Collecting Annual U.S. Housing Price Index Data from the Census Bureau using Manus AI

In this post we explore how Manus AI can be used to collect official housing price index data from the U.S. Census Bureau. The task focused on the Single-Family Houses Sold series, requesting annual figures to be gathered and organised into a clean, structured dataset suitable for further research.

Prompt

The instruction given to Manus AI was not only about retrieving official data but also about filtering it correctly. The expectation was that, from a broader Census Bureau table, the model should return only the annual values, leaving aside quarterly and regional breakdowns. This made the task both a data collection and a data selection exercise.

Output

To obtain the annual price index for Single-Family Houses Sold, Manus AI followed a stepwise retrieval workflow. It began with a broad scan of candidate pages and, although it parsed a few non-essential sites along the way, it ultimately resolved to the U.S. Census Bureau’s official Construction Price Indexes pages. From there it identified the Historical Series (1996 base year) as the correct source, recognised that the task required historical rather than current series.

Once the relevant dataset had been located, Manus AI proceeded to filter and restructure the material in line with the prompt’s requirements. Instead of reproducing the full table with quarterly and regional subdivisions, it extracted only the annual figures and organised them into a clean format. In this way, the output reflected not merely data collection but also an element of data curation, ensuring that the results corresponded precisely to the specified task. When compared with the official Census Bureau tables, every figure was returned accurately, confirming the model’s reliability in handling both retrieval and selective extraction.

Recommendations

This example illustrates how agent-based retrieval can be applied to the collection of official statistical data. Rather than requiring manual navigation of multiple Census Bureau tables, Manus AI was able to locate the relevant series, apply the necessary filtering, and return an accurate dataset tailored to the task. In this case, the model handled the request effectively, demonstrating that such systems can support structured data gathering.