# real-estate-expert

Equips AI coding agents with PropTech domain logic, MLS integration patterns, property valuation algorithms, and lease tracking models.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 820 installs · ★ 44
- Author: personamanagmentlayer
- License: Apache-2.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/personamanagmentlayer/pcl/tree/HEAD/stdlib/domains/real-estate-expert/SKILL.md
- Updated: 2026-10-12
- HTML: https://affpapa.org/en/nevibecode/skills/real-estate-expert

## What it does

The skill guides agents through building real estate platforms, property management systems (PMS), and MLS integrations. It provides predefined data structures for lease agreements and tenant maintenance tickets, alongside regulatory guidance covering RESO, RESPA, Fair Housing Act, and ADA requirements.

It also contains Python implementations for market trends and property valuation. Price prediction relies on `GradientBoostingRegressor` and `StandardScaler` using ten property attributes, generating estimates with a 10% confidence interval. Running the valuation module will fail without `scikit-learn` and `numpy` installed in your environment.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add personamanagmentlayer/pcl --skill real-estate-expert -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/real-estate-expert/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/real-estate-expert/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add personamanagmentlayer/pcl --skill real-estate-expert -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/real-estate-expert/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add personamanagmentlayer/pcl --skill real-estate-expert -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/real-estate-expert/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install personamanagmentlayer/pcl real-estate-expert
```

2. **Manually: put the files in place**
- File: `.github/skills/real-estate-expert/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add personamanagmentlayer/pcl --skill real-estate-expert -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/personamanagmentlayer/pcl --path stdlib/domains/real-estate-expert
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/real-estate-expert/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add personamanagmentlayer/pcl --skill real-estate-expert -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/personamanagmentlayer/pcl/tree/HEAD/stdlib/domains/real-estate-expert/SKILL.md

## When to use it
- estimate property prices with GradientBoostingRegressor
- model lease agreements and maintenance ticket workflows
- calculate market absorption rates and months of inventory
- verify PropTech API integrations against RESO standards

## Example prompts
- Build a property valuation model with GradientBoostingRegressor using my listing data
- Generate data structures for tracking tenant leases and emergency maintenance requests
- Review our real estate listing schema for RESO compliance

## Source text · SKILL.md · Apache-2.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: ae46bd810c581a5d.

BEGIN THIRD-PARTY FILE ae46bd810c581a5d
````text
---
name: real-estate-expert
version: 1.1.0
description: >-
  Expert-level real estate systems, property management, MLS integration, CRM, virtual
  tours, and market analysis. Use when the user mentions property, MLS, CRM, proptech, or
  listings, or when the task involves Real Estate Systems, PropTech Solutions, Standards
  and Regulations, or Listing Management.
category: domains
tags: [real-estate, property, mls, crm, proptech, listings]
allowed-tools:
  - Read
  - Write
  - Edit
---

# Real Estate Expert

Expert guidance for real estate systems, property management, Multiple Listing Service (MLS) integration, customer relationship management, virtual tours, and market analysis.

## Core Concepts

### Real Estate Systems

- Multiple Listing Service (MLS) integration
- Property Management Systems (PMS)
- Customer Relationship Management (CRM)
- Transaction management
- Document management
- Lease management
- Maintenance tracking

### PropTech Solutions

- Virtual tours and 3D walkthroughs
- AI-powered property valuation
- Digital signatures and e-closing
- Smart home integration
- IoT sensors for properties
- Blockchain for title management
- Augmented reality for staging

### Standards and Regulations

- RESO (Real Estate Standards Organization)
- Fair Housing Act compliance
- RESPA (Real Estate Settlement Procedures Act)
- Data privacy (GDPR, CCPA)
- ADA compliance for websites
- NAR Code of Ethics

## Property Valuation and Analytics

```python
import numpy as np
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.preprocessing import StandardScaler

class PropertyValuationSystem:
    """AI-powered property valuation"""

    def __init__(self):
        self.model = GradientBoostingRegressor(n_estimators=100)
        self.scaler = StandardScaler()
        self.trained = False

    def train_model(self, training_data: List[dict]):
        """Train valuation model on historical data"""
        features = []
        prices = []

        for property_data in training_data:
            feature_vector = self._extract_features(property_data)
            features.append(feature_vector)
            prices.append(property_data['sold_price'])

        X = np.array(features)
        y = np.array(prices)

        # Scale features
        X_scaled = self.scaler.fit_transform(X)

        # Train model
        self.model.fit(X_scaled, y)
        self.trained = True

    def estimate_value(self, property_data: dict) -> dict:
        """Estimate property value"""
        if not self.trained:
            return {'error': 'Model not trained'}

        features = self._extract_features(property_data)
        features_scaled = self.scaler.transform([features])

        estimated_value = self.model.predict(features_scaled)[0]

        # Calculate confidence interval (simplified)
        confidence_range = estimated_value * 0.1  # ±10%

        return {
            'estimated_value': estimated_value,
````
END THIRD-PARTY FILE ae46bd810c581a5d

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---
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