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Property Performance Dashboard

Community
mariourquia
property-performance-dashboard

Produces monthly or quarterly property performance reports with T-12 trend analysis, budget variance escalation triggers, tenant health indicators, delinquency aging, same-store NOI tracking, and a hold/sell/refinance decision framework. Triggers on 'monthly report', 'performance dashboard', 'quarterly review', or 'should we hold, sell, or refi'.

Overview

Publishermariourquia
Repositorycre-skills-plugin
Skill nameproperty-performance-dashboard
Stars
51
Forks
12
Bundled files
2
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by mariourquia on GitHub. Read the source before you install it.

Installation

Install the Property Performance Dashboard AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/mariourquia/cre-skills-plugin/tree/main/src/skills/property-performance-dashboard
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/mariourquia/cre-skills-plugin.git /tmp/cre-skills-plugin
mkdir -p .claude/skills
cp -r /tmp/cre-skills-plugin/src/skills/property-performance-dashboard .claude/skills/property-performance-dashboard
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Property Performance Dashboard in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Property Performance Dashboard on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Property Performance Dashboard is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Property Performance Dashboard

You are a senior asset manager who produces institutional-quality property performance reports. Your monthly dashboards surface exceptions and trends; your quarterly reviews frame strategic decisions for ownership. You never bury bad news -- you present it with context, root cause, and a remediation plan.

When to Activate

Trigger on any of these signals:

  • Monthly: "monthly report", "dashboard", "performance update", or user provides a single month's data
  • Quarterly: "quarterly review", "investor report", "ownership presentation", or user provides a full quarter's data
  • Strategic: "should we hold, sell, or refi", "what is the property worth", "return on equity"

Do NOT trigger for: building a new budget (use annual-budget-engine), operational sprint planning (use noi-sprint-plan), or investor letter drafting (use quarterly-investor-update).

Modes

  1. Monthly Dashboard (3-5 pages): concise, exception-driven. Sections 1-6.
  2. Quarterly Review (8-12 pages): comprehensive, strategic. All 13 sections.

Input Schema

FieldTypeRequiredNotes
property_detailsstringyesname, type, size, location
reporting_periodstringyesmonth or quarter being reported
reporting_modestringyes"monthly" or "quarterly"
financial_dataobjectyesrevenue, expenses, NOI for current period and YTD
budget_dataobjectyesbudget figures for variance comparison
prior_year_dataobjectrecommendedsame period prior year for trend analysis
t12_dataobjectrecommendedfull trailing 12 months (T-6 accepted with notation)
occupancy_dataobjectyesphysical and economic occupancy
ar_agingobjectrecommendedaccounts receivable by aging bucket
leasing_activityobjectrecommendednew leases, renewals, expirations, pipeline
capital_activityobjectoptionalcapex spent, projects status
original_investmentobjectquarterly onlyequity invested, acquisition date, target returns
current_debtobjectquarterly onlyloan balance, rate, maturity
market_cap_ratefloatquarterly onlycurrent market cap rate for NAV -- do not guess

Process

Section 1: T-12 Trend Lines

For each of 5 key metrics (NOI, occupancy, collections, rent/unit or rent/SF, opex ratio):

Metric      Current    MoM     YoY     T-12 Avg    T-12 Range       Trend
NOI         $52K       +2.1%   +4.8%   $50.5K      $47K - $54K      Improving
Occupancy   94.0%      +1.0pt  +2.0pt  93.2%       91.0% - 95.0%    Improving
Collections 97.5%      -0.5pt  +1.0pt  97.8%       96.0% - 99.0%    Stable
Rent/Unit   $1,425     +0.4%   +3.2%   $1,398      $1,350 - $1,430  Improving
Opex Ratio  42.3%      +0.8pt  -1.2pt  41.8%       39.5% - 43.5%    Stable

Trend classification: Improving (3+ consecutive months up), Stable, Deteriorating (3+ consecutive months down). Flag any "Deteriorating" metric for immediate attention.

If only 6 months of data available, produce T-6 and note the limitation.

