We can generate a rough distribution estimate of this behavior pretty quickly.. If you start at an EVEN distribution of renewal dates right now (it's unlikely

We can generate a rough distribution estimate of this behavior pretty quickly.. If you start at an EVEN distribution of renewal dates right now (it's unlikely but for argument): After 1 year: 33% renewing in the same 2-month period After 2 years: 50% After 3 years: 66% After 4 years: 83% After 5 years: 99% The actual distribution is going to be less than that 99% due to a lot of other factors. Such as only 80% of the leases following this same practice, and move-in/move-out pressure in an area. But the impact of this behavior will still be seen. This practice is price fixing. quick code: import pandas as pd import numpy as np # Initial distribution: 1000 leases evenly distributed across 12 months initial_leases = 1000 // 12 months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"] # Initialize a DataFrame to track the distribution over 6 years distribution = pd.DataFrame(0, index=np.arange(6), columns=months) distribution.loc[0] = initial_leases # Set initial distribution # Function to update the distribution for a given year def update_distribution(year): for month in months: if month in ["May", "Jun"]: # 12-month renewal distribution.loc[year, month] = distribution.loc[year - 1, month] else: # 10-month renewal # Calculate new expiration month (10 months later) new_month_index = (months.index(month) + 10) % 12 new_month = months[new_month_index] distribution.loc[year, new_month] += distribution.loc[year - 1, month] # Update the distribution for each year for year in range(1, 6): update_distribution(year) # Displaying the distribution distribution.T # Transpose for a better view