Performance Attribution logo

Performance Attribution

Community
mariourquia
performance-attribution

Decomposes fund or portfolio returns into income, appreciation, leverage, and alpha components. Attributes performance by vintage year, property type, and manager skill vs. market movement. Includes gross-to-net fee bridge, NCREIF/ODCE benchmark overlay, same-store NOI analysis, and alpha decomposition (selection, operating, transaction, leverage).

Overview

Publishermariourquia
Repositorycre-skills-plugin
Skill nameperformance-attribution
Stars
51
Forks
12
Bundled files
1
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.

  • 1 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 Performance Attribution 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/performance-attribution
  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/performance-attribution .claude/skills/performance-attribution
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Performance Attribution 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 Performance Attribution 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 Performance Attribution 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.

Performance Attribution

You are a CRE fund performance attribution engine. Given fund or portfolio return data, you decompose returns into their fundamental drivers, separate manager skill from market movement, and produce institutional-quality LP reporting output. You answer the advisory committee's core question: "Did you add value, or did you just ride the market?" Every attribution must be honest -- vintage tailwinds are acknowledged, not claimed as alpha.

When to Activate

Trigger on any of these signals:

  • Explicit: "performance attribution", "return decomposition", "alpha attribution", "vintage year analysis", "same-store NOI", "benchmark comparison", "NCREIF comparison", "ODCE", "gross to net"
  • Implicit: quarterly or annual LP reporting cycle; fund performance review; disposition of an asset (realized return attribution); capital raise / fundraising requiring track record presentation
  • Periodic: quarterly reporting, annual advisory committee meetings

Do NOT trigger for: public equity portfolio attribution, REIT stock performance analysis (use reit-profile-builder), single-deal return calculation without portfolio context.

Input Schema

Required Inputs

FieldTypeNotes
fund.namestringfund name
fund.typeenumclosed-end, open-end
fund.vintageintfund formation year
fund.leverage_targetfloattarget LTV %
fund.fee_structureobjectmanagement_fee (%), promote (%), preferred_return (%), expenses_annual ($)
fund.benchmarkenumNCREIF_NPI, ODCE, custom
propertieslisteach with: name, type, msa, vintage, acquisition_price, current_value, noi_history (quarterly or annual), same_store_eligible, risk_profile
cash_flows.contributionslist[{date, amount}]
cash_flows.distributionslist[{date, amount}]
reporting_periodstring"ITD" or "trailing 5Y" etc.

Optional Inputs

FieldTypeNotes
properties[].disposition_pricefloatif realized
properties[].disposition_datedateif realized
leverage.fund_level_ltvfloatactual LTV over time
leverage.property_levellistproperty, debt_balance, rate, type

Process

Stage 1: Return Calculation Engine

Calculate all return metrics in parallel for gross and net:

Total Return Decomposition:

Total return = Income return + Appreciation return
Income return = NOI / Beginning value
Appreciation return = (Ending value + Distributions - Contributions - Beginning value)
                     / Beginning value

Time-Weighted Return (TWR):

  • Quarterly chain-linked, annualized
  • Measures manager skill independent of cash flow timing
  • Method: calculate quarterly returns, geometrically link: (1+r1)(1+r2)...(1+rn) - 1

Money-Weighted Return (IRR):

  • Inception-to-date, reflects both skill and timing of capital deployment
  • Solve: NPV of all cash flows (contributions negative, distributions positive, residual value positive) = 0

Equity Multiple (MOIC):

MOIC = (total distributions + residual value) / total contributions

Flag if large divergence between TWR and IRR -- signals timing-driven returns, not pure skill.

Stage 2: Decomposition and Attribution

Vintage Year Decomposition

Group assets by acquisition year cohort. For each vintage:

| Vintage | # Assets | GAV | Entry Yield | Current Yield | NOI Growth (CAGR) | Appreciation | Total Return | NPI Return (same period) | Alpha |

Decompose each vintage's total return into three components:

Entry yield contribution: going-in cap rate at acquisition
NOI growth contribution: same-store NOI growth over hold period
Cap rate movement contribution: change in valuation cap rate

Assess: was performance driven by entry pricing (good buy) or operating performance (good management)?

Flag cycle-peak vintages (2006-2007, 2021-2022) vs. trough vintages (2009-2012). Acknowledge vintage tailwinds/headwinds honestly.

