How are median home values calculated?

The ACS asks respondents to estimate how much their property (house and lot, mobile home and lot, or condominium unit) would sell for if it were for sale. The median represents the middle value.

Does 'Value' mean assessment or market value?

It reflects an estimated market value by the owner, not assessed tax value or recent sales price.

What does Gross Rent include?

Gross rent includes the contract rent plus the estimated average monthly cost of utilities (electricity, gas, and water and sewer) and fuels.

U.S. Census Housing — Home Values, Rent & Real Estate Data MCP Server for Pydantic AI 5 tools — connect in under 2 minutes

Name: U.S. Census Housing — Home Values, Rent & Real Estate Data
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 5 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect U.S. Census Housing — Home Values, Rent & Real Estate Data through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to U.S. Census Housing — Home Values, Rent & Real Estate Data "
            "(5 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in U.S. Census Housing — Home Values, Rent & Real Estate Data?"
    )
    print(result.data)

asyncio.run(main())

U.S. Census Housing — Home Values, Rent & Real Estate Data

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About U.S. Census Housing — Home Values, Rent & Real Estate Data MCP Server

U.S. Census housing data.

Pydantic AI validates every U.S. Census Housing — Home Values, Rent & Real Estate Data tool response against typed schemas, catching data inconsistencies at build time. Connect 5 tools through the Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code — full type safety, structured output guarantees, and dependency injection for testable agents.

5 Tools

Housing by State — Values, rent, ownership
Housing by County — Drill down into local markets
State Profile — Full socioeconomic snapshot
County Profile — Deep dive into specific counties
Query Census — Run any custom API query

Authentication

Requires a free API key from the Census Bureau.

The U.S. Census Housing — Home Values, Rent & Real Estate Data MCP Server exposes 5 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect U.S. Census Housing — Home Values, Rent & Real Estate Data to Pydantic AI via MCP

Follow these steps to integrate the U.S. Census Housing — Home Values, Rent & Real Estate Data MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 5 tools from U.S. Census Housing — Home Values, Rent & Real Estate Data with type-safe schemas

Why Use Pydantic AI with the U.S. Census Housing — Home Values, Rent & Real Estate Data MCP Server

Pydantic AI provides unique advantages when paired with U.S. Census Housing — Home Values, Rent & Real Estate Data through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture — switch between OpenAI, Anthropic, or Gemini without changing your U.S. Census Housing — Home Values, Rent & Real Estate Data integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your U.S. Census Housing — Home Values, Rent & Real Estate Data connection logic from agent behavior for testable, maintainable code

U.S. Census Housing — Home Values, Rent & Real Estate Data + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the U.S. Census Housing — Home Values, Rent & Real Estate Data MCP Server delivers measurable value.

Type-safe data pipelines: query U.S. Census Housing — Home Values, Rent & Real Estate Data with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple U.S. Census Housing — Home Values, Rent & Real Estate Data tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query U.S. Census Housing — Home Values, Rent & Real Estate Data and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock U.S. Census Housing — Home Values, Rent & Real Estate Data responses and write comprehensive agent tests

U.S. Census Housing — Home Values, Rent & Real Estate Data MCP Tools for Pydantic AI (5)

These 5 tools become available when you connect U.S. Census Housing — Home Values, Rent & Real Estate Data to Pydantic AI via MCP:

get_county_profile

Get a comprehensive profile for a specific county

get_housing_by_county

Essential for real estate investors, developers, and market researchers. Get housing data for all counties in a state — values, rent, ownership

get_housing_by_state

renter-occupied, and vacant units for every state. The #1 dataset for real estate market analysis. Get housing data for all states — home values, rent, ownership rates, vacancies

get_state_profile

One query = complete state intelligence. Get a comprehensive socioeconomic profile for a state — population, income, housing, demographics

query_census

Specify ACS variables (e.g., B01003_001E for population), geography (state:*, county:*, place:*), optional in-clause for filtering, year, and dataset path. See api.census.gov/data.html for all available datasets and variables. Run a custom Census API query — any dataset, variables, and geography

Example Prompts for U.S. Census Housing — Home Values, Rent & Real Estate Data in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with U.S. Census Housing — Home Values, Rent & Real Estate Data immediately.

"What is the median home value in California?"

"How many houses are vacant in Nevada?"

"Use a custom query to find the renter to ownership ratio in Ohio"

Troubleshooting U.S. Census Housing — Home Values, Rent & Real Estate Data MCP Server with Pydantic AI

Common issues when connecting U.S. Census Housing — Home Values, Rent & Real Estate Data to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

U.S. Census Housing — Home Values, Rent & Real Estate Data + Pydantic AI FAQ

Common questions about integrating U.S. Census Housing — Home Values, Rent & Real Estate Data MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer — your U.S. Census Housing — Home Values, Rent & Real Estate Data MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.