For self-employed Australians, navigating the mortgage market can be complex—especially when income documentation doesn’t fit the standard mould. In this guide, Stephen Andrianakos, Director of Red Door Financial Group, outlines eight flexible loan structures designed to support business owners, freelancers, and entrepreneurs.
1. Full-Doc Loan
A full-doc loan is the most straightforward and competitive option for self-employed borrowers with up-to-date tax returns and financials. Lenders assess two years of tax returns, assessment notices, and business financials. This type of loan offers high borrowing capacity, access to features like offset accounts and redraw facilities, and fixed and variable rate choices.
2. Low-Doc Loan
Low-doc loans are designed for borrowers who can’t provide the usual financial documentation, such as those in start-up mode or recently expanded businesses. Instead of full tax returns, lenders accept alternatives like profit and loss statements or accountant’s declarations. While rates may be slightly higher, these loans make finance accessible where banks might otherwise decline.
3. Standard Variable Rate Loan
A standard variable loan moves with the market and offers flexibility in repayments, extra contributions, and redraw options. It’s ideal for borrowers who want to manage repayments actively or pay off their loans faster when income permits. With access to over 40 lenders, brokers can help match borrowers with a variable product suited to their financial strategy.
4. Fixed Rate Loan
A fixed-rate loan offers repayment certainty over a set term—typically one to five years. It’s popular with borrowers seeking predictability, especially in volatile rate environments. While fixed loans offer fewer flexible features, their stability can be valuable for budgeting and cash flow planning.
5. Split Loan
A split loan combines fixed and variable portions, giving borrowers the security of a fixed rate on part of the loan and the flexibility of a variable rate on the other. This structure benefits self-employed clients with irregular income, allowing them to lock in part of their repayment while keeping some funds accessible.
6. Construction Loan
Construction loans release funds in stages aligned with the building process, from the initial slab to completion. These loans suit clients building a new home or undertaking major renovations. Most lenders offer interest-only repayments during construction, switching to principal-and-interest after the build. Managing timelines and approvals is key to a smooth experience.
7. Interest-Only Loan
Interest-only loans allow borrowers to pay just the interest portion of the loan for a set period, preserving cash flow. This structure is often used during growth phases in business or for investment purposes. After the interest-only period, the loan typically converts to principal-and-interest repayments.
8. Offset Home Loan
An offset home loan links your savings account to your mortgage, reducing the interest charged on the loan. For self-employed borrowers with fluctuating income, it’s a valuable tool for managing cash flow while still reducing interest and accelerating loan repayment. The funds remain accessible, offering both flexibility and efficiency.
Red Door Financial Group is a Melbourne-based brokerage firm that offers personalised financial solutions for residential, commercial, and business lending.
A divide has opened in the tech job market between those with artificial-intelligence skills and everyone else.
A 30-metre masterpiece unveiled in Monaco brings Lamborghini’s supercar drama to the high seas, powered by 7,600 horsepower and unmistakable Italian design.
A divide has opened in the tech job market between those with artificial-intelligence skills and everyone else.
There has rarely, if ever, been so much tech talent available in the job market. Yet many tech companies say good help is hard to find.
What gives?
U.S. colleges more than doubled the number of computer-science degrees awarded from 2013 to 2022, according to federal data. Then came round after round of layoffs at Google, Meta, Amazon, and others.
The Bureau of Labor Statistics predicts businesses will employ 6% fewer computer programmers in 2034 than they did last year.
All of this should, in theory, mean there is an ample supply of eager, capable engineers ready for hire.
But in their feverish pursuit of artificial-intelligence supremacy, employers say there aren’t enough people with the most in-demand skills. The few perceived as AI savants can command multimillion-dollar pay packages. On a second tier of AI savvy, workers can rake in close to $1 million a year .
Landing a job is tough for most everyone else.
Frustrated job seekers contend businesses could expand the AI talent pipeline with a little imagination. The argument is companies should accept that relatively few people have AI-specific experience because the technology is so new. They ought to focus on identifying candidates with transferable skills and let those people learn on the job.
