whether a deal meets lender thresholds (e.g., DSCR).
  • Comparable (Comps) Valuation Support: Builds comparable property reports using adjustment methodology across location, size, condition, and market differences to support valuation and negotiation.
  • Lease & Due Diligence Readiness: Analyzes lease structures (NNN, gross, modified gross, percentage rent) and generates diligence checklists covering common investigation areas.

Quick Start

Copy the Commercial Re persona folder into your OpenClaw workspace, then ask it to analyze a specific property deal with the asking price and NOI so it can produce a metrics table, red flags, and sensitivity analysis.

Frequently Asked Questions about Commercial Re

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run commercial real estate deal underwriting with DSCR and cap rate calculations?

Commercial real estate deal underwriting requires calculating cap rate, NOI, cash-on-cash, and DSCR to evaluate lender thresholds. You input the asking price and NOI to generate a metrics table, identify red flags, and run sensitivity analyses for occupancy and expense downside scenarios.

What is risk-aware commercial real estate investment analysis?

Risk-aware commercial real estate investment analysis evaluates property deals by applying sensitivity modeling to occupancy and expense downsides instead of presenting projections as certainties. It calculates key metrics like cap rate, NOI, cash-on-cash, and DSCR with assumption transparency to support confident decision-making.

How do I build a comparable property comps report with adjustment methodology?

Building a comparable property comps report involves applying adjustment methodology across location, size, condition, and market differences. This structured approach supports property valuation and negotiation by comparing targeted commercial assets against similar recent transactions.

Can I analyze NNN and gross lease structures for due diligence readiness?

Yes, you can analyze lease structures including NNN, gross, modified gross, and percentage rent for due diligence readiness. The analysis evaluates lease terms and generates diligence checklists covering common commercial real estate investigation areas.

What is the best way to evaluate if a commercial deal meets lender DSCR thresholds?

Evaluating if a commercial deal meets lender DSCR thresholds requires calculating the debt service coverage ratio alongside NOI and cash-on-cash returns. Applying downside sensitivity scenarios to occupancy and expenses tests whether the deal maintains acceptable risk-adjusted metrics under stress.

When should I use sensitivity modeling for commercial real estate expense downsides?

Sensitivity modeling for commercial real estate expense downsides should be used during deal underwriting to test projection resilience. It reveals whether cap rate, NOI, and DSCR calculations remain viable under stressed occupancy and expense scenarios, preventing assumption-heavy evaluation errors.