Vinkius
NCREIF Custom Query

Analyze property indices and real estate performance data.
Claude Claude
ChatGPT ChatGPT
Cursor Cursor
Gemini Gemini
Windsurf Windsurf
VS Code VS Code
JetBrains JetBrains
Vercel Vercel
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Connect to your AI in seconds.

NCREIF Custom Query allows your AI client to run custom SQL-like queries against institutional real estate data. It pulls performance metrics, historical index returns (NPI), Cap Rates, and occupancy stats from major US indices like NPI and ODCE.

Forget manual spreadsheet lookups; just ask your agent for the numbers you need.

What your AI can do

Execute query

Run a custom query to pull any data point from NCREIF that fits your criteria.

Get historical npi

Retrieves historical total return performance for the NPI index over time.

Get predefined kpi

Fetches standardized key metrics, such as Cap Rates and Occupancy percentages, using a simple request.

Calculate Total Returns

Run custom queries to calculate total returns based on income and appreciation data.

Access Historical Index Data

Get NPI historical return data over specific time ranges.

Retrieve Standard Metrics

Fetch predefined key performance indicators, like Cap Rates and Occupancy percentages.

Filter by Geography or Type

Limit query results using specific filters for region, CBSA, or property class.

Analyze Specific Indices

Target data from the NPI (Property Index) or ODCE (Fund Index).

Compatible AI Apps

OAuth 2.0 Compatible
Vinkius runs on Claude Claude
Vinkius runs on ChatGPT ChatGPT
Vinkius runs on Cursor Cursor
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Vinkius runs on Vercel Vercel
Vinkius runs on Zendesk Zendesk
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AI Agent

NCREIF Custom Query: 3 Tools for Real Estate Data

These three tools let your AI client execute custom queries, retrieve NPI history, or fetch predefined KPI data points.

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Start using NCREIF Custom Query on Vinkius

Execute Query

Run a custom query to pull any data point from NCREIF that fits your criteria.

Get Historical Npi

Retrieves historical total return performance for the NPI index over time.

Get Predefined Kpi

Fetches standardized key metrics, such as Cap Rates and Occupancy percentages, using...

Connect to your AI in seconds. Security and governance baked right in.

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Claude AI

Claude AI

1

Open Claude Settings

Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.

2

Add Custom Connector

Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:

https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. For OAuth-protected servers, expand Advanced settings to add credentials.

3

Start a conversation

Open a new chat. The NCREIF Custom Query integration is available immediately — no restart needed.

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Works with Claude, ChatGPT, Cursor, and more

The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.

This connection provides 3 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.

Pulling market intelligence usually means opening a dozen browser tabs and juggling spreadsheets.

Today's process requires exporting index data from one dashboard, downloading it to Excel, writing the formulas for total return calculations, then filtering by region in a separate tool. It’s repetitive, slow, and easy to miscalculate.

With NCREIF Custom Query MCP Server, you just ask your agent: 'What was the Q4 2025 total return for industrial properties.' The server runs the complex query instantly—you get the number, period.

The NCREIF Custom Query MCP Server delivers real estate data insights.

Before this, getting a simple KPI like Cap Rate meant finding the right report, waiting for it to refresh, and manually checking if the number applied to your specific property class. It was friction-filled work.

Now, you ask the agent for the predefined KPI data directly. The server executes `get_predefined_kpi` in milliseconds, giving you a clean metric without any manual clicks or guesswork.

What your AI can actually do with this

You're gonna let your AI client run custom SQL-like queries against institutional real estate data using the NCREIF Query Tool API.

This server pulls performance metrics, historical index returns (NPI), Cap Rates, and occupancy stats from major U.S. indices like NPI and ODCE. You don't gotta manually look up spreadsheets; you just ask your agent for the numbers you need.

When you use execute_query, you can pull any data point from NCREIF that fits your criteria. This means you control the formulas when calculating total returns, whether it's based on income or appreciation data. You also get to filter results deep down by geography, like limiting a query to specific regions or CBSA areas, or narrowing it by property class.

For index analysis, get_historical_npi retrieves historical total return performance for the NPI index over time. This lets you track how the market performed across different periods and calculate returns based on those long-term data points.

