Roles of Data Science and Machine Learning in Real Estate

Real Estate is one of the booming sectors of the economy at the moment. It has received several investors in the past years, who believe there is quite a fortune to enjoy from their investment. This is true, in as much as the approach to Real Estate is of a high standard. Today, we are looking at the various Roles of Data Science and Machine Learning in Real Estate and we hope you learn from this today!

Over the years, researchers have come up with better methods to handle Real Estate management to make it yield better and more favorable results. These methods also make the whole process easier and quicker than usual. Some of these approaches are Data Science and Machine Learning.

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Data Science and Machine Learning have various applications in Real Estate today. I will discuss these roles and how they affect the Real Estate sector.

Roles of Data Science in Real Estate

The Real Estate industry has been pressured to unlock the potential of big data and integrate machine learning and evidence-based methods in their workflows. After research some time ago, it was discovered that Artificial Intelligence, Machine Learning, Big Data, Data Science, and Analysis would primarily impact the Real Estate industry the most in years to come.

So how does Data Science affect Real Estate? How can Data Science be applied to Real Estate? What are the benefits and roles of Data Science in Real Estate? Let’s get to know all of that.

1. Property Price Indices

The applications of Data Science in Investing and Finance are endless. Today, Data-driven computer models have accounted for over 80% of trading. In Real Estate, every reported transaction represents the exchange of a unique asset. Keep in mind that no two properties are similar or the same. It could be a transaction involving two units of the same building; they will still be significantly different; therefore, pricing must be different.

However, this can be a big problem for real estate. Here’s the catch; How is it possible to harness large data sets to comprehend individual sub-market performance, knowing they are not identical? If you consider taking the simple average of past transactions, it would be not very objective, significantly if the properties in the transaction vary.

Data Science helps provide several solutions to this problem. There is a method of Data Science called Hedonic Regression used in Singapore; it operates on the principle that individual characteristics of each property in the entity can be priced individually to regulate the differences across the assets.

This method is, however, not the only approach to data analysis. You can also compare price changes in properties sold more than once. This method is known as the repeat sales method; The US Case-Shiller indices are a famous example of this method.

The advantage of these methods is that they enable users to exceed human capacity. With these techniques, real estate workers can work on more data than one person could work on his own to produce clear signals of the property market’s performance.

Real Estate developers can combine thousand of rows of transaction data with information about demographics, property characteristics, locality, etc. The combination can be utilized to produce granular sub-market prices. Indexation helps find out historical trends, which is helpful in current estate pricing and the potential returns in the future.

2. Geographic Information Systems (GIS)

During Real Estate Analysis, one of the most important factors is location; and there are GIS tools such as Quantum GIS that help to analyze, visualize and understand locality intelligence. Unlike before, when Government data sources were very scarce, information is now readily available to people. Such data include population migration by neighborhood to a new location with public amenities.

One of the ways of solving problems using GIS is to load all property transactions for a given year and load all train station locations in the area, then determine which properties are within a specific radius/distance of the train stations. You can also test whether the listed properties have a higher pricing per square foot than those further from the train stations.

In addition, you can use GIS to determine commute times or locate properties matching specified criteria. GIS enables lots of factors, including site selections and competitor placement.

3. Cluster Analysis

Depending on location, Real Estate performance can significantly vary. Countries have different macroeconomics conditions, and so does the real estate sector. Even cities within the same country may change due to local factors, including supply and economic activity.

Furthermore, within a particular city, neighborhoods can also perform quite differently. Approach to Cluster Analysis can help you identify patterns in the data, which will help you find out the groups of properties that are most likely to perform similarly and which are most likely to achieve divergently.

There are many other applications of Cluster Analysis. One is to determine market periods when property performance might be more or less similar. The time when Real Estate property performance is most affected us due Government intervention. It could be significant changes in Real Estate law, which may cause price and investment performance to differ over time. With Cluster analysis, you can also predict the time when property performance is more likely to be similar.

In addition, Cluster Analysis helps you build targeted models for each group or period, thereby increasing accuracy. Business models can also use the approach to guide their strategies. This application works by determining the portions of the market that different teams should target or what investment period the market is likely to be in.

Roles of Machine Learning in Real Estate

Recently, the rate at which the Real Estate sector is adapting to new technology has been overwhelming. With the use of Artificial Intelligence and Machine Learning Approaches, Real Estate is helping improve the quality of work and simplifying core processes and methods.

Past Research data shows that Artificial Intelligence and Machine Learning could generate over $15 trillion in the global economy over the next ten years. Due to this, investors have started diverting funds into Real Estate, which has begun implementing these new technological approaches. Let’s see the roles of Machine Learning in Real Estate today.

1. Undervalued Real Estate Identification

As a Real Estate Developer, you can determine relationships and correlations by making use of divergent parameters and variables; since well-designed AI algorithms usually test the effects and possibility of various parameters, the insights gained can trigger what is called the “butterfly effect,” which happens when a small factor results into unforeseen variations and to large amounts that can result in a complex system in the future.

In addition, Artificial Intelligence can help determine the prices of properties with the predictive analytics algorithm. This kind of algorithm will help foresee the likelihood of certain events happening just by examining given information. Furthermore, this will determine the actual intrinsic value of a given property.

2. Building Maintenance and Cost Optimization

Real Estate has faced many problems for a very long time, and one of them is ensuring a high level of occupancy. To solve this problem, various solutions for artificial intelligence are being investigated to optimize rental and building maintenance.

The function of the final cost of building maintenance is rent, and the only way a landlord can keep his costs in check is to lower the rents they are to pay, thereby making them more committed to the facilities. With the help of Artificial Intelligence, Real Estate professionals can quickly identify the underloaded and overloaded areas in a building, thereby developing an optimal monetization strategy from a variety of data points/sources. Forbes has proven that Artificial Intelligence and Machine Learning can help save billions of dollars in Real Estate and even reduce costs by over 40%.

In the 2020 CRE Innovation Report, it was shown that Artificial Intelligence has tremendous potential for significant cost savings and operational efficiency up to 46% compared to the previous year. It is predicted that Artificial Intelligence will have a significant disruptive effect of up to 43% in subsequent years.

3. Qualified Prospect Generation

Some essential parts of the real estate cycle are identifying, managing, and closing a qualified prospect. Manual processes have mainly driven this process. With the advent of Artificial Intelligence algorithms, PropTech companies use such algorithms to complement the investment team, optimize their time allocation, and increase their conversion rate. Instead of chasing after every lead, Artificial Intelligence algorithms evaluate every authority based on thousands of data points and rank them in a conversion probability ranking.

Recently announced integrated AI tool, Astra, aims to accelerate home sales by using an algorithm that analyzes customer behavior data to generate accurate leads, increase marketing efficiency, reduce costs, and provide an engaging user experience for developers and their customers.

Conclusion: Roles of Data Science and Machine Learning in Real Estate

In concluding the statement on the role of AI and ML in real estate, Vir says the influence of artificial intelligence in real estate will only grow exponentially over the coming decades on real estate intelligence. With the advancing digitization of various industries, industry players recognize the immense potential of AI.

Image Credits: istockphotos

Written by Nwachukwu Richard

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