The Xome® real estate and auction applications are developed using a combination of data and machine learning. Several libraries are used in machine learning, each offering its own unique set of functionalities. The choice of which machine learning libraries we use has a significant impact on our user’s application experience.
Different machine learning libraries offer various algorithms and models suited for specific tasks and data types. When developing real estate applications, algorithms that are best suited to address the specific challenges and requirements of the project should be considered. For example, regression algorithms may be useful for automated property valuations, while classification algorithms can be used for predicting market trends.
Scalability and performance are other big factors to consider. Real estate applications often deal with large datasets and require timely predictions. The machine learning library chosen should be able to handle increasing data volumes and user demands.
There are a few top machine learning libraries that developers can use for building real estate applications. Depending on the project’s needs, one or more of these libraries can be utilized to access the appropriate tools for specific use cases.
TensorFlow
TensorFlow is an open-source machine learning library implemented in Python. It is known for its capabilities in deep learning, which enables the development of automated valuation models (AVMs) that can forecast various aspects of the real estate market such as property prices.
Much of modern-day machine learning happens on Tensorboard, a visualization tool provided by TensorFlow, which is famous for neural network deployments and structuring. It allows monitoring and visualization of various aspects of a TensorFlow model, including its training progress and performance.
TensorFlow also supports other programming languages such as C++ and Java through its TensorFlow C++ API and TensorFlow Java API. These options allow developers to use TensorFlow in their preferred programming languages and integrate it with existing codebases.
XGBoost
XGBoost is written in C++ and provides interfaces for various programming languages, including Python, R, Java, and Julia, but it is not a general-purpose machine learning library like TensorFlow. It is a specialized library dedicated to gradient boosting, which combines multiple weak or base models to create a strong predictive model. Through this, it can control overfitting and battle regressivity in AVMs by attempting to automatically select the inflection point where performance on the test dataset starts to decrease.
XGBoost is also highly scalable and able to handle large datasets efficiently. This is extremely important as real estate platforms deal with vast amounts of property data, including sales records and property attributes.
Pandas
Pandas is a widely used Python library for data manipulation and analysis. It is often used with other machine learning libraries, such as TensorFlow and Scikit-learn, for preprocessing and preparing data.
Real estate datasets require cleaning, merging, and transforming data before analysis. With Pandas, users can easily load, clean, and preprocess data while handling missing values and different data types, helping technical teams simplify the data collection and preparation phase of the machine learning lifecycle.
Additionally, it provides data structures and functions that make it easier to work with structured data, such as time-series data. Through time series analysis, Pandas allows real estate platforms to analyze historical property sales data and identify seasonal patterns.
NumPy
NumPy stands for Numerical Python, which is a machine learning library for numerical computing in Python. It offers a wide range of mathematical functions which are optimized for performance and are implemented in compiled C code, making them faster than operations implemented in pure Python. Because of this, real estate platforms can quickly perform complex mathematical operations on large datasets and meet user demands.
NumPy also supports vectorized operations, allowing real estate platforms to apply operations to entire arrays of data efficiently. Compared to traditional iterative approaches, this improves computational performance and makes it particularly beneficial for tasks involving large datasets.
Scikit-learn
Scikit-learn is an open-source machine learning library in Python built on NumPy, SciPy, and matplotlib. It includes a comprehensive selection of machine learning algorithms and tools, including regression, classification, clustering, and model selection. These can be directly applied to real estate datasets for tasks such as predicting property prices, classifying property types, and identifying clusters of similar properties.
The wide range of algorithms and developer tools available also allows for experimentation to help choose the most suitable approach for specific real estate applications.
Leveraging the benefits of machine learning in real estate applications
This is just scratching the surface of machine learning libraries and their benefits. To get more information on each library, there is no better way than for developers to test them out to decide if they meet the needs of the platform being built.
By selecting a library that has the right algorithms, performance, and capabilities, developers can improve the overall quality of their real estate applications. As a result, this leads to more reliable predictions that allow users to make better-informed decisions when buying or selling a home.