To get quick reads on standard metrics, you use get_predefined_kpi. That tool fetches standardized key metrics with a simple request, pulling numbers like Cap Rates and Occupancy percentages. You can target specific indices using this system; it handles both the NPI (Property Index) and the ODCE (Fund Index).

If you need to crunch the actual mechanics of returns, execute_query lets you run custom queries to calculate total returns that combine appreciation figures with income data points. You can also pull specific performance metrics for a given property type or region by using detailed filtering clauses within this query function.

The capabilities cover accessing historical index data specifically from NPI over set time ranges. Beyond the indices, get_predefined_kpi provides immediate access to standardized key indicators like Occupancy percentages and Cap Rates through a streamlined request process. You can use any of these tools together; for instance, you might first run an execute_query to pull raw income data for a specific CBSA area, then follow up with get_predefined_kpi to check the current occupancy percentage for that same region.

The system supports analyzing multiple indices. You can target the NPI or the ODCE when requesting performance metrics. If you need to restrict your search results, you've got filters available for the property type and specific geographic areas within a given state or county. Every tool operates on institutional-grade data, ensuring that what your agent hands you is accurate real estate intelligence.

You can ask execute_query to calculate complex total returns based on multiple income streams and appreciation inputs simultaneously. This query function doesn't just pull data; it runs calculations for you against the NCREIF dataset. When checking historical performance, get_historical_npi provides a time series of the NPI index's total return history.

The process is direct: one tool does one thing. You use get_predefined_kpi when you need standard numbers—think Cap Rates or Occupancy percentages—without building a massive query. Meanwhile, execute_query handles everything else; it pulls the granular data points and runs custom logic against them. Whether you're looking at raw property performance for filtering by region or needing to compare NPI versus ODCE historical trends, your agent can handle it.

Built · Hosted · Managed by Vinkius NCREIF Custom Query - Real Estate Index Analysis MCP Server
Server ID 019d75db-d26a-704a-b5a8-2cf8dc6cda73
Vinkius Inspector
Compliance Grade A+
Score 100/100
Vinkius Inspector Badge — Score 100/100

Questions you might have

How do I use NCREIF Custom Query to calculate total returns? +

You use the execute_query tool. You simply tell your agent what metrics you want to combine (like income and appreciation) and which time frame, letting the server handle the underlying calculation logic.

Can I check historical NPI returns using get_historical_npi? +

Yes. The get_historical_npi tool fetches performance data for the NPI index over specific, defined periods (e.g., last 4 quarters), giving you a clear time-series breakdown.

What is the difference between get_predefined_kpi and execute_query? +

The get_predefined_kpi tool handles common, standardized metrics (Cap Rates, Occupancy) with simple calls. Use execute_query when you need to build custom reports or combine multiple non-standard data points.

Do I need credentials for NCREIF Custom Query? +

Yes. You must subscribe and provide your full NCREIF Username and Password so the agent can authenticate against the institutional data source.

When using `execute_query`, how do I apply specific filters, like by region or property type? +

You filter results by including powerful 'WHERE' clauses directly in your query string. You specify the criteria—for example, filtering properties only within a certain CBSA code. This allows you to narrow down large datasets efficiently.

If I run `execute_query` and encounter an error, how should I troubleshoot it? +

First, check your SQL-like syntax against the NCREIF documentation; most issues are simple typos or incorrect column names. If the syntax is correct, verify that the requested data index is accessible with your current credentials.

Does `get_historical_npi` provide returns for specialized indices, such as timberland or farmland? +

The get_historical_npi tool focuses specifically on major US real estate indices. For highly specialized metrics like timberland performance, you should use the custom query feature with execute_query.

What is the ideal format for the input parameters when writing a complex query via `execute_query`? +

The tool expects an SQL-like string that defines both your required metrics (e.g., 'income returns') and the necessary filtering conditions. Keep the language concise and highly specific to ensure accurate results.

Who can access this API? +

Access is limited to NCREIF Data Contributor Members and paid Data Subscribers. You must use your website login credentials.

Built & Managed by Vinkius 30s setup 3 tools

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Vinkius runs on Claude Claude
Vinkius runs on ChatGPT ChatGPT
Vinkius runs on Cursor Cursor
Vinkius runs on Gemini Gemini
Vinkius runs on Windsurf Windsurf
Vinkius runs on VS Code VS Code
Vinkius runs on JetBrains JetBrains
Vinkius runs on Vercel Vercel
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