We change the decision-making and operations of our customers. We challenge the traditional consulting by delivering software, not presentations or spreadsheets, that optimize businesses of our clients. We serve our customers globally. We are not afraid of any time zone – from the US through all over Europe up to Hong Kong and Australia.
Appsilon uses a holistic perspective for each project we take on. We consider ourselves partners that augment a company’s existing resources. This philosophy can also be applied in the real estate industry. For example, some clients may need help with their apartment, while others are working with large skyscrapers. And, the services we provide vary accordingly to tailor to the individual needs of the property. Because each property has its own unique challenges, every project will require the services of various real estate professionals. We have personal brokers, lawyers, construction crews, developers, facility managers, listing sites, appraisers, and more. All of these individuals or companies coalesce in an effective shifting ecosystem. But, when there is data involved, you need Appsilon Data Science.
An important tidbit to keep in mind is that data science does not need ‘big’ data to work. We are more than happy to work with distributed NoSQL data sets, and Excel also works; we can even help you begin the data collection process. Even if you have no idea where to start, how to do it, or what data collection can do for you, we will be there every step of the way.
It’s always easier when you have a starting point to work from. We hope these examples inspire you to think of the future that we can create together.
With predictive maintenance, we monitor equipment to avoid catastrophic failure. Historical data gives a thorough overview of how a building or a particular piece of equipment is used. Identifying anomalies and patterns is the first step in this process. With enough data, these factors begin to show themselves, allowing us to react and avoid downtime. This works for both small and large facilities and can even be used as an additional bargaining chip when negotiating over minimum uptimes. Using machine learning in predictive maintenance makes implemented models better over time, while simultaneously decreasing the need for hands-on monitoring.
We are on our way towards predicting what will happen to these buildings; for example, let’s look at our energy consumption predictions. Not only does this help us budget, but it can also help us look for improvement in energy-intensive properties. It’s a bit difficult to recommend to “company x” that their servers on “floor y” are using too much energy but decreasing HVAC draw is more likely. And, the environment will thank us. By decreasing our energy usage, we can strive for energy performance certificates while working on our Corporate Social Responsibility. This is the right thing to do for our planet, and at the same time, having a green reputation increases our brand’s attractiveness to customers and potential employees. All of the solutions we mentioned can take advantage of a whole gamut of models. If your organization is just beginning its endeavor in AI, then we’d recommend starting with decision trees; they are white box solutions that are very easy to understand and can increase buy-in from up the ladder. If you are looking for the next-level performance, it’s probable that ensemble models or deep learning will be useful for you. Keep in mind that all of these solutions can work in real time and immediately provide notifications.
When deciding the location of a new property, it is important to consider multiple aspects. These factors can have immense effects on property costs and its overall usefulness. A famous example of this concept was when a fire department’s headquarters location was already determined but officials decided to ask for a data scientist opinion. It turns out that he found a location that was not only cheaper but also decreased the aggregate response time and distance. Such insights may seem niche, but there is nothing stopping us from applying them to the market at large.
Location is not the only variable that we can look at. Timing is an important factor in property pricing. Let us predict what a region’s estimated attractiveness will be in a few years to leverage your investment and allow you to make a more informed decision. This also works the other way around. It may make sense to sell a few existing properties, as they are on their way down, and this trend will continue for the foreseeable future. But, there is a way to counteract the depreciated areas. A single, dilapidated property may account for most of the adverse effects on neighboring buildings. If one of these building is ours, it would make sense to invest in this property and reinvigorate the surrounding area.
A property with existing structural integrity affects the total investment cost, and in turn, the return on investment. Image recognition has reached a point where its error rate is smaller than that of a human. In combination with anomaly detection, we can estimate potential cracks or idiosyncrasies that flag the need for further inspection.
What we are actually talking about is Automatic Valuation Models (AVMs). Similar to other machine learning models, AVMs can improve with time and keep track their past accuracy. As previously done, we can even take this a step further by combining expert knowledge with these models. Expert appraisers received our predictions through a decision support system, and they made property decisions that we used as input for improving the model’s accuracy week after week.
Think back to when you were looking at an apartment you wanted to rent or a new house you wanted to buy. Do you remember when you stumbled upon a listing that seemed too good to be true? You may have asked yourself, is it haunted? Instead of being excited about finding the perfect listing, you were skeptical. This dilemma is detrimental to everyone involved. Unfortunately, listing sites do not know the exact details for each property, and it is not practical for you to send an appraiser to every listing you are interested in.
So, how do we fix this? Well, there are a few solutions. One of them is automatic pricing. Listing sites could add dynamic pricing so that their listing changes to accommodate the market. This would require historical analysis. We can even take this a step further by running an assessment on a potential buyer’s predisposition to pay above market for their dream home. Or, we could completely sidestep the search process by searching for that dream home automatically.
Stop wasting hours going through Craigslist listings one by one and let an intelligent recommendation engine create a list of the best property listing for you.
We’ve looked at three types of real estate segments where we can apply data science and machine learning. As the number of possible sensors increases, so does the types of data collected. As a result, the number of use cases that come about. What you just read is a brief overview of current use cases, but we hope it leads you towards new, interesting ideas for your business when you are on the search for the perfect property or home.