Often, though, companies seem to hold out for dream candidates with deep backgrounds in machine learning. Many AI-related roles go unfilled for weeks or months—or get taken off job boards only to be reposted soon after.
Playing a different game
It is difficult to define what makes an AI all-star, but I’m sorry to report that it’s probably not whatever you’re doing.
Maybe you’re learning how to work more efficiently with the aid of ChatGPT and its robotic brethren. Perhaps you’re taking one of those innumerable AI certificate courses.
You might as well be playing pickup basketball at your local YMCA in hopes of being signed by the Los Angeles Lakers. The AI minds that companies truly covet are almost as rare as professional athletes.
“We’re talking about hundreds of people in the world, at the most,” says Cristóbal Valenzuela, chief executive of Runway, which makes AI image and video tools.
He describes it like this: Picture an AI model as a machine with 1,000 dials. The goal is to train the machine to detect patterns and predict outcomes. To do this, you have to feed it reams of data and know which dials to adjust—and by how much.
The universe of people with the right touch is confined to those with uncanny intuition, genius-level smarts or the foresight (possibly luck) to go into AI many years ago, before it was all the rage.
As a venture-backed startup with about 120 employees, Runway doesn’t necessarily vie with Silicon Valley giants for the AI job market’s version of LeBron James. But when I spoke with Valenzuela recently, his company was advertising base salaries of up to $440,000 for an engineering manager and $490,000 for a director of machine learning.
A job listing like one of these might attract 2,000 applicants in a week, Valenzuela says, and there is a decent chance he won’t pick any of them. A lot of people who claim to be AI literate merely produce “workslop”—generic, low-quality material. He spends a lot of time reading academic journals and browsing GitHub portfolios, and recruiting people whose work impresses him.
In addition to an uncommon skill set, companies trying to win in the hypercompetitive AI arena are scouting for commitment bordering on fanaticism .
Daniel Park is seeking three new members for his nine-person startup. He says he will wait a year or longer if that’s what it takes to fill roles with advertised base salaries of up to $500,000.
He’s looking for “prodigies” willing to work seven days a week. Much of the team lives together in a six-bedroom house in San Francisco.
If this sounds like a lonely existence, Park’s team members may be able to solve their own problem. His company, Pickle, aims to develop personalised AI companions akin to Tony Stark’s Jarvis in “Iron Man.”
Overlooked
James Strawn wasn’t an AI early adopter, and the father of two teenagers doesn’t want to sacrifice his personal life for a job. He is beginning to wonder whether there is still a place for people like him in the tech sector.
He was laid off over the summer after 25 years at Adobe , where he was a senior software quality-assurance engineer. Strawn, 55, started as a contractor and recalls his hiring as a leap of faith by the company.
He had been an artist and graphic designer. The managers who interviewed him figured he could use that background to help make Illustrator and other Adobe software more user-friendly.
Looking for work now, he doesn’t see the same willingness by companies to take a chance on someone whose résumé isn’t a perfect match to the job description. He’s had one interview since his layoff.
“I always thought my years of experience at a high-profile company would at least be enough to get me interviews where I could explain how I could contribute,” says Strawn, who is taking foundational AI courses. “It’s just not like that.”
The trouble for people starting out in AI—whether recent grads or job switchers like Strawn—is that companies see them as a dime a dozen.
“There’s this AI arms race, and the fact of the matter is entry-level people aren’t going to help you win it,” says Matt Massucci, CEO of the tech recruiting firm Hirewell. “There’s this concept of the 10x engineer—the one engineer who can do the work of 10. That’s what companies are really leaning into and paying for.”
He adds that companies can automate some low-level engineering tasks, which frees up more money to throw at high-end talent.
It’s a dynamic that creates a few handsomely paid haves and a lot more have-nots.
BMW has unveiled the Neue Klasse in Munich, marking its biggest investment to date and a new era of electrification, digitalisation and sustainable design.
A luxury lifestyle might cost more than it used to, but how does it compare with cities around the world?