1. Northern Illinois Property Group (NIPG) manages multiple apartment properties throughout their region. NIPG operates an online service that enables potential customers to enter a neighborhood or suburban location, desired characteristics (e.g., bedrooms, space), and lease period to view images of any available properties and their prices. Rego Reredef, NIPG's COO, has been reviewing the reports from this system and noticed something that he believes might represent an opportunity for NIPG. Table 1 below presents part of that report for two-bedroom units in a given neighborhood (labeled ``A"). The table shows the number of requests in the past 6 months entered on the site for quotes and availability in that category and neighborhood, the number of times the system showed no availability, the percent this represents of the total requests, the offers made through the site, the actual signed leases resulting from the offers, the conversion rate (leases divided by offers), and the average monthly rent for each lease period. Rego's interest was piqued by the higher unavailability (or ``denial") rate for 3-month leases and the higher conversion rate for these leases. Since the short leases have a higher monthly rent, Rego thought there might be an opportunity to shift some of the longer-term lease demand to shorter leases to lift revenues.

Table 1. Two-bedroom units in Neighborhood A.

For more detail, Rego considered the monthly two-bedroom leases in Neighborhood A (which has a total of 545 two-bedroom units) over the past several years up to the May 2022. These data appear in Table 2 below where the "demand" estimates are implied by multiplying the requests in each month by the overall conversion rate calculated in Table 1. Rego showed this to NIPG's MBA intern, Ava Parihs. Ava immediately recognized an opportunity to use revenue management to determine the potential of improved lease controls on NIPG's properties.

Table 2. Historical implied demand, lease starts and lease ends in Neighborhood A.

• a) What type of model can Ava use to determine potential increases in revenues for the Neighborhood A properties? What assumptions does she need to make for this model to be valid?