A realisation of an apartment dynamic pricing system

Wang, Jian

doi:10.1057/rpm.2008.11

Cited by 4 publications

(3 citation statements)

References 9 publications

(6 reference statements)

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“…In traditional apartment management, rents are often set with the objective of maximizing occupancy and return on investment. Rents are normally determined by such factors as physical characteristics of a property, its current vacancy rate and competitive position in the market place, along with managers' prior experience (Wang 2008). Sirmans and Benjamin (1991) performed an extensive literature review about the setting of apartment rents.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In modern apartment management, rents are often set with the objective of maximizing total revenue. For example, apartment revenue management (RM) proposes to optimize the rents by optimally balancing demand and supply in the consideration of competitor rents such that total revenue will be maximized (Wang 2008).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Apartment communities belong to the service industry, but they have their own characteristics (Wang 2008). For example, the duration of a tenant is continuous.…”

mentioning

confidence: 99%

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Predicting and measuring customer lifetime values for apartment tenants

Wang¹,

Das²,

Duggasani³

2016

Z Immobilienökonomie

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It is of great benefit for an apartment community to predict the customer lifetime value (CLV) for each tenant. This prediction can be used not only to identify the most profitable residents, but also to make better pricing decisions, especially when optimizing renewal rents for expiring leases. CLV has been studied extensively in many industries such as service and retail. However, to our knowledge, there is no literature specifically addressing the estimation of CLV for apartment tenants. In this study, we propose an approximate approach to predicting the lifetime length and value for apartment tenants as well as their renewal probabilities. The model was estimated and tested based on a real dataset from 68 apartment companies in the US. The resulting prediction accuracy was particularly satisfactory for the tenants who did not renew or only renewed once.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%