# Location AAL for Analysis

Identify location exposures that produce the greatest losses

# Overview

The Location AAL service retrieves average annual loss (AAL), coefficient of variation (CV), and standard deviation statistics for modeled location exposures. Using these statistics, underwriters can identify the location exposures in their portfolios that are at the greatest risk.

In this tutorial we will use Location AAL to review the vulnerability of location exposures belonging to a particular portfolio. The locations may be assigned to one or more accounts belonging to the portfolio. You will also need to specify location coverage for each location exposure.

# Step 1: Analyze location exposures

Location AAL statistics let you compare the relative vulnerability of location exposures. The call may retrieve specified analysis generated using either the DLM or HD models.

For an overview of catastrophe modeling using Risk Modeler 2.0 APIs, see Cat Modeling. This tutorial describes steps for preparing exposure data for analysis and analyzing data using a DLM model profile.

# Step 2: Retrieve location AAL analysis results

The Get location AAL by analysis result service returns location AAL statistics including the average annual loss, coefficient of variation, and standard deviation for one or more locations. The analysis ID must be specified in the endpoint path.

```
curl --request GET \
--url 'https://api-euw1.rms.com/riskmodeler/v2/analyses/453/location-aal?perspective=GR&sort=AAL%20DESC&limit=100&offset=0' \
--header 'Accept: application/json' \
--header 'Authorization: XXXXXXXXXX'
```

The service accepts multiple query parameters that may be specified in the service endpoint. Using query parameters, you may filter Location AAL statistics by financial perspective or paginate requests using the limit, max, and sort parameters. In the example, we have appended optional query parameters to the service path to retrieve results for the `GR`

perspective.

The `sort`

query parameter enables you to compare location AAL metrics returned for each location exposure so that you may identify location exposures at the greatest risk. By default, metrics returned by this service are sorted by `aal`

attribute in descending order. You can use the `sort`

query parameter to sort on any column in either ascending or descending order.

# Step 3: Review data

A successful response returns AAL metrics for all of the location exposures belonging to the portfolio. For each location exposure, the response returns the `locationId`

, `locationName`

, `locationNumber`

, `aal`

, `coefficientOfVariation`

, and `standardDeviation`

.

```
{ "totalCount": 1,
"offset": 0,
"limit": 1000,
"locationAALs": [
{
"locationId": 58,
"locationName": "Location_Name",
"locationNumber": "Location_Num",
"aal": 294.25022798552993,
"coefficientOfVariation": 5.652179067641994,
"standardDeviation": 1663.1549792686967
},
{
"locationId": 60,
"locationName": "Location_Name",
"locationNumber": "Location_Num",
"aal": 274.25022798662993,
"coefficientOfVariation": 6.62779067641994,
"standardDeviation": 1234.1549792686967
},
...
]
}
```

`aal`

. The average annual loss (AAL), sometimes called pure premium or burn cost, is the expected value of the modeled loss distribution. It is the loss one would expect to see in a year on average. Since the AAL represents only an average, the actual annual losses will fluctuate around the AAL in any given year. AAL does not include expenses, non-modeled loss, profit, or risk load. One may be interested in AAL for ground-up, gross, net of reinsurance, or other views of risk.`coefficientOfVariation`

. For exceedance probability (EP) analyses, the coefficient of variation (CV) statistic is a measure of the relationship between the pure premium (AAL) and the standard deviation of the annual losses and reflects the volatility in the annual losses. It is calculated by dividing standard deviation of the annual losses by pure premium. CV provides a comparative basis for assessing diversification of risk and compares volatility across analyses, such as between portfolios.`standardDeviation`

. The standard deviation associated with a mean loss value characterizes the secondary uncertainty associated with that mean loss value, that is, the uncertainty in an event loss, given that a certain event has occurred. The event loss table includes correlated and independent standard deviations:The correlated standard deviation for a portfolio assumes that all locations are completely correlated, which implies that if the losses are large for one correlation, they are likely to be large for the other location.The independent standard deviation for a portfolio assumes that all locations are completely independent, which means that knowing the size of the loss at one location does not provide any information about the size of the loss at the other location.Portfolio standard deviation is calculated as a weighted average of these two extreme cases.

Updated 5 months ago