Published on

January 4, 2024

Machine Learning

ML-Driven House Price Prediction in 2023

Explore the world of ML-driven house price prediction for 2023 with our guide.
Julia Dunlea
VP of Marketing
Machine Learning

The real estate market, a cornerstone of the global economy, has always been characterized by its ever-shifting landscape. For centuries, buyers and sellers alike have been in pursuit of accurately determining the value of a property. 

In this timeless quest, the advent of Machine Learning (ML) – a subset of artificial intelligence (AI) – has emerged as an ally, offering a data-driven approach to this challenge. By leveraging advanced statistical techniques, ML can analyze historical data to predict house prices accurately and unravel complex patterns that may elude manual modeling, thus empowering real estate professionals to make informed decisions.

This article embarks on a comprehensive exploration of house price prediction using machine learning. From the nitty-gritty of data collection and preprocessing to the nuances of model training and deployment, we leave no stone unturned. We'll also delve into a comparative study of ML models and traditional programming languages like Python, shedding light on their respective advantages and disadvantages.

Plus, we’ll introduce you to Akkio, an innovative ML platform that simplifies the task of predictive modeling for the real estate industry. By the end of this article, you'll have a deep understanding of how ML can enhance house price prediction, driving insightful and impactful outcomes!

Machine learning for house price prediction: A comprehensive guide

The realm of real estate is increasingly embracing the power of machine learning for house price prediction. This involves the application of advanced statistical machine learning techniques to develop models capable of predicting real estate property prices

Imagine a realtor establishing a broad target price range and then deploying an ML model to refine the prediction to a narrower range. This process not only enhances precision but also aids in making informed decisions.

ML algorithms are often the backbone of such predictions, with regression algorithms being a popular choice. 

Regression is a type of supervised learning algorithm used for predicting continuous numerical values based on input features. It’s especially useful when the target variable to predict is a real number, such as a price, temperature, or age.

The beauty of ML algorithms lies in their ability to learn patterns and relationships from historical data to make accurate predictions on new, unseen data. Here's a glimpse into the general process:

1. Data collection

The journey to accurate predictions starts with gathering a comprehensive dataset. This dataset should include various features or attributes of properties, such as:

  • Location.
  • Size.
  • Number of bedrooms and bathrooms.
  • Amenities.
  • Nearby schools.
  • Crime rates. 

Moreover, it should contain historical sale prices. The richer the dataset, the more informed the ML model will be.

2. Feature selection and engineering

It's time to select relevant features that are likely to have a significant impact on house prices. This process, called feature selection, is necessary for developing an accurate model.

You can also create new features by combining or transforming existing ones to capture more information, a process known as feature engineering.

3. Model selection

Choosing an appropriate ML algorithm for your prediction spans common options, including:

  • Linear regression models.
  • Decision trees.
  • Random forests.
  • Support Vector Machines (SVM).
  • Neural networks. 

Fortunately, some ML platforms, such as Akkio, can automatically handle this for you, making the process easier for real estate businesses that want to integrate ML into their workflow.

4. Training

Dividing the dataset into a training set and a testing set is the critical step. The training set is used to teach the ML algorithm the relationship between the features and the target variable (house prices). The algorithm adjusts its internal parameters to minimize the prediction error on the training data. 

It's essential that the training set is different from the test set. In practical settings, the dataset is split into three parts: training, validation, and testing. The validation set is used to verify that the model is learning the right things before evaluating it on test data.

Pro tip: Akkio’s easy-to-use interface lets you select which model you want to apply to your dataset according to your goals.

Training the prediction model on Akkio.

5. Model evaluation

Once the model is trained, it's time to evaluate its performance using the testing set. This step helps ascertain the accuracy and reliability of the model.

6. Deployment

The final step is to implement the trained model into a real-world application, such as a website or mobile app. 

Users can then input property details to receive predicted house prices, thereby making the entire process user-friendly and efficient.

These steps outline the procedures of machine learning that businesses and individuals can leverage to predict house prices accurately, resulting in better decision-making and increased profitability in the real estate market.

Machine learning models vs. traditional statistical models: A comparative analysis

The prediction of house prices has long been a domain of traditional statistical models. However, the advent of ML-based data analysis has brought a paradigm shift, offering a new approach to this age-old problem.