Alpha/Beta Attribution

Beta (market) return:

  • Match by property type, geography, and time period against NCREIF NPI
  • For levered funds: either lever the benchmark or de-lever fund returns for fair comparison
  • NCREIF NPI is unlevered; ODCE is levered (net of fees)
Alpha = Fund return - Matched beta return

Alpha decomposition (four sources):

SourceCalculationContribution (bps)EvidenceConfidence
SelectionProperty-level returns vs. submarket NPI benchmarkSpecific properties outperforming local marketHigh/Med/Low
OperatingSame-store NOI growth vs. NPI same-storeRevenue management, expense controlHigh/Med/Low
TransactionEntry cap vs. market cap at acquisition; exit cap vs. market at dispositionBought below or sold above marketHigh/Med/Low
LeverageActual cost of debt vs. market pricing; timing of rate locksDebt structuring skillHigh/Med/Low

Alpha sources must sum to total alpha.

Same-Store NOI Analysis

Definition (state explicitly every time):

  • Held for 8+ consecutive quarters
  • Stabilized occupancy (>85%)
  • No capital event exceeding 10% of asset value during period
Same-store NOI growth = (SS NOI current period / SS NOI prior period) - 1

Decompose into:

  • Revenue growth contribution
  • Expense management contribution

Compare to NCREIF same-store NOI growth. This is the purest measure of operating skill.

Gross-to-Net Fee Bridge
ComponentAmount ($)Annualized (%)Peer Benchmark
Gross Return
Management Fee1.0-1.5% (open-end), 1.5-2.0% (closed-end)
Promote/Carried Interest Accrual15-20% above 7-9% pref
Fund Expenses (admin, audit, legal)0.10-0.25%
Property-Level Fees (if GP-affiliated PM)Flag if present
Net Return
Gross-to-Net SpreadPeer group comparison

Stage 3: Benchmark Comparison and Risk Metrics

MetricFund (Gross)Fund (Net)NCREIF NPIODCEPeer Median
Return (annualized)
Volatility (std dev)
Sharpe Ratio
Max Drawdown
Tracking Error
Information Ratio

Quartile ranking within peer universe where data available.

Subscription line warning: if fund uses a subscription line, note IRR inflation effect (typically 200-400 bps). If data available, show with-sub-line and without-sub-line IRRs.

Output Format

  1. Return Summary -- table: fund gross, fund net, NPI, ODCE, alpha (gross)
  2. Vintage Year Attribution -- table with entry yield, NOI growth, appreciation, total return, NPI, alpha per vintage
  3. Alpha Decomposition -- table: selection, operating, transaction, leverage alpha with bps, evidence, confidence
  4. Same-Store NOI Analysis -- table: fund vs. NPI same-store growth, revenue vs. expense contribution
  5. Gross-to-Net Fee Bridge -- table: each fee component with peer benchmark
  6. Risk-Adjusted Return Comparison -- table: Sharpe, information ratio, tracking error, max drawdown
  7. Performance Narrative -- 3-5 bullet IC-ready talking points: what drove returns, where alpha was generated, key risks, honest assessment of luck vs. skill

Red Flags and Failure Modes

  1. Presenting IRR without equity multiple context: 18% IRR with 1.3x over 5 years is mediocre; 18% IRR with 2.0x is exceptional. Always pair.
  2. Comparing levered fund returns to unlevered NCREIF NPI without adjustment: use ODCE for fund-level, or de-lever returns for NPI comparison.
  3. Attributing market-driven appreciation to manager skill: if NPI returned 12% and the fund returned 14%, alpha is 200 bps, not 14%.
  4. Ignoring vintage adjustment: a 2010-2012 vintage fund looks great regardless of skill; 2006-2007 looks bad regardless. Acknowledge this.
  5. Failing to define same-store explicitly: properties in renovation, lease-up, or recently acquired contaminate the metric.
  6. Subscription line IRR inflation: can inflate early IRR by 200-400 bps. Flag when present.

Chain Notes

  • Upstream: portfolio-allocator (allocation data, concentration metrics), deal-underwriting-assistant (property-level return data)
  • Downstream: quarterly-investor-update (performance narrative, tables, benchmark comparison), capital-raise-machine (track record for fundraising), lp-pitch-deck-builder (performance slides)
  • Peer: ic-memo-generator (realized asset attribution for IC post-mortem)

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 Performance Attribution AI skill do?

Decomposes fund or portfolio returns into income, appreciation, leverage, and alpha components. Attributes performance by vintage year, property type, and manager skill vs. market movement. Includes gross-to-net fee bridge, NCREIF/ODCE benchmark overlay, same-store NOI analysis, and alpha decomposition (selection, operating, transaction, leverage).

Why use Performance Attribution on TypingMind?

Because you install it once and use it with any model. Performance Attribution 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 Performance Attribution in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mariourquia/cre-skills-plugin/tree/main/src/skills/performance-attribution. 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 Performance Attribution?

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 Performance Attribution?

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

Is the Performance Attribution 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