For many seasoned commercial property players, the words Artificial Intelligence (AI), property technology and disruption are ‘over the horizon’ terms which are ‘medium to long term strategic concepts’.
Over the last decade, certain types of technologies like AI, Blockchain, Internet of Things and Big Data have been infiltrating sectors such as banking, healthcare and telecommunications at blistering speed.
However, Real Estate has been a ‘laggard’ in technology adoption, primarily because many business models have traditionally relied upon ‘growing the value of the asset’ as the business, rather than using technology to add value to the business asset. Whilst this has worked in the past, today it is no longer a reality.
Furthermore, Australia is at least five or more years behind the rest of the Western World in adapters of technologies and now it is playing ‘catch-up’ at alarming speed.
‘PropTech’ (Property Technology) can broadly be divided into three main categories, some of which are shared with ‘FinTech’ (Financial Technology):
Smart Real Estate
These are the technologies that make the operation and management of property assets more efficient. These technologies range from ‘Smart Building Automations Systems’ to Big Data Building collection systems and Internet of Things tools such as Digital Twins.
Real Estate Trading and Leasing Platforms
These are technology-based platforms which facilitate trading of assets including researching, pricing, funding and management. This is by far the biggest and most active sector and includes platforms like ‘CoreLogic‘ and RealEstate.com.au.
For commercial building owners, leasing and portfolio management are the most pressing issues driving cash flow and ultimately returns.
The process of leasing is usually manual and often highly ineffective. PDF copies of leases are sent back and forth between Landlords and Tenants with no way of technologies being able to read and understand the data trapped within them.
Financial systems that rely on this data cannot read these documents and therefore need to be re-keyed, creating errors and further inefficiencies.
Technologies such as Natural Language Processing (a form of AI) are changing this by ‘reading’ the data in these leases and turning them into structured data.
Company’s already adopting these technologies are reporting massive gains in productivity and significant reductions in risk, errors and the cost of administering their portfolios.
For example, Rachel Bessis, former Head of Market Development ANZ for Swarovski Australia and New Zealand, who was the first beta tester of our AI system Accurait®, reported that Swarovski could save 2,000 hours over the life of a lease contract.
With Swarovski’s roughly 60 lease portfolio in Australia and New Zealand with an average of five years per leases, that is a massive 120,000 hours saved over five year administering their portfolio.
This equates to major cost savings for the business. “We anticipate Accurait® could save us in excess of $100,000 per year in terms of increased productivity, reduction in additional labour, legal and accounting costs and savings in rent form processing errors”.
If you’d like to learn more about how Accurait® can potentially save your business hundreds of thousands of dollars per year in administering your portfolio, contact us on 1300 RETAIL (738 245) or firstname.lastname@example.org for an obligation free discussion and demo.
Join the likes of Swarovski, Chanel, Jaycar Electronics and Mister Minit, just to name a few of our clients, who are reaping the benefits of embracing technology in the commercial Real Estate sector.
This post was authored by Simon Fonteyn. Simon is one of Australia’s leading experts in retail, childcare and medical leasing and rental valuations. He holds a Degree in Accounting & Finance, a Diploma of Valuation, a Masters of Management and is an Associate of the Australian Property Institute. With over 25 years experience in the commercial property industry, Simon founded LeaseInfo® as a way to provide more transparency to the industry.