Let’s compare and contrast the pros and cons of using machine learning models and traditional statistical models for predicting house prices.

Machine Learning Pros and Cons
Machine Learning Traditional statistical models
Pros Automated learning: ML models shine with their ability to automatically learn complex patterns and relationships in data, potentially capturing subtle nuances that might be missed through manual modeling. Control and flexibility: Traditional, manual statistical modeling allows for precise control over the modeling process, feature selection, and data preprocessing. This gives modelers a high degree of freedom and flexibility.
Adaptability: These AI models can adapt to changing market conditions and incorporate new data without extensive manual intervention. This makes them highly dynamic and responsive. Interpretability: Traditional modeling approaches are often more interpretable, which enables an easier explanation of how predictions are derived. This can be a significant advantage in stakeholder communications and decision-making.
Non-linear relationships: Machine learning algorithms can capture non-linear relationships that might be difficult to represent using traditional programming. This makes them a versatile tool for diverse datasets. Customization: Custom features and calculations can be easily integrated into the model based on domain expertise. This allows for a high degree of personalization and specificity.
Predictive accuracy: Leveraging large datasets and sophisticated learning techniques, advanced ML algorithms can achieve high predictive accuracy Transparency: Traditional modeling approaches allow for clear and explicit logic, making it easier to spot errors or bugs. This can enhance the reliability and trustworthiness of the model.
Cons Data dependency: ML models heavily depend on the quality and quantity of data. Poor or biased data can lead to inaccurate predictions, making data collection and cleaning crucial. Manual feature engineering: Manually selecting and engineering features can be time-consuming and might overlook complex relationships. It might also introduce biases as the model will try to predict something based on what the modeler thinks is important, which does not necessarily capture the full reality.
Complexity and interpretability: Some AI-based models, especially deep neural networks, lack interpretability, making it challenging to explain predictions to stakeholders. This can be a significant hurdle in industries where transparency is paramount. Limited complexity: Conventional models might struggle to capture complex and non-linear patterns in data. This can limit their effectiveness in dealing with complex, real-world datasets.
Overfitting: ML algorithms can overfit training data, leading to poor generalization of new data if not properly regularized. However, this can often be diagnosed using the test set. Limited scalability: Manually created models might not handle large datasets efficiently. This can pose a challenge in today's data-rich world, where scalability is often key to success.

As you can see, both machine learning models and traditional statistical models have their strengths and weaknesses. The choice between the two often depends on the specific requirements of the task, the available data, and the expertise of the modeler. 

However, with the increasing availability and sophistication of machine learning methods and tools, such as Akkio, more and more businesses are turning to AI for their house price prediction needs.

The risks of over-reliance on machine learning for house price prediction

While ML algorithms bring a new level of sophistication and accuracy to house price prediction, it's essential to remember that they should not be relied upon blindly or exclusively. 

Instead, they should be used as one tool in a broader toolkit that includes human expertise and judgment to mitigate stakes, such as:

  • Struggling to adapt to sudden changes or shifts in the real estate market not present in the training data. Such unprecedented events can skew predictions, leading to inaccurate results. 
  • Focusing on correlation, not causation. The predictive relationships they learn from data might not accurately reflect the true causal factors affecting house prices. This can lead to models that are highly accurate in their training data but fail to generalize well to real-world scenarios. 

A case in point is Zillow and its Zestimate algorithm. This algorithm, used in conjunction with the Zillow Offers program, made cash offers to homeowners looking to sell. Despite the AI model’s predictive accuracy reportedly being over 96%, the program was a massive failure. It lost the company around $500 million and was shut down after just eight months. 

Zillow CEO and co-founder Rich Barton acknowledged this, stating: 

“The unpredictability in forecasting home prices far exceeded what the company had expected.”

This serves as a stark warning that while AI models can provide valuable insights and predictions, they are not infallible. They should be used in conjunction with, and not as a replacement for, human expertise and review.

Getting started with machine learning for house price prediction

For realtors and real estate businesses keen on harnessing ML-based models for house price prediction, the journey might appear dismaying. Thankfully, with the right tools and some basic understanding of key concepts, you can harness the power of machine learning to your advantage. 

