Predicting Lease Risk
Okapi AI provides comprehensive risk assessment for all sectors of the commercial real estate market, using complex machine-learning technology. By looking beyond the lease data that landlords currently have at their disposal, Okapi is able to extrapolate, benchmark and predict different risk factors associated with existing or prospective tenants during the due diligence phase. This enables asset managers and leasing teams to make informed decisions that can mitigate risks and reduce potential loss.
The Okapi platform analyses hundreds of data points collected daily from our internal systems, as well as information collected and purchased from third-party providers, for example company rating vendors such as S&P Global, macro statistics from the National Bureau of Statistics, market-specific data Data Axle, financial reporting from companies such as CSI Market, along with data capture from all the major news sites and social media platforms. These data points are evaluated against dozens of benchmarked risk factors which have been derived from analyzing over 20,000 leases. The outcome is a comprehensive risk report targeted at the level of tenant, asset and portfolio – which clearly presents the overall risk along with the underlying risk factors.
How it Works
Integrate seamlessly with existing systems to collect data
Wherever your data is stored, Okapi AI’s advanced data extraction system connects your data silos without burdening your IT department. Okapi improves the quality of our recommendations and predictions by enriching your building and business data with dozens of external data sources, introducing even more opportunities to optimize financial performance.
Machine learning algorithms learn from your data to personalize interactions
Okapi AI leverages artificial intelligence, machine learning and significant computing power to process massive amounts of data. At Okapi we understand that every tenant/lease behaves differently. This is why we designed our system to automatically and continuously monitor those differences and adjust its inner workings to easily fit within day-to-day risk analysis and monitoring processes.
