Should you buy a house and rent it out?
Buying a property to rent out can be a good financial investment, but has high up-front and on-going costs, and is relatively illiquid. Is it worth it?
How it works
The model runs by simulating 1,000 "possible futures" based on the parameters you specify. In Causal, model parameters are probabilistic — they can take on a range of values to account for the uncertainty in your estimates. Causal's outputs are distributions too, letting you understand the uncertainty in your results.
Customise the inputs to run your own scenarios comparing buying and renting.
Inputs
Model
Instructions:
- Click on a node to change its value
- Click and drag on a range to increase or decrease it
- Click on the chart below to see the model ouput at different points in time
Outputs
- Buy to rent (95% confidence)
- Buy to rent (65% confidence)
- Baseline (6% annual growth)
Overview
If you know Excel, you'll quickly pick up Causal.
The Model
We model the wealth accumulated at different points in time through buying a house and renting it out. The chart compares this scenario to the simple baseline of investing the property's initial costs into the stock market, receiving 6% annual returns. Here's how the scenario was modeled. Example figures are given in square brackets.
Buying to rent
When buying a property of a certain Property Price [$600,000], you need some initial capital:
- Down-payment [$120,000] — a lump-sum that you pay up-front (typically around 20% of the property price)
- One-off costs [$36,000] — various fees involved in the property purchase (typically 3-6% of the property value)
You then take out a loan with a particular Loan Term [30 years], for the property price less the down-payment [$480,000]. The Interest Rate [3–5%] of your loan fluctuates slightly each year, and each month, you pay off part of the loan, and pay interest on the remaining unpaid part.
Each month you also receive rent payments from your tenant(s). We assume that these cover at least the cost of your monthly mortgage payment, but they'd ideally be a little more [$2,000].
Each year, you pay Annual Costs [1.5-2.5%] for property maintenance, and each year, the value of your property increases according to the rate of Annual Appreciation [3–6%]. Each year, you raise the monthly rent in line with Annual Appreciation.
Each year, the wealth you've accumulated is equal to the current value of the property, minus the loan amount outstanding, plus the total rent you've collected.
In practice, there will be costs involved with selling the property that our model doesn't account for. Property is also relatively illiquid — there's no guarantee that you'll be able to sell the property at your desired price and on your desired timeline.
When analysing a real-world scenario, there is always a trade-off between model complexity and accuracy. For simplicity, we've ommitted some real-world factors, like tax incentives and deductions here.
Technical Details
Probabilistic modeling
Unlike spreadsheets, nodes in Causal can be probabilistic, to account for uncertainty in estimates.
It's hard to say exactly what Annual Appreciation will be over the next 10, 20, 30 years. But we can reasonably say that they'll probably be 0–4%, and that there's a small chance they may even be significantly lower or higher. Representing variables as probability distributions lets us do this.
Simulation
Causal's models run via simulation. By sampling appropriately from each probabilistic node, the Monte Carlo engine simulates 1,000 "possible futures". So instead of a single output number, Causal outputs a distribution representing the range of plausible outcomes.
What's wrong with spreadsheets?
Spreadsheets can't handle uncertainty.
Most quantities in a model aren't precisely known, but spreadsheet cells hold fixed values. Without uncertainty, you can't know how confident to be in your results.
Causal’s models are probabilistic, letting you understand the uncertainty of your results.
Spreadsheets aren't collaborative.
Spreadsheet models are opaque — it's hard for your team to understand your model, even after tracing through each cell and its references one by one.
Causal’s visual diagrams and abstractions make it clear how your model works.
Spreadsheets can't connect to data.
To work with datasets, you have to pass around CSVs in email attachments, or build data pipelines to periodically export data from other sources.
Causal has live data integrations with SQL databases, APIs, and your existing SaaS products, like Stripe and Salesforce.
If this resonates with you, please leave your email above, or get in touch: hi@causal.app.
We'd love to hear from you.