Predicting fundamental real estate trends and deconstructing them to a site or neighborhood level can be difficult when evaluating investments. Asset and investment managers often times must rely on government-published statistics and other public data sources that lack granularity. Understanding how neighborhoods are changing, influenced by social mobility, is a key component of this challenge.
Distilled VALUE™ can provide the solution.
What is Distilled VALUE™
Distilled VALUE™ is a predictive Real Estate analytics platform. We base it on Social Physics, a machine learning data driven analytics methodology developed at MIT. Our field tests prove that mobility patterns, aggregated from people’s daily digital traces, can provide predictive insight into underlying value trends for real estate.
How does Distilled VALUE™ work?
Distilled VALUE™ identifies and predicts real estate value by analyzing human activity data sourced (e.g. geocoded payment transactions, aggregated and anonymized mobility data) and combines them with additional business intelligence data. We are able to accurately assess and predict Micro GDP™, a unique measure of economic productivity at a half city block level, through our analytical modeling.
What can Distilled VALUE™ do for your Business?
Provide 3 to 5 year real estate value forward trends
Identify areas of increased or decreased economic productivity
Provide a forecasting toolkit to augment your analytical abilities and decision-making
What is Social Physics?
Social Physics, developed at MIT, is a computational social science that merges fundamental equations about human behavior with large-scale data observations. Social Physics can be used to understand and predict human behavior at scale.
How do we address data privacy and policy concerns?
You can read more about these concepts in the book Trust::Data published by our co-founders David Shrier and Prof. Alex Pentland and see the open source code project at trust.mit.edu.