NCREIF Custom Query MCP. Analyze property indices and real estate performance data.

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.