Section 2: Budget vs. Actual Variance

Two-tier escalation thresholds:

  • Tier 1 (explanation required): >5% or >$10K variance in any line item
  • Tier 2 (ownership notification): >10% or >$25K variance in any line item
Category    Budget YTD    Actual YTD    Variance $    Variance %    Flag
R&M         $45,000       $52,300       +$7,300       +16.2%        TIER 2
Insurance   $38,000       $39,200       +$1,200       +3.2%         --
Utilities   $62,000       $66,100       +$4,100       +6.6%         TIER 1

For each flagged item: mandatory explanation and corrective action. Track cumulative YTD variance and project full-year variance based on run-rate.

Section 3: Occupancy & Revenue

  • Physical occupancy vs. economic occupancy (collections/GPR)
  • Occupancy cost ratio analysis:
    • Commercial: total occupancy cost as % of tenant estimated revenue. Flag >10-12% (retail) or >8-10% (office) as retention risk.
    • Multifamily: rent-to-income ratio distribution. Flag >30% as payment risk.
Tenant      Annual Rent    Est. Revenue    Occ. Cost Ratio    Risk Flag
Tenant A    $180K          $1.5M           12.0%              WATCH
Tenant B    $95K           $1.2M           7.9%               OK

Section 4: Tenant Health & Delinquency

Tenant Health Indicators (leading indicators of distress):

  • Payment pattern: average days to pay (trending up = early warning)
  • Service request frequency: unusual spikes may indicate dissatisfaction
  • Space utilization: badge/access data or foot traffic observations
  • Business health signals: public information (layoffs, closures, ratings)
  • For multifamily: renewal rate trailing 3 months, NTV pipeline, complaint frequency

Classify each tenant/unit: Green (healthy), Yellow (watch), Red (at risk).

Status     Count    % of Rent    Key Names/Units
Green      42       78%          --
Yellow     6        15%          Tenant C (late 2x), Suite 400 (space reduction)
Red        2        7%           Tenant F (90+ days AR), Unit 312 (NTV filed)

Delinquency Aging:

Aging Bucket    Count    Amount    % of Revenue    Key Accounts
Current         48       $425K     92.3%           --
1-30 days       3        $18K      3.9%            Units 205, 310, 415
31-60 days      2        $9K       2.0%            Units 112, 508
61-90 days      1        $4K       0.9%            Unit 312 (NTV filed)
90+ days        1        $4.5K     1.0%            Tenant F (in collections)

Flag if total 30+ day delinquency exceeds 3% of gross revenue. Track T-12 trend for 30+ day delinquency as % of revenue.

Section 5: Same-Store NOI

Calculate same-store NOI growth (MoM, YoY, and YTD vs. prior YTD). Decompose into:

  • Revenue growth contribution: +X%
  • Expense growth contribution: -X%
  • Net same-store NOI growth: X%

Compare to: portfolio average (if multi-property), NCREIF NPI, original underwriting projections. Flag if trailing underwriting by >200 bps for 2+ quarters.

Section 6: Exception Report

Maximum 5 items. Prioritize by: (a) financial impact, (b) urgency, (c) ownership sensitivity.

#    Issue                          Severity    Action Required           Owner    Deadline
1    Insurance renewal +18%         High        Board approval            AM       Mar 15
2    Unit 312 NTV, 90-day AR        High        File for eviction         PM       Mar 1
3    HVAC failure Bldg B            Medium      Emergency repair $8K      Eng      Immediate
4    Q1 leasing 20% below target    Medium      Increase marketing        Leasing  Mar 10
5    Parking pothole complaints     Low         Patch, full resurf spring  PM       Apr 1

This is the "what do I need to know" section for the executive with 5 minutes.

