Data Science for RealTransforming property management with advanced analytics and machine learning“It’s tangible, it’s solid, it’s beautiful..The property manager still has an important role to play in this new digital age, but we are now increasingly able to make him more efficient and let him spend time on the tasks that have a greater business impact.There are several ways real estate companies can leverage data science to improve property management — from integrating smart building technology to implementing machine learning models for tenant management..This article will discuss some of the ways to prepare for the use of these technologies, and present concrete use cases for advanced analytics and machine learning models within property management.Walk Before You RunUsing machine learning algorithms to drive profits sounds cool — and it can be a great driver of value, but there needs to be an understanding of business processes, structure and governance in place before these technologies can be fully utilized.ProcessFirstly, the business processes should be clearly understood and defined..Data science — the application of advanced analytics and machine learning models to industry problems — is the next natural step..Below are a few examples of how this technology can be used to enhance property management.Tenant ChurnChurn modelling is one of the classical applications of data science..Industries such as banking, insurance and telco have been using churn models for decades to predict customer behavior, and it has obvious use cases for property management as well.In its simplest form a churn model is a binary classification model that given a set of predictors, or input variables, outputs a classification..Ultimately, leading to a more efficient use of capital.An example of a real estate company who takes tenant management seriously is Spire Property Management..When the feature set is rich and complex, machine learning models often perform this type of classification at superhuman level, and scale infinitely better than humans.For commercial real estate there are also several providers that help companies generate leads and increase sales..Given that we have enough observations, the time series then contains the data needed to build machine learning models that predict the next failure of a system.These machine learning models are typically either classification models or a regression models..Data that could be provided to the engineers include service history, system specifications and contract information, making the repair process easier and faster, and as with predictive maintenance also lead to shorter down times for critical infrastructure.Beyond Property ManagementIt is important to note that we have primarily discussed how digitization and data science can be used to improve property management..This is of course just a subset of the real estate industry and there are a plethora of other possible application areas of data science for the industry as a whole..Machine learning models are now used to predict anything from price and rent income to demographic trends..Learn how our open and connected…www.mrisoftware.comHow tenant churn could impact profitabilityTenants, as the main source of income for commercial buildings, are key, thus tenant retention should be a primary…www.bizcommunity.comMachine Learning Techniques for Predictive MaintenanceIn this article, the authors explore how we can build a machine learning model to do predictive maintenance of systems…www.infoq.comWhat is data governance (DG)?. More details