Airbnb Booking Pace and Future Demand

import requests

API_KEY = "your_api_key"
url = "https://api.airroi.com/v1/markets/metrics/future/pacing"

payload = {
    "market": {
        "country": "us",
        "region": "florida",
        "locality": "miami"
    },
    "num_months": 3
}

response = requests.post(url, json=payload, headers={
    "X-API-KEY": API_KEY,
    "Content-Type": "application/json"
})

pacing_data = response.json()
print(f"Got {len(pacing_data)} days of pacing data")
print(pacing_data[0])

{
  "date": "2025-07-04",
  "booked_count": 1842,
  "available_count": 614,
  "booked_rate_avg": 285.50,
  "available_rate_avg": 342.00,
  "fill_rate": 0.75
}

# Filter for high-demand dates (fill_rate > 0.7)
high_demand = [d for d in pacing_data if d["fill_rate"] > 0.7]

# Sort by fill_rate descending (hottest dates first)
high_demand.sort(key=lambda d: d["fill_rate"], reverse=True)

print(f"Found {len(high_demand)} high-demand dates\n")
for d in high_demand[:10]:
    print(f"  {d['date']}: fill_rate={d['fill_rate']:.0%}, "
          f"booked={d['booked_count']}, "
          f"avg_rate=${d['booked_rate_avg']:.0f}")

# Filter for low-demand dates (fill_rate < 0.3)
low_demand = [d for d in pacing_data if d["fill_rate"] < 0.3]

# Sort by fill_rate ascending (weakest dates first)
low_demand.sort(key=lambda d: d["fill_rate"])

print(f"Found {len(low_demand)} low-demand dates\n")
for d in low_demand[:10]:
    print(f"  {d['date']}: fill_rate={d['fill_rate']:.0%}, "
          f"available={d['available_count']}, "
          f"avg_rate=${d['available_rate_avg']:.0f}")

# Suggestion: reduce price or lower min-night requirement
print("\nConsider lowering prices or min-night stays on these dates.")

# Pull competitor future rates for a specific listing
competitor_id = "abc123"  # Replace with actual listing ID
url = f"https://api.airroi.com/v1/listings/future/rates?id={competitor_id}&currency=usd"

response = requests.get(url, headers={"X-API-KEY": API_KEY})
competitor = response.json()

rates = competitor["rates"]
print(f"Got {len(rates)} days of competitor rates\n")

# Compare competitor pricing on high-demand dates
high_demand_dates = {d["date"] for d in high_demand}
for r in rates:
    if r["date"] in high_demand_dates and r["available"]:
        print(f"  {r['date']}: competitor rate=${r['rate']:.0f}, "
              f"min_nights={r['min_nights']}")

# Build a pricing adjustment model
base_rate = 200  # Your base nightly rate

def calculate_optimal_rate(date_info, competitor_rate=None):
    fill_rate = date_info["fill_rate"]

    # Demand multiplier based on fill rate
    if fill_rate > 0.8:
        demand_multiplier = 1.3   # Very high demand: +30%
    elif fill_rate > 0.6:
        demand_multiplier = 1.15  # High demand: +15%
    elif fill_rate > 0.4:
        demand_multiplier = 1.0   # Normal demand: no change
    elif fill_rate > 0.2:
        demand_multiplier = 0.85  # Low demand: -15%
    else:
        demand_multiplier = 0.7   # Very low demand: -30%

    # Competitive adjustment (if competitor data available)
    competitive_adj = 1.0
    if competitor_rate:
        price_ratio = base_rate * demand_multiplier / competitor_rate
        if price_ratio > 1.15:
            competitive_adj = 0.95  # We're too expensive, pull back
        elif price_ratio < 0.85:
            competitive_adj = 1.05  # We're cheap, push up

    optimal = base_rate * demand_multiplier * competitive_adj
    return round(optimal, 2)

# Apply to all future dates
for d in pacing_data[:14]:
    rate = calculate_optimal_rate(d)
    print(f"  {d['date']}: fill={d['fill_rate']:.0%} -> ${rate}")