Choosing the right machine learning platform for your needs

The first step is to choose an ML platform that is cost-effective, easy to integrate into your workflow, and user-friendly even if you don’t have extensive experience with machine learning. 

That's where Akkio comes into the picture!

Akkio is an ideal ML platform that transforms complex data into actionable insights and makes accurate predictions without the need for coding knowledge. Its no-code application offers many off-the-shelf tools that can automatically learn and adapt to the problem at hand. 

This is a significant advantage over more code-intensive data science practices, such as linear or polynomial regression techniques, where you need to decide which variables are important, how much regularization to use, and so on. 

Akkio eliminates the need for these in-depth techniques. With Akkio, you can:

  • Build, train, and deploy predictive machine learning models without writing a single line of code. 
  • Connect your datasets (or upload them in CSV format), set the right parameters for your model, and extract insights all from a user-friendly interface that simplifies the entire process. While you'll still need to know how to work with data and select the right features to train your model, Akkio makes the process much more manageable.
  • Deploy your AI model to a custom web app or any endpoint that may already be integrated into your infrastructure. This flexibility makes Akkio a highly versatile tool for a wide range of applications.

Understanding feature selection for house price prediction

When it comes to building an ML model for house price prediction, one of the most paramount steps is feature selection. Choosing the right features can significantly improve the accuracy of the model and make it more effective. 

But what exactly are these features, and how do you choose them?

Features are essentially the variables or attributes that your model will analyze to make predictions. For house price prediction, these could be a range of factors, each with a potential impact on the value of a property. Here are some examples:

  • House Price Index (HPI): The HPI is a measure of the overall change in property prices over time in a specific market or region. It provides an indication of the direction and magnitude of price movements in the real estate market, reflecting broader market trends and dynamics.
  • Location: Location is a key factor in determining property value. Features related to location include neighborhood, proximity to amenities (schools, parks, shopping centers), and accessibility to transportation.
  • Property size and structure: Attributes such as total area, number of bedrooms, number of bathrooms, and the overall layout and design of the property can significantly impact its value.
  • Property condition: The condition of the property, including its age, maintenance history, and any recent renovations or upgrades, can influence its value.
  • Amenities and features: Special elements like a swimming pool, fireplace, garage, and energy-efficient appliances can add value to a property.
  • Comparable sales: Historical sale prices of similar properties (comps) in the area can provide valuable insights into the current market value.
  • Market trends: Factors like overall real estate market trends, supply and demand dynamics, and economic conditions like GDP growth, interest rates, and consumer behavior can affect property values.
  • Property tax information: Property tax assessments or historical tax rates might provide indicators of value.

While choosing the right features for your model can be a convoluted task, Akkio can assist with this process. Akkio provides a spectacular feature – Chat Data Prep – that lets you preprocess and clean up your dataset as easily as chatting with a friend.

The Chat Data Prep feature in action.

Even better, Akkio’s Chat Explore functionality enables you to explore your dataset and conjure up quick insights in only a few seconds, including data visualization.

Alt text: A demo of the Chat Explore feature.

Akkio also allows you to pick the features that you want your predictions to be based upon from its simple GUI, and configure your model settings for enhanced accuracy.

Easy feature selection using Akkio.
Configuring the ML-based prediction model in Akkio.

Once that training phase is complete, you can deploy your ML model and even get live reports of its performance that can be shared seamlessly across your team.

Akkio’s ML prediction summary.‍
Akkio’s ML prediction report sample.

Note: The housing price prediction dataset used for this demonstration was obtained from Kaggle.

Predict house prices more accurately with Akkio

The journey of predicting house prices has evolved significantly with the dawn of machine learning and AI. ML models offer numerous advantages, such as automated learning, adaptability, and the ability to capture non-linear relationships. 

However, they also come with their challenges, such as data dependency, complexity, and the risk of overfitting. The key is to use these models as tools to enhance human expertise, not replace it.

Akkio stands out as a superior platform for anyone looking to leverage machine learning for house price prediction. Its no-coding-required approach democratizes the use of advanced AI, making it accessible even to those without a technical background. 

With features like predictive modeling and live charts and reports, Akkio makes the process of building a machine learning model more efficient and user-friendly.

Ready to get started? Harness the power of machine learning and transform your house price prediction process with Akkio today to make more precise and informed predictions!

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