Consistency is Key When Using Multiple Fee Managers

As multifamily investors and operators, we are constantly investigating pricing strategies to find the best and most efficient ways to achieve consistent returns. But what about owners who use multiple fee managers in different markets? The folks at New York-based Abacus Capital Group addressed their need for a pricing strategy across operating platforms by deploying a revenue management system.

“We believe it is most efficient to find local management with local expertise, and we don’t expect a single fee manager to understand every market either,” says Kyle Ellis, one of the managing partners of Abacus. “We’re perfectly comfortable working with several ‘best-in-class’ management firms across our portfolio.”

The first major issue is when multiple fee managers are used they all use different data and their own internal metrics and strategies.

The second problem with using multiple fee managers is inconsistent reporting. “When there are different types of reports coming in, on different schedules, it can be quite difficult to keep up with what’s really going on out there,” Ellis says.   “What you want is a single, consistent, and methodical approach to pricing and an accurate and timely reporting solution that’s used by all managers.”

Abacus adopted a standardized pricing strategy by deploying RealPage’s Yieldstar revenue managemnent system to help eliminate many of the inconsistencies they were experiencing.

Revenue management systems for multifamily generate pricing based on several factors, including current and expected vacancies, projected demand, recent rental rates, amenities, lease term, move-in date and existing market dynamics. Optimized pricing can be achieved daily, which in turn will help operators generate more income and improved financial results.

Read more about the Abacus case and other Yieldstar case studies on optimized pricing here:

Multiple Fee Managers, One Revenue Management Solution




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Lincoln Finds 4.3% Lift with RevMan in Challenging Rental Markets

Automated Lease-Rent Pricing Solution Takes Guess Work, Emotion Out of Price Setting

Atlanta, GA (PRWEB) November 3, 2009 — Every company is addressing the current market challenges differently; some more aggressively and successfully than others. In late 2008, Lincoln Property Company decided to test a new price setting process to see if it could improve revenue. Lincoln’s executives designed a scientific test of the newest multifamily housing revenue management technology and used it at eight of their communities in separate markets. To ensure an objective evaluation, they paired test properties with similar communities in the same markets that continued to set prices with their customary process. The results proved a definite increase in lease rents at the automated properties – in spite of the economy.

scott_wilder1“Seven of eight properties using automated rate setting had better results than our control group setting rates manually,” said Scott Wilder, senior VP, property management for the Lincoln Property Company residential division (shown at left). “Our perception entering the test was that we would activate the ‘black box’ and it would do the thinking. We were encouraged how engaged our team became; by using LRO and contributing to our weekly pricing calls, they became more focused on rate setting and the factors that drive revenue.”

Lincoln Property Company has a corporate culture of diligent pricing analysis and rate setting. “We are good at what we do but wanted to evaluate automated multifamily revenue management software tools and test the one we thought would be the best fit,” Wilder said. The company selected the LRO system, from The Rainmaker Group, which is widely used in the multifamily industry.

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Maximizing Revenue Management

Using revenue-management programs to optimize rental rates is not a new concept, but perhaps not enough attention has been paid to the experts who make it work in the real world.

The November issue of UNITS magazine will feature an article profiling the Laramar Group and how it uses a pricing manager whose daily duties are entirely devoted to managing Laramar’s revenue-management software. By using a pricing manager to incorporate feedback between Laramar’s property managers into the revenue-management program, they are able to maximize the value of their revenue management system.

According to the article, Laramar’s pricing manager includes the community managers on pricing decisions on a weekly basis, confirming the accuracy of the program’s generated data. This overrides any aspects of the program that the property managers think are out of line with competitors’ rates or not appropriate for the current market place. When they decide to override the program based on observations of the property managers, the appropriate pricing adjustments can be made within 30 minutes.

For the entire article, check out the November issue of UNITS magazine arriving to National Apartment Association members and UNITS subscribers in mid-November.

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Technical White Paper Available – Implementing an Apartment Dynamic Pricing System

Is revenue management just a “black box?”  Have the technical challenges been solved in creating a software that can optimize the rent yield in apartment communities?  What’s the math behind revenue management?  A new technical white paper answers these questions and many more.

Dr. Jian Wang

Dr. Jian Wang

In a recently published white paper, “The Implementation of an Apartment Dynamic Pricing System,” Dr. Jian Wang, vice president of research and development for The Rainmaker Group, describes in great detail the implementation of an apartment dynamic pricing system with particular emphasis on setting optimal rental rates for new leases. The system he describes “has been helping several leading apartment operators offer prospective tenants a menu of rent options for the last six years. It sets the optimal rents everyday, which are presented in the form of unit type, move-in week and lease term.

Click here for entire white paper


Apartment operators struggle with a myriad of issues, one of the most important is setting rental rates for new and renewal leases. Traditionally, rents are typically set with the goal of achieving market share, maintaining occupancy and remaining profitable. Rents are determined by various factors including market condition, competition, condition of property, and vacancy rates. Management experience also plays a role in determining rental rates.

Revenue Management (RM) has emerged as another way to determine lease rates for apartment operators. This methodology uses data-driven pricing to find the optimal price for individual apartments based on current and forecasted market conditions.  Apartment Revenue Management Systems (RMS) is a rapidly growing trend for setting rental rates in the apartment industry. Once limited to the airline and hotel industries, RMS has seen significant growth in the apartment industry since multifamily-specific software hit the market several years ago.

The article includes a study of the characteristics of apartment rental firms compared to hotels from a revenue management perspective. The characteristics that apartments and hotels share:

  • Both are perishable products (they are worthless until they are occupied again)
  • Both have constrained supply (there’s only so much to go around)
  • Both are effected by advance consumption decisions (customers reserve product before using)
  • Both have censored demand observations due to product availability and/or pricing constraints

However, the apartment industry does distinguish itself with the following characteristics:

  • Longer lengths of stay
  • Fewer transactions
  • No repeat customers
  • More renewals
  • Riskier decisions
  • No group booking
  • No walk-ins
  • Concessions

Dr. Wang outlines his expert view on the design of an optimal multifamily revenue management system with modules including:

  • Data Aggregator – links the property management systems and the RMS
  • Statistics Operator – estimates a number of business statistics based on the aggregated historical data
  • Demand Forecaster – predicts the remaining unconstrained demand for a finite planning horizon, which will be fed into the Rent Optimizer module
  • Supply Forecaster – predicts the number of units available for lease for a finite horizon of future weeks
  • Reference Rent Calculator – estimates reference rates
  • Rent Optimizer – calculates optimized rents from which the optimal rental rates are derived in the Rent Recommender module
  • Rent Recommender – module that recommends optimal rents by disaggregating optimized rents

Dr. Wang has more than 16 years of experience in mathematical modeling, system architecture, and implementation in engineering and software vendor industries. He’s a demonstrated leader in operations research and is published in several top journals.

Click here for entire white paper


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