How much can you really borrow?

In this Friday Fundamentals episode, Ben Kingsley sits down with mortgage broker Luke Oxenham to unpack one of the most common questions property buyers ask:

“How much can I borrow?”

The answer isn’t always straightforward.

Luke explains how lenders assess income, expenses, HELP debt, credit cards, business income, car loans and more, plus why different banks can produce dramatically different borrowing outcomes for the exact same borrower.

They also discuss why borrowing capacity shouldn’t be confused with borrowing comfort, and why the largest loan available may not always be the right one for your goals.

You’ll learn:

✅ How lenders assess your income
✅ What HEM (Household Expenditure Measure) means
✅ Why credit cards can impact borrowing power
✅ How HELP debt is assessed differently today
✅ Why self-employed borrowers receive different outcomes
✅ The difference between lender tiers
✅ Why borrowing capacity is only a starting point

Because the real question isn’t always how much can I borrow?

Sometimes it’s how much should I borrow?

How to Retire On $3k A Week Case Studies: https://thepropertycouch.com.au/playbook-case-studies/
Moorr: https://www.moorr.com.au/

Got a question or a “hill” you’d like us to unpack? Send it through here 👉 https://thepropertycouch.com.au/topics/

⏱️ Timestamps

00:00 Intro
00:17 Welcome to Friday Fundamentals
00:22 The borrowing power question everyone asks
01:13 How lenders assess income
01:30 What is HEM?
02:06 The 3% serviceability buffer explained
02:27 Why lenders calculate income differently
03:15 First-tier, second-tier and third-tier lenders
04:08 How self-employed income is assessed
05:35 Credit cards and borrowing capacity
06:41 How HELP debt affects borrowing power
08:10 New lender approaches to HELP debt
09:02 Car loans vs novated leases
10:09 Online borrowing calculators explained
10:46 Using Moorr to estimate borrowing power
11:19 When to speak to a mortgage broker
12:44 Just because you can borrow more…
13:08 Updated $3,000 Per Week case studies

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