margin and profitability

Margin & Profitability: Engineered Through Recruiting Strategy

Most margin problems aren’t solved by cutting costs. They’re solved by recruiting the right mix of agents and building systems that help them succeed.

Brokers Have a Margin Problem

Many brokers are seeking to add "top-producing" agents to grow revenue, but profitability isn’t just about top-line volume. Brokers face rising splits and shrinking margins. We can help with that!

Expert Analysis

Agent mix analysis based on split tiers, production history, and attrition risk

Targeted Recruitment

We develop targeted recruitment strategies aligned with profitability goals.

Internal Strategies

We use our analysis to develope roster modeling tools to forecast margin impact.

Case Study: Mathematical Modeling

Leads To Higher Margins

We used a mathematical model called linear programming to determine the most profitable agent mix

The Challenge

Real estate brokerages often structure commission splits based on agent production. A common challenge brokers face is optimizing their agent mix to strike a balance between revenue and profitability.

Consider ACME Brokerage (Not their real name), which operates with a cap of 60 agents unless it undergoes an expensive expansion. We worked with them about 6 months ago. Their agent commission splits vary based on the past 12 months' production, and the brokerage needs to generate $160 million in sales volume to maintain its market share while retaining a 20% gross margin for profitability.

The question is: What is the ideal agent mix? Assuming agents perform similarly to last year and new recruits average $1.5 million in production, how should ACME structure its roster? Additionally, the model accounts for attrition, with 15% for most agents, except for top producers, who experience 10%.

How We Found the Solution

To solve this, we used Linear Programming (LP) with PuLP in Python. Below are the constraints and optimization formulas used.

Decision Variables

Let:

( X1 ) be the number of high-producing agents $10M avg vol / 90% avg split

( X2 ) be the number of mid-high producing agents $8.5M avg Vol / 80% avg split)

( X3 ) be the number of mid-producing agents $5M avg vol / 70% avg split

( X4 ) be the number of lower-producing agents $2M avg vol / 65% avg split

( X5 ) be the number of new agents $1.5M avg vol / 60% avg split

Objective Function

Total Profit = (X1 × 10 × 0.10 × 0.9) + (X2 × 8.5 × 0.20 × 0.85) + (X3 × 5 × 0.30 × 0.85) + (X4 × 2 × 0.35 × 0.85) + (X5 × 1.5 × 0.40 × 0.85)

The Outcome

Using Linear Programming, we knew with mathematical certainty what the ideal mix of agents needed to be in order to acheive revenue and margin target while factoring in attrition.

Optimized Agent Mix

X1 (Top Producers): 8 agents
X2 (High-Mid Tier): 14 agents
X3 (Mid-Tier): 15 agents
X4 (Lower-Tier): 14 agents
X5 (New Agents): 9 agents

Our Action Plan

Acme's agent mix at the time did not resemble this structure. Like many brokerages, too much of it's revenue was generated by only a few high-production agents. These agents we on very high split plans and received other generous compensation. The broker was constantly terrified that one of these agents would leave the firm and take a huge chunck of revenue with them.

We helped them develop a plan to recruit workhorse agents. We kept the producers happy while slowly rebuilding the agent mix of the office. The result was a healthier office and a happier broker who now didn;t have all of her eggs in just a few baskets

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