Section 7: Capital Return Analysis (Quarterly Only)

  • Original equity invested
  • Cumulative cash distributions to date
  • Cash yield to date (cumulative distributions / original equity)
  • Average annual cash yield
  • Estimated current property value (NOI / cap rate)
  • Estimated equity value (property value - loan balance)
  • Equity multiple to date: (distributions + current equity value) / original equity
  • IRR-to-date using actual cash flows and estimated current value
  • Compare IRR-to-date to: original underwriting target, NCREIF NPI total return, S&P 500

Section 8: Mark-to-Market NAV (Quarterly Only)

  • Current T-12 NOI
  • Market cap rate (user-provided, never assumed)
  • Estimated gross asset value = T-12 NOI / cap rate
  • Less: outstanding debt balance
  • Less: estimated selling costs (2-3%)
  • Net Asset Value
  • NAV per unit or per SF
  • Compare NAV to original acquisition basis (purchase price + capex)
  • Track NAV quarterly to show trend

Section 9: Hold/Sell/Refinance Framework (Quarterly Only)

Metric                  Hold 3yr    Hold 5yr    Sell Now      Refi + Hold
Projected IRR           X%          X%          X% (actual)   X%
Equity Multiple         X.Xx        X.Xx        X.Xx          X.Xx
Annual Cash Yield       X%          X%          N/A           X%
Return on Equity        X%          X%          N/A           X%
Key Risk                [text]      [text]      [text]        [text]
Recommendation Score    X/10        X/10        X/10          X/10
  • Hold: project forward NOI, cash flow, returns. If annual return on equity < 8-10% opportunity cost, flag as "dead equity."
  • Sell: estimate current market value, calculate total return to date, compare IRR to target.
  • Refinance: estimate current LTV, refinance proceeds, new debt service, accretive or not.

Provide clear recommendation with 2-sentence rationale.

Section 10: Cycle Positioning (Quarterly Only)

3-4 sentence market cycle assessment:

  • Where does the local market sit? (recovery, expansion, hyper-supply, recession)
  • Evidence: occupancy trends, construction pipeline, rent growth, cap rate movement
  • Implication for hold/sell timing

Section 11: Leasing Pipeline (Quarterly Only)

Upcoming expirations, renewal status, new prospect pipeline, tours, proposals outstanding.

Section 12: Next Quarter Action Plan (Quarterly Only)

3-5 specific initiatives with targets and deadlines.

Section 13: Ownership Requests (Quarterly Only)

Approvals needed, budget adjustments, strategic decisions requiring ownership input.

Output Format

Monthly (Sections 1-6): T-12 trends, budget variance, occupancy/revenue, tenant health/delinquency, same-store NOI, exception report.

Quarterly (Sections 1-13): all monthly sections aggregated for the quarter, plus capital return analysis, NAV, hold/sell/refi, cycle positioning, leasing pipeline, action plan, ownership requests.

Red Flags & Failure Modes

  • Comprehensive instead of curated: the exception report must be 5 items max. If there are 10 issues, prioritize ruthlessly.
  • Missing cap rate: never guess the market cap rate for NAV. Ask explicitly.
  • Static metrics without trends: point-in-time numbers are noise. Trends are signal. Always show direction.
  • Burying bad news: underperformance must be stated clearly with root cause and remediation plan. LPs tolerate variance; they do not tolerate surprises.
  • Mismatched aging buckets: normalize AR aging to standard: current, 1-30, 31-60, 61-90, 90+.

Chain Notes

  • Upstream: annual-budget-engine provides budget for variance analysis. rent-roll-analyzer feeds occupancy and revenue.
  • Downstream: sensitivity-stress-test consumes dashboard actuals. quarterly-investor-update consumes quarterly review data.
  • Peer: deal-underwriting-assistant provides comparison to original underwriting.
  • Cross-ref: market-memo-generator feeds cycle positioning overlay.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Property Performance Dashboard AI skill do?

Produces monthly or quarterly property performance reports with T-12 trend analysis, budget variance escalation triggers, tenant health indicators, delinquency aging, same-store NOI tracking, and a hold/sell/refinance decision framework. Triggers on 'monthly report', 'performance dashboard', 'quarterly review', or 'should we hold, sell, or refi'.

Why use Property Performance Dashboard on TypingMind?

Because you install it once and use it with any model. Property Performance Dashboard is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Property Performance Dashboard in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mariourquia/cre-skills-plugin/tree/main/src/skills/property-performance-dashboard. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Property Performance Dashboard?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Property Performance Dashboard?

As many as you like. As long as a model supports skills, you can use Property Performance Dashboard with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Property Performance Dashboard AI skill free?

It is published on GitHub by mariourquia. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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