The matchmaking secrets for high-performing teams

By Lisa Kennedy

Boosting productivity by building high-performing teams is a key survival strategy for companies in tough economic times – and few sectors have faced tougher conditions over recent years than the real estate industry. But how exactly do you mix and match staff with different characteristics and performance levels to create more effective teams, especially when managers have to assign jobs on the spot or have no effective way of assessing the qualities employees possess that are critical to teamwork, such as soft skills?

A new study has introduced a quantitative approach for making the best matches between real estate agents with widely differing latent characteristics and performance levels to build teams with the greatest likelihood of completing property deals.

EntitledHeterogeneous complementarity and team design: The case of real estate agents, the study was conducted by Mandy Hu Mantian, Associate Professor of the Department of Marketing at the Chinese University of Hong Kong (CUHK) Business School, in collaboration with Yan Xu of Virginia Polytechnic Institute and State University, Chu Junhong of the University of Hong Kong, and Andrew Ching of Johns Hopkins University.

It found that real estate firms could boost the number of deals they successfully conclude on sales and rentals if they restructure the agent teams following the quantitative approach. As real estate agencies typically earn a commission based on a percentage of the property’s sale price, even a small increase in successful deals can translate into significant revenue.

“Given that the average value of a property in mainland China is 2.16 million Chinese yuan (US$324,000), this translates to a financially significant improvement,” says Professor Hu, underscoring the practical implications of research findings in team restructuring to boost sales. “Furthermore, this restructuring strategy does not incur any additional hiring costs and, therefore, the expected output gain translates to profits directly, which is especially valuable for firms facing staff shortages or hiring freezes.”

A large evidence base

The study applied the proposed quantitative model to the context of 484 sales agents at Lianjia, one of mainland China’s largest real estate brokerage companies, from 2011 to 2017. The agents all worked across 17 stores in the Beiyuan subdistrict, a business zone in Beijing, and their income came primarily from commissions on successful deal closures. During the period, they worked on 56,146 properties and successfully closed 17,842 deals.

“Store managers make team assignment decisions when a property owner first visits a Lianjia store to list the property for sale or to rent,” says Professor Hu. “The first agent to interact with the owner automatically joins the property’s sales team. In addition, the manager might assign extra agents who are available at that moment to the property’s team. The management also prioritises maintaining workload fairness among all agents.”

“The findings suggest that we may want to pair up agents who show moderate differences in their working ability.”Professor Mandy Hu Mantian

The researchers were given the complete team assignment history of every agent and detailed characteristics of all the properties assigned to them. They analysed the performance and collaboration data of all teams with one or two members, which jointly accounted for 94.4 per cent of properties handled over the period. They found that by dividing the agents into six types based on their solo performance and teamwork effectiveness in completing deals—where agents with the lowest success rates are labelled as type 1 and the highest performers as type 6—it is not necessarily the case that pairing a low-performing agent with a high-performing one in two-agent teams would increase the success rate.

Next, the researchers developed a quantitative model with a cutting-edge technique to figure out the latent compatibility of agents to work with each other.  The model also incorporates observed data on agents’ gender, home province, education level and age, which was used for further team performance analysis.

The best team players

The study found that the agents with average solo performances (latent type 4) are the best team players. For all agent types, except the best solo performer (type 6), the probability of closing a deal is greatest when paired with type 4. Pairings involving another type 4 agent have the highest chance of success, followed by matches with a type 5 and type 3 agent, respectively.

Type 6 agents always performed best when working alone. Teams that combined a low-performing agent of type 1 or 2 with a high performer from type 5 or 6 were less productive than when type 5 and 6 agents worked on their own.

“Teaming up agents of the same type generally does not yield superior performance compared to pairing two agents of different types, with the exception of type 4,” says Professor Hu. “On the other hand, pairing up agents of very different types also hurts performance. These findings suggest that we may want to pair up agents who show moderate differences in their working ability.”

The researchers found that working in a multiple-agent team often requires coordination and may be more suitable for agents with certain work styles. Type 6 may be very independent but not very adept at coordinating with others, while type 4 is better suited to a collaborative work environment. Restructuring all teams within a company using the above formula is found to increase the number of successful deals struck by agents by 26.6 per cent.

Gender and education also matter

Taking into account demographic factors of gender and education level, the researchers found that the likelihood of closing a deal would be higher if teams included at least one female member and the members were older or had more education. However, the age or education gap among members should not be too large.

Changing the gender mix of teams to the optimum level would raise total expected output by 2.3 per cent, while assigning all agents with the same education level increased overall performance by just one per cent. Compared to the 26.6 per cent increase following the proposed method, the common practice of paring agents based on observed demographic information may not achieve optimal results.

Professor Hu says the main results show how managers can utilise the proposed model and historical teamwork performance data to reassign existing employees in a more effective way that enhances overall performance, which will lead to better results than using demographic information.

“When new hires join the company, managers can consider using observed demographic information to create teams with gender diversity or assign agents with similar educational backgrounds or ages to the same team,” says Professor Hu. “once the new hires have been working at the company for enough time to assess their sales performance, managers can then gauge which type they belong to. With this information, team assignments can be further optimised.”