A Thesis between Code and Concrete

Lessons learnt from my code heavy housing thesis/dissertation project

Disclaimer: My data sample is indicative and not conclusive, and based on a Bulawayo sample. I am open to sharing my code for the webscraper in case anyone wants to build on the project.

Why Quantify Housing Affordability?

“So what can I afford?” The silence that filled the space between us immediately became palpable. At that exact moment, I had access to more than a hundred properties online, at least fifteen offline properties but I knew there was none that could help me avoid the nihilistic conversation that followed.

I’ve confidently built and sold properties in different suburbs, different income affordances and yet, I’ve also been intuitive to the idea that the average citizen in Zimbabwe can not buy a home. Dissonance in the system? “How bad is it?”

The Findings

TLDR:

1. Economic Freedom is at the bottom rung in Bulawayo’s property market.

2. Deposits Trump Interest Rates.

3. Income is King.

The baseline, we’re living through an acute housing affordability crisis. But that’s nothing new to you. Above the basic, among property owners, Bradfield proved—relatively—to be the most affordable (price is not the most significant affordability factor), followed by Mbundane. The third sample suburb, Burnside, is so far off the other two we might as well stop talking about it, ps: It’s shaping up to be a rental dominated suburb.

Among all suburb residents (see disclaimer), the trend was the same save for the fact that the affordability gap between Bradfield and Mbundane was significantly reduced. Bradfield still led the losing race but there are essays to be written about wage stability and stringent financing terms when both are Sisyphean tasks in today’s reality.

Interestingly, income and not price was the leading signal towards affordability followed closely by amount paid as deposit. The higher the deposit, the better the long term affordability.

The Gap Between prop-tech heaven and Zimbabwe

Be it the CRM slash property management platform I am currently developing or the rather simple webscraper one aspect continues to concern me. Who owns the data?

I do not ask this on the premise of paying you for your hair type data but rather in an attempt to find the silos. There’s a limit on what tech can do in any industry when key info is hidden behind silos.

Be it financial institutions and income data or Zimstat and most of their accessible data being from 2014, if there’s to be any notable deep tech progression in Zim, there has to be some level of data transparency in the country.

If we were having this conversation with a different environment in context I’d point to state institutions providing this data. But maybe, just maybe, we could see private corporations in Zim lead data science forward transparency, accessibility and usability wise.

Next Steps?

I am currently holding onto a lot of projects at the moment but here’s my current build list:

1. Development/Deal Analysis Model

2. A Market Velocity Indicator (mostly a webscraper with some math logic)

3. A construction material index

*Discussion is in line with indicative data from a research project I conducted titled: “Measuring Housing Affordability in Bulawayo’s Suburbs: An index based on Income, Financing terms, and spatial factors”