Guide Your Next Property Investment in Africa with Data Science
The goal here is to identify the most and least profitable states/regions in select African countries (Ghana, Kenya and Nigeria) so as to guide your property investment decisions with data science techniques.
The ideal neighborhood for investing in property would have
- high rental price
- low sale price. So the rent would be higher than the monthly mortgage payment to make a profit.
To achieve this, we need a way to compare the rental prices and sale prices. For each neighborhood, we calculated two things:
- unit price per square foot for rental prices, and unit price per square foot for sale prices
- averaged monthly mortgage under the assumption of 20% down payment, 19.67% annual interest rate (average across selected countries), and 30 years mortgage period.
Before we begin to compare rental price and sale price, it is important to have a first glance at the dataset.
I noticed that some neighborhoods have very few to none listing. The low number of listings could introduce instability and lead to false results. Therefore, I removed neighborhoods with equal or less than 5 listings to ensure the quality of the data.
Here are some examples of the neighborhoods with more than 3 listings. For instance, one square foot in Abuja, Nigeria costs $4.53 to rent and $344 to buy, and one square foot in Greater Accra Region, Ghana costs $1.68 to rent and $165 to buy. In Nairobi, Kenya — $2.47 to rent and $203 to buy.
Assessing Profitability
To measure how profitable a region is, we computed the ratio of rent unit price over sale unit price. What this means is that: the higher unit rent to unit sale ratio, the less profitable the region is. See plots below:
From our profitability assessment, the plots below show the most and least profitable regions for property investment of all the select countries.
In closing
The analysis may not be perfect in that there’s no standard exchange rate across these countries. Hence there may be variation in pricing (Nigeria itself has at least four different dollar FX rates). Also, the data source (web scraped from House Jumia) may not contain all listed properties, hence a bit of skewness.
However, this does not forfeit the techniques applied to this analysis.
I’d love your feedback on the analysis. Please leave a note or send a tweet @elesinOlalekan
We hope you find this useful in property investment decisions.
Thanks to Wendy Yu for letting me reproduce her analysis