Property Price Prediction Using Machine Learning

Real estate has ballooned to be worth a whopping $326.5 trillion globally. Yes, trillion, making it the world's largest asset class. Larger than stocks, shares, and bonds combined.

Powering this sector's growth is a host of cutting-edge technology including Artificial Intelligence. The biggest names in real estate are increasingly employing AI, to help look at thousands of data points to make accurate and fast property price predictions with more data than ever before.

Accurate property and land price predictions serve buyers and sellers alike to answer fundamental questions like:

• What is the best price possible to rent/buy?

• Where or when should I invest?

• Which property should I develop or purchase to maximize returns?

Even banks and insurers rely on accurate property valuations to make fair assessments to offer their services.

In this use case, you will learn how leveraging Machine Learning and more types of data can help buyers and sellers alike answer pricing-related questions.

So much publicly available data, so little time

Traditionally property valuation has been an imprecise science. Individual appraisers and valuers would review different data sources and call on their domain understanding of contributing factors and market conditions to arrive at a valuation.

The approach is riddled with issues. Often, the variation in value between individual appraisers is high, the process is slow and, in the age of data, where there are thousands of data points to analyze (and growing) it is no longer practical or even……optimal for a person to fulfill this role.

As such, leveraging the growing plethora of data and using machine learning (ML) algorithms to predict property prices is faster and more accurate than the traditional methods. This gives individuals and companies that take this ML route an edge in the ever-competitive real estate market; helping buyers, sellers banks, and insurers make more informed decisions.

The 3 commonly-used ways of valuating a property

There have been 3 property valuation approaches to consider: The comparison approach, the cost approach, and the income approach.

Comparison approach

Uses an estimation based on recently sold similar properties (similar locations and attributes) and infers the target value from the comparables.

Cost approach

Estimates how much it would cost to build the same property,

Income approach (Profits method)

Estimates the value of the property from the income that the property generates.

These approaches make estimations based on similar or comparable properties. While useful, these traditional methods provide merely a hypothetical value.

Given the volume and velocity of incoming data, the manual effort required to collect, compile and analyze is time-consuming. Often the amount of data used for these mostly-manual valuations is inadequate for a fair and accurate assessment.

With each transaction in the real estate market involving hundreds of thousands of dollars (and more!), accurate property valuation is paramount.

For a guide to the top 18 AI/Machine learning use cases, check out this article.

The Solution: Real estate price prediction using machine learning

Automating the pricing process using ML means less time, fewer human errors, and the power to take into account more data.

Using ML, thousands of data points from a variety of sources can be quickly evaluated to produce a predictive output of a property’s valuation.

With ML, we can look not just at the number of similar and comparable properties. But we have the power to consider more features and non-traditional data.

Disrupting traditional valuation methods with non-traditional data

You may be asking - what is non-traditional data? It's data, but data from new or novel sources. With leaps forward in collecting, indexing, and storage of data there are even more avenues for extending the performance of ML algorithms.

Often this data can represent features that give an indication of socioeconomic elements. This could be the ratings of restaurants and schools nearby or could even be IoT devices used in smart buildings, remote senses, or urban big data. This data, when combined with existing property data, can build a richer, more accurate picture of property prices.

How the AI & Analytics Engine can help you build a model to accurately predict property price

The AI & Analytics Engine is capable of analyzing large volumes of data, from different sources, then guiding users through the process of developing and deploying their predictive pricing model.

The process would loosely follow:

1. Ingest property data and non-traditional data into the Engine

2. Create a data preparation recipe. The recipe is a sequential list of data transformation actions used to get the data into an ML-ready format.

3. Then, use the automatically generated recommended feature set. This is generated by the Engine's AI and takes into account the features that are most predictive of your target column (price). Using the recommended feature set is optional you can also build your own.

4. Select and train your model. Use the recommended models (algorithms) listed by the Engine. Select a few to compare them prior to training. This saves time because only the models that meet the performance criteria need to be trained.

5. Evaluate and compare the trained models for how they perform against the target (price) within the test portion of the dataset (this train/test split is done automatically at the time of model training).

6. Select and easily deploy your ML model to the PI.EXCHANGE cloud, and begin making predictions by inputting new data.

Benefits of using the AI & Analytics Engine for property price prediction

With the Engine, no longer will agents, homeowners, and investors, need to use traditional methods to compare similar houses to predict the price of a property. Instead, using the Engine they can build their own property price prediction model, quickly and easily - no coding required. Benefits include:

• With AI & Analytics Engine, the process to get to predictions is streamlined and fast. Giving valuable time back to all parties involved;

• There is a very small learning curve with no pre-requisite data science and machine learning experience required;

• There is a reduction in the manual cost and efforts involved in valuing a property;

• By relying on a purely data-driven valuation, there can be greater accuracy, objectivity, and consistency in the valuation approach and;

• Valuating can take greater consideration to diverse non-traditional data for better outcomes.

With more accurate property valuations:

• Buyers can reduce the risk of overpaying for a property;

• Homeowners can sell their homes at the right price without misinformation;

• Investors can determine the risk of purchasing property;

• And appraisers and evaluators can build repeatable, accurate, and streamlined valuation methods to help inform their valuation practice.

The AI & Analytics Engine can help unlock the power of more traditional and non-traditional property data. This wave of high-quality data from non-traditional and traditional sources allows for the creation of new datasets, which in turn allows new levels of prediction methods used at scale in real estate.

The application of ML models can be applied not just to property valuations but also: Lease Renewal, Income Default, Expected Utilisation, and Upgrade Costs; Truly, the applications are limitless.