Building value with data
Kania combines fundamental knowledge of real assets investments with technological advances in data analysis and processing. We focus on quantitative analysis of real estate markets, provide analytics, identify and test predictive characteristics of data and deploy ML processes to large data sets for superior information and insights.
Analytics
Geolocation analytics developed specifically for logistics real estate markets
Kania logistics analytics is a geolocation based model framework based on large scale alternative data processing of logistics activity impacting demand for logistics/industrial space. It is designed to provide predictive information about logistics markets, submarkets and specific geolocations. It also helps to identify how attractive a location is compared to other locations within its market or other markets, if the location is improving (logistics activity at location is increasing) or deteriorating (logistics activity at location is stagnating/decreasing) making the location less attractive.
Data processing is based on geolocation specific networks and flows, incorporating satellite information and machine learning (ML) data processing. Locations are assessed based on drive-time weighted access to and flow of goods at freight handling locations within a network and estimate of transport vehicle flows on major distribution roads at geolocation based on large scale ML information processing. This flow information combined with demographic and physical market attributes provides unique and granular insights into logistics activity nowcasting with predictive characteristics to inform better investment, asset management, operational and risk management decisions.
Why listed real estate securities markets are more suitable for systematic alpha with consistent, repeatable processes potentially offering more attractive outcomes.
Rental growth is one of the main focus areas of fundamental managers, but how does it materialise in listed REITs funds portfolios.
What does trading activity in fundamental funds tell us about forward-looking aspects of portfolio construction.
For information about systematic alpha, our indices or our specialist factor data for listed real estate securities markets, please contact info@kaniaadvisors.com
Machine Learning
Processing geolocation data using ML
Machine learning techniques can be used to get accurate real estate market information. In this case we track new supply at a submarket geolocation over a period of time which provides accurate, specific and timely information about new construction activity, number of projects, size of projects, completion timelines and overall available stock supply. This allows us to monitor logistics assets at any location including markets where traditional estimates or coverage might be insufficient or unavailable. Further, machine learning techniques can process large volumes of data and provide valuable insights for investment decisions.
