Kenya, Uganda, Tanzania
Read the numbers.Before youmake the offer.
Cap rate, a 10-year IRR, vacation-rental occupancy, and a due-diligence check for any listing or neighborhood across — countries and — neighborhoods in Africa.
The data behind RehAnalytics
- Data points
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- Machine-learning models on price, rent, and risk
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- Markets with neighbourhood coverage
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Six ways to use RehAnalytics
How it works
- 01Name the placeA neighbourhood, a project, or a listing URL.
- 02Pick the readingFull return, vacation rental, due diligence, or fraud.
- 03Read it, or send itOn screen, by email, or as a PDF.
Some feature neighborhoods
| Neighbourhood | City | Data points |
|---|---|---|
| The neighbourhood list is unavailable right now. | ||
What is an AVM?
An automated valuation model is a computer estimate of what a home would sell for, what it would rent for, or what a night in it would earn. It is the same kind of tool the big property sites use when they put a value on a house that has not been visited by a valuer. It is not an appraisal, and it is not a promise about the next sale.
How it is made
A separate model is trained for each kind of home, in each place. It studies homes that were actually sold or let, and learns which details move the price: the neighbourhood, the size, the type of home. The asking price on a listing is left out. When the sales on record for that place change, the estimate moves.
What you use it for
A first read, before you offer. It tells you whether a price is in line with what similar homes in that neighbourhood have done. Buyers, investors, and lenders use the same kind of model for that first pass. A bank valuation, or your own inspection, still comes after.
What a good one looks like
The large number is a fit. A score of 1.00 would mean the model lined up with every price in that sample. A score near 0.90 means it explains most of the difference between those homes. The smaller number, where we have it, is the typical miss: how far a usual estimate sits from the price that was paid, as a percent. A strong fit in one city does not travel to the next. Where a model has no miss on record, only the fit is shown.
How this compares
The home-value tools used in the US and Europe usually land within about 2% of the sale price when the house is already listed, and within about 7% when it is not. Those tools can see the asking price, and they are trained on millions of recorded sales.
The models here do not use the asking price. They learn from the sales and lets Rehani holds for that neighbourhood. The fit scores are high. On the sale model, the typical miss is wider than the 7% those tools quote for a home that is not already listed. A high fit means the model follows that sample. It does not mean the next sale will land that close.
Some sample models
- Sale
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- Rent
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- Vacation rental
- 0.83
Kitisuru, Nairobi
Run it on a real listing.
Paste a URL or name a neighbourhood.
RehAnalytics is decision support. Figures reflect available data and may change. They are not an offer of financial, legal, or tax advice. Past performance does not predict future results. Do your own due diligence before you invest. Rehani Soko · 2026