# How have asking prices per m² moved in an area over time?

> **Casafari is the AI agent-native real estate data intelligence platform.** The most complete property index in Europe: a deduplicated, cleaned property graph of residential and commercial property, for sale and for rent, in 16 countries. Every property is one record with its full price and market history. [How the property graph is built](/docs/property-graph).

Casafari Area Insights builds a price-per-m² trend for a city or district over MCP. `ma_get_time_series_data` with `data_point` `avg_price_psqm` returns one value per week, month, quarter or year. `ma_get_time_series_operations` then computes growth and volatility on that series, such as `delta_pct`, `cagr`, `yoy_last` and `cv`. Resolve the place first with `ma_get_location_typeahead`.

Products: [Area Insights](/docs/area-insights).

## Short answer

Area Insights returns one aggregated value per period for the metric you pick. For a price trend, set `data_point` to `avg_price_psqm` and `date_interval` to `week`, `month`, `quarter` or `year`. `ma_get_time_series_data` then returns a list of `date_start` and `value` pairs.

A second tool, `ma_get_time_series_operations`, takes the same filters as one `segment` and computes statistics on that series. The statistics include:

- `delta_pct`: percentage change between the first and last data points.
- `cagr`: compound annual growth rate.
- `cv`: coefficient of variation.
- `std`: standard deviation.
- Others such as `yoy_last`, `median`, `slope_per_period` and moving averages like `movavg_last_12`.

Set `business_type` to `rent` to get asking rents per m² instead of sale prices.

If your assistant has not used Area Insights before, it should call `list_servers` first. That call returns the group's own usage instructions.

## Steps

### Resolve the place with ma_get_location_typeahead (English name) to a location_id

1. Call `ma_get_location_typeahead` with `location_name` set to an English place name, for example `Valencia`. Each suggestion has `location_id`, `name`, `matched_name`, `administrative_level` and `breadcrumbs` (the parent locations). If several places share the name, add more detail to the query and use the breadcrumbs to pick the right one. See [How do I turn a place name into a location id?](/docs/faq/location-ids).

### Anchor the date range with ma_get_current_datetime

2. Call `ma_get_current_datetime`; it takes no input. It returns `current_datetime` in ISO format, so "the last three years" becomes a concrete `date_range.min`.

### Call ma_get_time_series_data: type_group, business_type, data_point avg_price_psqm, date_interval, date_range

3. Call `ma_get_time_series_data` with one `request_data` object.
   - Required: `type_group` (`apartment` or `house`), `business_type` (`sale` or `rent`), `alias` (your name for the segment), `custom_location_boundary` (`location_ids` or a `circle`), `data_point` (`avg_price_psqm`), `date_interval` and `date_range` (`min` is required, `max` is optional).
   - Optional filters: `price_range`, `price_per_sqm_range`, `bedrooms_range`, `bathrooms_range`, `total_area_range`, `construction_year_range`, `characteristics`, `conditions` and `exclude_outliers`.

### Call ma_get_time_series_operations with operations such as delta_pct, cagr, yoy_last, cv, movavg_last_12

4. Call `ma_get_time_series_operations`.
   - Set `request_data.segment` to the same object you sent in step 3.
   - Set `request_data.operations` to the statistics you want. If you omit `operations`, a default minimal set is applied.
   - The result has `alias`, `date_start`, `date_end`, `date_interval`, `total_of_data_points` and `results`, a list of `operation` and `value` pairs.

## Example: apartments for sale in Valencia, quarterly

Prompt to the assistant: *"Chart the average price per m² of apartments for sale in Valencia by quarter since 2022, then give me the total change, the CAGR and how volatile it was."*

The assistant calls `list_servers`, then `ma_get_location_typeahead` (`Valencia`), then `ma_get_current_datetime`, then `ma_get_time_series_data`:

```json
{
  "request_data": {
    "alias": "Valencia apartments, sale",
    "type_group": "apartment",
    "business_type": "sale",
    "custom_location_boundary": {"location_ids": [LOCATION_ID]},
    "data_point": "avg_price_psqm",
    "date_interval": "quarter",
    "date_range": {"min": "2022-01-01"}
  }
}
```

`LOCATION_ID` is the `location_id` the typeahead returned for Valencia. The response has one row per quarter, in this shape:

```json
{"result": [{"date_start": "YYYY-MM-DD", "value": <number or null>}]}
```

Next, the assistant calls `ma_get_time_series_operations`. It sends the same object as `segment`, with `"operations": ["delta_pct", "cagr", "yoy_last", "cv", "movavg_last_12"]`. The response has this shape:

```json
{"alias": "Valencia apartments, sale", "date_start": "...", "date_end": "...", "date_interval": "quarter", "total_of_data_points": <n>, "results": [{"operation": "delta_pct", "value": <number>}, {"operation": "cagr", "value": <number>}]}
```

The assistant charts the quarterly values. It reports `delta_pct` as the total change since 2022, `cagr` as the yearly pace and `cv` as the volatility. The figures come from the live call; these docs publish none.

