Machine Learning

Machine Learning is the “practice of building systems, known as models, that can be trained using data to find patterns which can then be used to make predictions on new data”. It is built on statistical relationship, which are used to learn about the past instances of what we are predicting and then are applied to new data to observe an analyze specific attributes called features. The event or value, which is predicted, is known as the target variable (Devlin, 2017).

“The field of Machine Learning is devoted to the building and testing learning algorithms that automatically recognize patterns in the ever-increasing quantity of available information” (Masias, 2016). In order to build a consistent and reliable predictive model, historical data must meet exceptionally high quality standards. Data must be unbiased, over the entire range of inputs, for which one aims to develop the predictive model (Redman, 2018).

### Linear Regression for Machine Learning

The fundamental basic algorithm that every machine learning is based on, is a linear regression algorithm. Linear regression is a linear model that assumes a linear relationship between the input variables (x) and the single output variable (y). More specifically, that (y) can be calculated from a linear combination of the input variables (x). When there is a single input variable (x), the method is referred to as simple linear regression. When there are multiple input variables, the method is known as multiple linear regression. In general, we usually deal with data, which have multiple input variables. The case when we have more than one feature is known as multiple linear regression, or simply, linear regression. The equation of the multiple linear regression can be defined as follows: y(x)=w0+w1x1+w2x2+…+wnxn where w are the weights of the variables (x) affecting the final output of the evaluation (Zikmund, 2000).

For developers starting a project, such as investing in a real estate asset, the greatest challenge is often securing initial equity partners. Since those equity investors typically become the property owners, they bear much of the project risk. Hence how can real estate data be used to improve the investment decision process and optimize returns? Software and cloud-based platforms are now implemented for this purpose, enabling automated data aggregation into large valuable database. By visualizing, filtering, analyzing, or even simulating future scenarios, the real estate industry can assess market trends, financial assets, and design decisions. It can even predict potential future outcomes.

To understand the emerging real estate predictive analytics market, it is important to get a sense, albeit superficial, of the kind of technology at stake. By using machine learning, deep learning, or neural networks, new technologies are taking traditional statistical tools to a new level. Advanced statistical methods follow the same process. First, a phase of “training” in which the machine is fed a dataset and “learns.” In other words, it assimilates the dataset’s complexity and tries to weigh the impact of each factor (house characteristics for instance) on the output value (house price). Following that is a “testing” phase in which the machine previously trained is tested against a set of data where the output is known. We observe here how accurately the algorithm predicts the output value. Once the machine is calibrated (after a certain number of iterations), it is ready to predict. This is the prediction phase, in which we use the algorithm to guess the output value of a dataset with unknown output value. The real estate industry has invested a great deal of effort into using simple regression models to complete short term analysis. The predictive precision of machine learning is pushing the boundaries of forecasting (Chaillou, Fink and Gonçalves, 2017).