Data Science for Real
By: Hans Christian Ekne
Tags: Property Management, Machine Learning, Data Science, Digital, Real Estate

Using Data Science

With the right processes, data governance and architecture to support it, it is time to venture further along the digital journey. 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 Churn

Churn 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. For instance, if a tenant rents a unit, a churn model can be used to predict the probability that tenant will leave within a given time frame, for example 1 year.

Armed with this knowledge a property manager can better understand how the tenant mix will change over time and which units are likely to become available soon. This will enable managers to proactively target the high churn candidates and provide incentives for them to extend their contract. In addition, the model can highlight which units likely will need to be filled in the future, so that the vacancy period of a unit can reduced.

Churn models can also be used efficiently as a component of larger forecasting models, where the probability of churn is factored into future cash flow streams and aggregated across a broad population of tenants. By doing this, a real estate company can make more accurate predictions of its cash flow and therefor increase its total leverage without a proportional increase in risk. 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. They use MRI — a dedicated real estate software solution — to assist in their churn prevention, among other things. According to Sean Paul, and executive director of Spire they use MRI to “streamline customer relationship management… organize, automate, and evaluate its retention efforts — keeping track of leases and lease anniversaries, tenant records, maintenance activity and work orders”. Effectively creating win-win solutions for both property managers and tenants.

Lead Generation For New Tenants

Do you know who your best tenants are? Are they the ones who have the highest willingness and ability to pay, or perhaps the ones who always pay late so you can collect late fees? Or perhaps the best tenant is the one with a short one- to two-year turnover, giving you the opportunity to create new contracts and increase the rent. The answer to this question cannot be answered by a machine learning model, but should be evaluated from a business standpoint, aligned with the companies’ overall strategy.

Once the optimal tenant has been clearly defined and a company knows who it wants as tenants — keep in mind this can vary between buildings — it is possible to use machine learning algorithms to map out the features that are characteristic of these ideal tenants. This feature set can then be run through various leads-lists to determine which have the potential to be the best tenants. 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. One example of such a company is Vainu. Founded in 2013, they are using machine learning models paired with large databases to assist in the sales process. A case study from the real estate company Technopolis, revealed that they were able to use 3 times less time on sales prospecting than before. This type of solution could be especially relevant for property managers looking to increase their lead generation for their commercial units.

Location Optimization for Employees

Consider the following scenario. A company owns two office buildings with several units and have employees that use both buildings. Due to unexpected circumstances there is an imbalance in the usage of office space, which leaves one building over capacity while the other is under-utilized. How can this sub-optimal situation be improved with technology?

Using analytics and sensor systems the location of employees can be monitored, and an automatic messaging system can be configured to inform employees of the imbalance and guide a subset of the group to the less occupied space. This type of optimization exercise is not difficult to implement but can provide substantial savings if imbalances frequently arise, and can lead to less overall usage of office space.

Smart Building Technology

Smart building technology holds the promise to greatly benefit property managers. Some of the innovations are rather new and their potential not fully explored yet, but there are a few use cases that really stand out.

Predictive Maintenance

Predictive maintenance can help you identify issues with buildings before they occur and thus take action to prevent a breakdown or limit downtime as a result of repairs. This can make the maintenance staff more effective and thus reduce costs, as well as potentially increasing the total lifetime of appliances. For tenants as well, having a shorter downtime on critical infrastructure repairs will be advantageous.

The first step in using predictive maintenance techniques is to add sensors to the equipment that needs to be monitored. Data from these sensors is then collected in a database, and after some time in operation, time series data about the operation of the system is accumulated. 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. In the case of a classification model we will try to predict what the probability of failure will be within the next n time steps. With a regression model we predict how much time is left before the next failure, and this is often referred to as ‘remaining useful life’. Whichever model is chosen will depend the type of system and available data for modelling.

Building Information Modeling

Building Information Modeling, or BIM for short, is another exciting technological development within the smart building space. The basic idea is that the building has an identical digital twin complete with schematics and 3D-models. While many construction companies use BIM to aid in their building process, they can also provide significant advantages over the lifetime of the property, and thereby assisting the property manager.

A typical example of the usefulness of a BIM for property management would be when a system in a building failed. Since the building has a complete 3D-model of itself, the maintenance engineers can use augmented reality while traversing the building and obtain critical building information while performing repairs. 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 Management

It 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. As we continue to use more IoT devices and algorithms get better we will surely see many more use cases develop.