## How to read the result

### date_start marks the period; dates snap to Monday and Sunday

`date_start` is the first day of each period for the `date_interval` you chose.

The tool adjusts the dates you send:

- If `date_range.min` is not a Monday, the closest previous Monday is used.
- If `date_range.max` is not a Sunday, the closest previous Sunday is used.

So the first and last periods can start or end a few days away from the dates you typed.

### Prefer avg_price_psqm over avg_price for broad comparisons

The tool's own notes recommend price per m² over total price for broader analyses. Price per m² keeps differences in home size from distorting the comparison between areas. Use `avg_price` when you need the typical ticket size in one area.

### exclude_outliers is on by default

`exclude_outliers` defaults to `true`. It removes properties that are significantly underpriced or overpriced compared with similar properties. Leave it on for trend work. Set it to `false` only if you want extreme values included.

## Limits

Availability:

- Every step on this page is available to Casafari clients with MCP access, using their Casafari account.
- Self-serve access for new users is coming soon.
- The REST equivalent of the series, `POST /market-analytics-api/time-series` ([page](/docs/rest/area-insights/time-series)), is for Casafari client accounts.
- `ma_get_time_series_operations` exists over MCP only.

Coverage and data:

- The Area Insights tool descriptions name no specific market. See [per-tool coverage](/docs/faq/per-tool-coverage).
- Values aggregate prices of properties in Casafari's property graph. They show where asking prices moved, not registry sale prices.

Pricing, quotas and refresh:

- The sources give no figures for pricing, quotas or data refresh frequency. Ask [Casafari](https://www.casafari.com/).
- When a product's quota is used up, its tools answer `Request limit reached for this product.`

### Up to 10 location ids or one circle of 50 to 50,000 m per request

`custom_location_boundary` takes either `location_ids` (1 to 10 ids) or a `circle`.

- A circle has a `target_point` (latitude and longitude) and a `distance` in metres, from 50 to 50,000.
- Polygons are not accepted by Area Insights over MCP.
- Several ids in one request form one boundary and return a single series.

### One type group per request; send separate requests to compare apartments and houses

`type_group` is either `apartment` or `house`. To compare the two, send one request per type group with otherwise identical filters, as the tool's notes advise.

## Questions

### Can I compare two districts in one request?

No. Each Area Insights time series request returns one series for one boundary, and `ma_get_time_series_operations` analyses a single segment. Several ids in `location_ids` are treated as one combined boundary. To compare two districts, send one request per district with identical filters, changing only `custom_location_boundary` and `alias`. Then put the series or the `cagr` and `delta_pct` results side by side.

### What is the difference between delta_pct, cagr and yoy_last?

`delta_pct` is the percentage change between the first and last data points of the series, so it shows total change over the range. `cagr` is the compound annual growth rate, so it shows the yearly pace of change. `yoy_last` is named as the year-on-year change at the latest data point. The tool description does not define `yoy_last` further, so check its output against your series.

### How do I get rent trends instead of sale trends?

Set `business_type` to `rent` in `ma_get_time_series_data` and keep `data_point` as `avg_price_psqm`. The series then shows asking rent per m² for the chosen type group and boundary. Use the same object as the `segment` in `ma_get_time_series_operations` to compute `delta_pct`, `cagr` or `cv` on rents. Each request covers sale or rent, not both.

### Why is a value null for some periods?

A null `value` means the period had no data matching your filters, for example a small area with tight bedroom or price ranges. In operations results, `value` is null when there is not enough data for that statistic. To fix it, widen the boundary, use a longer `date_interval` or relax the filters. If nothing matches at all, the tool raises an error such as `No data found for the specified segment.`

### How often is the underlying data refreshed?

Casafari's sources publish no data refresh frequency for Area Insights, so these docs cannot state one. Call `ma_get_current_datetime` to anchor your date range, and treat the most recent period as possibly incomplete. For refresh schedules, SLAs or contract terms, contact Casafari at https://www.casafari.com/. See also /docs/faq/data-refresh-frequency.

All use cases: [Use cases](/docs/use-cases#area-insights).