Data-Driven Decisions For Ghana's Real Estate Market

With the growing popularity of YouTube videos showcasing residential real estate developments across Ghana, it is evident that the sector is attracting significant interest and investment. However, this raises an important question: how do developers decide where to build, what to build, and at what price point?

In this article, I explore how developers can increase their return on investment (ROI) by using data to:

• Identify optimal locations for development projects

• Segment customers into distinct groups to guide marketing and the housing design process

Identifying optimal locations for residential development projects

To do this, key data points would include:

• population growth by area

• sales data by area

• housing vacancy rates

The data may for example reveal the following:

Although the population growth is slightly higher in East Legon, Aburi demonstrates stronger market performance, with higher unit sales, greater revenue generation, and a faster sales cycle.

While other factors should be considered, these insights suggest that housing development in Aburi may offer a more attractive investment opportunity. The stronger demand and faster unit turnover could translate into higher returns on investment, improved cash flow, and a lower risk of unsold inventory.

Note: These insights are hypothetical and intended to illustrate the potential value of data. They may not reflect actual market conditions.

Customer segmentation for targeted marketing and housing design

To do this, key data points would include:

• Customer demographics (age, income, occupation)

• Inquiry data (who shows interest vs who buys)

• Budget ranges

• Housing preferences (apartment, 2-bedroom house etc)

The data may for example reveal the following:

On average, 12 individuals aged 22 - 25 make property purchase inquiries each quarter, with an average budget of GHS 70,000. However, only 15% convert into actual buyers, making this segment the lowest in both purchasing power and conversion rate.

In contrast, the 26 - 30 age group is the most commercially attractive segment. With 35 quarterly inquiries, an average budget of GHS 285,000, and a high conversion rate of 53%, this group generates the highest volume of actual buyers and overall expected revenue.

Although buyers aged 30 - 40 have the highest average budget (GHS 300,000), their lower inquiry volume and moderate conversion rate (38%) place them behind the 26 - 30 segment in expected total revenue.

Based on these insights, from an ROI perspective, developers should prioritise marketing strategies toward the 26 - 30 segment, while maintaining a secondary focus on the 30 - 40 segment. Housing units should be tailored to align with each group’s budget and preferences, without compromising on quality.

The 22 - 25 segment, while less immediately profitable, represents a longer-term pipeline of future buyers. Developers can engage this group through targeted educational marketing strategies - such as financing guidance and homeownership literacy - to build trust and prepare them for future purchasing decisions.

Note: These insights are hypothetical and intended to illustrate the potential value of data. They may not reflect actual market conditions.

Conclusion

I believe there might be an opportunity to leverage data analytics to improve residential development in Ghana. To realise these benefits, developers need access to quality data. I encourage players in the sector to:

• Begin consistently tracking their own data

• Explore partnerships with other developers to share and combine data sources over time

To all residential developers in Ghana:

• How do you see data analytics improving decision-making in the sector? What approaches seem feasible, and what challenges or concerns do you anticipate?