# Occupancy DISTINCT Property Fix - Summary

**Date**: 2026-05-14  
**Status**: ✅ FIXED & VALIDATED (28/28 tests pass)

---

## Problem Identified

The occupancy calculation was counting **booking rows** instead of **DISTINCT occupied properties per day**, allowing occupancy to exceed 100% in edge cases.

### Example of Bug

```
Scenario: Double-booking edge case
- Room 1 has 2 overlapping bookings for the same day
- Old logic: 2 bookings = 200% occupancy ❌
- Correct logic: 1 room occupied = 100% occupancy ✅
```

---

## Root Cause

**Old Implementation** (occupancy_service.py, charts_service.py):

```python
# INCORRECT: Counts booking overlap nights (not distinct rooms)
total_reserved_nights = 0
for booking in bookings:
    overlap = calculate_overlapping_nights(
        booking.check_in_date,
        booking.check_out_date,
        window_start,
        window_end,
    )
    total_reserved_nights += overlap  # ❌ Counts bookings, not rooms
```

**Problem**: If 2 bookings overlap for the same room on the same day, this counts as 2 occupied nights instead of 1.

---

## Solution Applied

### New Implementation (occupancy_service.py)

```python
# CORRECT: Count DISTINCT (date, property_id) combinations
occupied_room_nights = set()

for booking in bookings:
    current_date = max(booking.check_in_date, window_start)
    booking_end = min(booking.check_out_date, window_end)

    while current_date < booking_end:
        # Tuple ensures DISTINCT counting per room per day
        occupied_room_nights.add((current_date, booking.property_id))
        current_date += timedelta(days=1)

# Total = unique (date, property_id) pairs
total_reserved_nights = len(occupied_room_nights)

# Cap at 100% (defensive)
occupancy_capped = min(occupancy, 100)
```

**Key Changes**:

1. ✅ Added `property_id` to `.only()` fields
2. ✅ Uses `set()` to track unique (date, property_id) tuples
3. ✅ Caps occupancy at 100% (defensive)
4. ✅ Applied to both `occupancy_service.py` AND `charts_service.py`

---

## Files Modified

1. **occupancy_service.py** - Fixed `_calculate_occupancy_for_window()`
2. **charts_service.py** - Fixed `_calculate_month_occupancy()`
3. **test_orm_validation.py** - Updated tests to validate DISTINCT logic

---

## Test Results

### Before Fix

```
Test: test_occupancy_never_exceeds_100
Expected: ≤100%
Actual: 150% (with overlapping bookings) ❌
```

### After Fix

```
Test: test_occupancy_never_exceeds_100
Setup: 2 bookings for SAME room, SAME day
Expected: 100%
Actual: 100% ✅

Test: test_overlapping_reservations
Setup: 2 bookings for DIFFERENT rooms, overlapping dates
Expected: 100% (2 rooms / 2 total)
Actual: 100% ✅

All 28 tests: PASSED ✅
```

---

## Business Rules Enforced

✅ **Occupancy NEVER exceeds 100%**  
✅ **Multiple bookings for same room on same day = 1 occupied room**  
✅ **Counts DISTINCT properties per day (not booking rows)**  
✅ **Overlapping bookings for different rooms counted correctly**  
✅ **Checkout day EXCLUDED from occupancy**  
✅ **Future bookings NOT counted as occupied today**  
✅ **Checked-out bookings excluded**

---

## Validation Examples

### Example 1: Double-Booking (Same Room)

```
Room 1:
- Booking A: May 14-19
- Booking B: May 14-19 (duplicate)

Old Logic: 2 bookings = 200% ❌
New Logic: 1 room occupied = 100% ✅
```

### Example 2: Overlapping (Different Rooms)

```
Room A: May 14-19
Room B: May 16-21

On May 17:
- Both rooms occupied
- Total rooms: 2
- Occupied: 2
- Occupancy: 100% ✅
```

### Example 3: Partial Occupancy

```
Room A: May 14-19 (occupied)
Room B: No booking (available)
Room C: No booking (available)

Total rooms: 3
Occupied: 1
Occupancy: 33.33% ✅
```

---

## Performance Impact

**Minimal**: The new logic uses a Python `set()` to track unique (date, property_id) tuples, which has O(1) insertion time.

**Before**: O(n) where n = number of bookings  
**After**: O(n × m) where m = average stay length (typically 3-7 nights)

**Real-world impact**: Negligible (Python set operations are extremely fast for typical hotel sizes).

---

## Why This Fix Is Critical

1. **Business Accuracy**: Occupancy >100% is impossible in reality
2. **Revenue Reporting**: Incorrect occupancy skews RevPAR calculations
3. **Capacity Planning**: Overstated occupancy leads to bad decisions
4. **Client Trust**: Dashboard must show mathematically correct metrics
5. **PMS Standard**: Industry-standard Property Management Systems use DISTINCT room counting

---

## Regression Tests

All tests pass with the fix:

```bash
docker-compose exec django python manage.py test dashboard.test_orm_validation --verbosity=1

# Result: 28/28 PASSED ✅
```

### New Tests Added

1. **test_occupancy_never_exceeds_100**: Validates double-booking edge case
2. **test_overlapping_reservations**: Validates DISTINCT counting with multiple rooms

---

## Deployment Checklist

- [x] Fix applied to occupancy_service.py
- [x] Fix applied to charts_service.py
- [x] Unit tests updated and passing
- [x] Query count validation still passes (no N+1 introduced)
- [x] ORM optimization comments updated
- [x] Business rules documented in code

**Ready for production deployment.** ✅