> ## Documentation Index
> Fetch the complete documentation index at: https://docs.propaideals.co.uk/llms.txt
> Use this file to discover all available pages before exploring further.

# Strategy scores endpoint

> Machine-learning fit scores per investment strategy (BTL, HMO, flip, BRRR, SA, R2R) for a UK property, plus the best-fit strategy. Required scope investment:read.

# Strategy scores

The strategy scores endpoint returns machine-learning fit scores for a property across each major UK investment strategy — buy-to-let, HMO, flip, BRRR, serviced accommodation, and rent-to-rent — along with an overall score and the best-fit strategy. Use it to route a property to the strategy it suits best before you underwrite it in detail.

**Required scope:** `investment:read`
**Cost:** 2 requests per call

## Get strategy scores for a property

```http theme={null}
GET /api/v1/strategy-scores
```

Returns per-strategy fit scores and the best-fit strategy for a single property, identified by its UUID.

### Authentication

```bash theme={null}
Authorization: Bearer paid_your_key
```

All endpoints accept a `paid_*` API key or a logged-in Clerk session. Anonymous requests are rejected with `401`.

### Query parameters

| Param         | Type          | Default | Description                       |
| ------------- | ------------- | ------- | --------------------------------- |
| `property_id` | string (UUID) | —       | **Required.** The property's UUID |

### Request

```bash theme={null}
curl "https://api.propaideals.co.uk/api/v1/strategy-scores?property_id=5fa1b2c3-d4e5-6f78-9012-3456789abcde" \
  -H "Authorization: Bearer paid_your_key"
```

```python theme={null}
import requests

res = requests.get(
    "https://api.propaideals.co.uk/api/v1/strategy-scores",
    params={"property_id": "5fa1b2c3-d4e5-6f78-9012-3456789abcde"},
    headers={"Authorization": "Bearer paid_your_key"},
)
scores = res.json()["data"]
```

```javascript theme={null}
const params = new URLSearchParams({ property_id: "5fa1b2c3-d4e5-6f78-9012-3456789abcde" });
const res = await fetch(
  `https://api.propaideals.co.uk/api/v1/strategy-scores?${params}`,
  { headers: { Authorization: "Bearer paid_your_key" } },
);
const { data } = await res.json();
```

### Response

```json theme={null}
{
  "data": {
    "property_id": "5fa1b2c3-d4e5-6f78-9012-3456789abcde",
    "scores": {
      "overall": 74,
      "btl": 81,
      "hmo": 68,
      "flip": 55,
      "brrr": 72,
      "sa": 49,
      "r2r": 38
    },
    "best_strategy": "btl",
    "best_strategy_score": 81
  },
  "meta": {
    "usage": {
      "request_cost": 2,
      "monthly_used": 4825,
      "monthly_limit": 100000
    }
  }
}
```

### Response fields

| Field                 | Type    | Description                                        |
| --------------------- | ------- | -------------------------------------------------- |
| `property_id`         | string  | The property's UUID                                |
| `scores.overall`      | integer | Blended fit score across all strategies, `0`–`100` |
| `scores.btl`          | integer | Buy-to-let fit score                               |
| `scores.hmo`          | integer | HMO fit score                                      |
| `scores.flip`         | integer | Flip fit score                                     |
| `scores.brrr`         | integer | Buy, refurbish, refinance, rent fit score          |
| `scores.sa`           | integer | Serviced accommodation fit score                   |
| `scores.r2r`          | integer | Rent-to-rent fit score                             |
| `best_strategy`       | string  | The strategy with the highest fit score            |
| `best_strategy_score` | integer | The score of `best_strategy`                       |

## Common patterns

### Route a property to its best-fit strategy

```python theme={null}
res = requests.get(
    "https://api.propaideals.co.uk/api/v1/strategy-scores",
    params={"property_id": property_id},
    headers={"Authorization": "Bearer paid_your_key"},
).json()["data"]

if res["best_strategy_score"] >= 70:
    print(f"Underwrite as {res['best_strategy'].upper()} (fit {res['best_strategy_score']}/100)")
```