Zillow Real Estate Intelligence for Openclaw

A professional-grade real estate analysis skill for interpreting Zillow data, calculating investment metrics, and formulating property pricing strategies.

ivangdavila
v1.0.0
Feb 17, 2026
3
1.2k
0

Install & Download

1. ClawHub CLI

The fastest way to install a skill directly from the registry.

npx clawhub@latest install zillow

2. Manual Installation

Copy the skill folder to one of these locations

Global
~/.openclaw/skills/
Workspace
<project>/skills/

Priority: Workspace > Local > Bundled

3. Prompt Installation

Copy this prompt to OpenClaw to install it automatically.

Help me install zillow using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).

Prefer to download?

Get the raw skill files in a ZIP archive.

What is Zillow Real Estate Intelligence?

The Zillow skill for Openclaw Skills transforms your AI agent into a sophisticated real estate consultant. It provides a robust framework for navigating the US housing market, moving beyond surface-level listings to provide deep insights into property valuation and market dynamics. By utilizing this skill, users can accurately interpret Zestimates, evaluate hyperlocal trends, and avoid common traps associated with stale data or misleading listing prices.

Designed for developers and real estate enthusiasts, this skill integrates specialized logic for different user personas—ranging from first-time homebuyers to seasoned investors. It ensures that every property analysis accounts for the "true" cost of ownership, including property taxes, insurance, and HOA fees, while applying conservative financial modeling to investment opportunities.

Zillow Real Estate Intelligence Use Cases

  • Performing hyperlocal market research to identify pricing trends and days-on-market benchmarks.
  • Calculating comprehensive monthly carrying costs for buyers, including PMI and special assessments.
  • Conducting rental property ROI analysis using the 50% expense rule and vacancy adjustments.
  • Developing competitive listing strategies for sellers by comparing active listings against actual sold comps.
  • Auditing Zestimate accuracy by cross-referencing historical data and ZIP-code specific variance.

How Zillow Real Estate Intelligence Works

  1. The agent identifies the user's role (Buyer, Seller, Investor, or Agent) to tailor the analysis framework.
  2. It retrieves property data and applies a mandatory caveat check on Zestimates based on local market volatility.
  3. For buyers, the skill triggers a multi-component cost calculation that pulls actual tax records and regional insurance estimates.
  4. For investors, it utilizes the investing logic to determine cap rates and cash-on-cash returns based on realistic acquisition costs rather than list prices.
  5. The skill cross-references Zillow data with secondary sources like FEMA maps or county assessors to verify property specifications and flood risks.

Zillow Real Estate Intelligence Setup

To integrate this skill into your environment, ensure you have the core Openclaw Skills framework installed. Clone the skill repository and add the configuration to your agent's directory.

openclaw install zillow

Ensure that the investing.md and pricing.md reference files are available in your skills path to enable advanced calculation logic.

Zillow Real Estate Intelligence Data Schema & Taxonomy

The skill organizes real estate data into specific taxonomies for accurate processing:

Data Group Key Metrics
Valuation Zestimate, Estimated Range, Forecasted Appreciations, Sold Comps
Carrying Costs Principal, Interest, Property Taxes (Actual), Insurance, HOA, PMI
Investment Cap Rate, Cash-on-Cash Return, Gross Rent Multiplier, Vacancy Factor
Market Stats Days on Market (DOM), Views/Saves, Inventory Levels, ZIP Context

Zillow Real Estate Intelligence Advanced Features

  • Hyperlocal context adjustment: Logic that differentiates market velocity between high-density urban areas and suburban markets.
  • Investment risk mitigation: Automated application of the 50% expense rule to prevent over-optimistic ROI projections.
  • Multi-source cross-referencing: Hooks to integrate data from FEMA flood maps and local county assessor databases.
  • Persona-based guidance: Dynamic response formatting tailored specifically for the concerns of sellers vs. first-time buyers.

SKILL.md


Loading

Related Openclaw Skills

METADATA

Github Stars: 0
forks: 0

Featured*