Predictive Analytics for Market Trends
Employ machine learning to analyze market trends, economic indicators, and other external factors to make informed decisions about property investments, development, and portfolio management in the commercial real estate sector.
Predictive Property Maintenance
Utilize data analytics and machine learning to predict equipment failures and maintenance needs in commercial properties, optimizing operational efficiency and reducing downtime.
Energy Management
Implement machine learning models to analyze energy consumption patterns, identify inefficiencies, and recommend strategies for optimizing energy usage, thereby reducing costs and environmental impact.
Occupancy Forecasting
Employ data analytics to predict occupancy trends, enabling more accurate planning for space utilization and facilitating proactive decision-making in property management.
Risk Assessment and Fraud Detection
Utilize machine learning algorithms to assess and predict potential risks in lease agreements, identify fraudulent activities, and enhance security measures to protect commercial properties.
Tenant Satisfaction and Retention
Leverage data analytics to gather insights on tenant preferences, satisfaction levels, and potential issues, enabling property managers to proactively address concerns and enhance tenant retention.
Lease Price Optimization:
Implement machine learning models to analyze market trends, property demand, and other relevant factors to optimize lease pricing strategies, ensuring competitive and profitable rates.
Space Utilization Optimization
Use data analytics to analyze space usage patterns, identify underutilized areas, and recommend adjustments to maximize the efficient use of commercial spaces.
The reason Calligo’s machine learning predictions have been proven to be so accurate is not just down to their data science skills. It is because they have an unusual blend of technical expertise and commercial understanding. Statistically-accurate models are commonplace. But teams that can create models that are both mathematically precise and also commercially savvy are very rare, and it is what Calligo specialises in.
Michael Boese
Calligo did a great job of helping Sunrun understand the art of the possible. Calligo came into our business and really taught us best practices in terms of Tableau application and what good looks like.
Steve Maier
The skills required to perform this sort of analysis were almost as varied as the datasets themselves. The project had to be planned strategically, requiring in these early stages more business analysis capability than data science. There were then substantial amounts of expert data cleansing and re-architecture required to ensure the intended insights would be extractable. And then once the data scientists had built and painstakingly fine-tuned the custom models, the team then also distilled the mathematical outputs into comprehensible, usable outputs. From commercial strategy to data architecture and data science, Calligo’s special mix of capabilities was exactly what this project needed.
Michael Boese
Data science projects often slip up by not sufficiently considering the end user, and how the output is intended to be used in practice. If humans are expected to make decisions based on the model’s calculations, then the data needs to be presented contextually, interactively and attractively. Calligo’s dashboards and visualizations allowed our teams – most of whom had no analytical experience – to interact with some of the most advanced data science they would perhaps ever come across, and make strategic decisions simply and confidently. The importance of this skillset cannot be underestimated.
Michael Boese
Some of the Property Management clients Calligo has helped
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