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Machine Learning in Real Estate- 5 Use Cases

machine learning in real estate

Buying a new property involves both financial risk and emotional quotient. Earlier, people used to spend a lot of time to shortlist and finalize their dream property. But in the era of digitization, the entire process has become highly streamlined, automated and accurate. The real estate business is much more than just a ‘perfect house’ in an exotic location. It deals with huge volumes of data in terms of buyer/seller preferences, financial and risk-taking capabilities, etc. The real estate business is leveraging the power of machine learning to reap tangible benefits. By embracing emerging technological trends the industry is able to cater to the rapidly changing consumer demands.

Machine learning in real estate

Machine learning in real estate is refining the home search experience and improving the prediction of future property values. In this blog, we discuss how machine learning is transforming the real estate industry in different ways.

Machine learning in real estate finds several uses, including automating tasks to independent decision making. Keeping pace with the rapid growth and market expansion, companies are adopting latest technological advancements for better customer service. Presently AI capabilities are limited but in the near future the way property owners, tenants, and sellers operate will undergo a sea change.

Below we talk about the 5 ways machine learning is influencing the realty business.

Table of Contents

    1. Personalized property recommendations

    2. Enhanced data management

    3. Predictive analytics

    4. Interior designing

    5. Automated property valuation

The way ahead


5 ways machine learning will impact the real estate business

1. Personalized property recommendations

It’s really tough for real estate agents to find the desirable properties for their customers. This process of providing multiple property choices is time-consuming and exhausting. Machine learning in real estate makes this process seamless by offering the perfect set of properties using a large amount of data collected from various sources. ML algorithms pick up information through customer conversations and churn out the most suitable real estate options. This makes the job of realtors really easy as they need not struggle with the location, budget, and other criteria of prospective property buyers.

With machine learning, robust recommendations are provided to clients depending on their behavior and buying patterns. So customers get to see only those properties, which they wish to see. ML-based real estate platforms use algorithms to provide personalized property recommendations to their customers at optimum prices.

2. Enhanced data management

Estate brokers have to deal with huge chunks of data, including property appraisal reports, legal papers, sales details, zoning regulations, etc. As the broker is on the verge of finalizing a contract, the documentation increases and so does the data. Now, lease, partnership, and non-disclosure agreements are also added to the existing list of mandatory documents. Though in today’s scenario everything is digital, still a human presence is required to deal with massive data. Several companies use ERP, ECM, and other systems to solve data-related issues, which makes them more effective and efficient.

Machine learning has added a whole new dimension to data management by making it automated and involving minimal human intervention. Machine learning ensures that the data collected is accurate and authentic through continuous analysis. If any inconsistency is observed, such as invalid characters, missing signatures, vacant fields, etc, the machine learning-powered tool notifies it. This helps to securely store data without any replication and streamlines data management in the real estate business.

Related: Download the checklist that lays out everything you should consider  before implementing a machine learning project or workflow →

3. Predictive analytics

Machine learning models can make relevant predictions based on customer priorities. Suppose a customer is looking for a property in a specific geographic area, the model can predict its price based on physical changes or future infrastructure development. An ML model can also predict whether a particular locality is prone to crimes or thefts, which is an important criterion for those buying or renting apartments. Customer relationship management systems, powered by machine learning can analyze whether an individual can default on their EMI payments or face cheque bounce issues.      

Some predictive analytics techniques used by real estate agents include heatmap analysis, projected revenue charts, property rental calculators. These tools help in analyzing the property’s future performance and the ROI associated with it.

4. Interior designing

Machine learning in real estate is changing the way people decorate their homes. Advanced deep learning algorithms can automatically create customized interior designs. You just need to click your room’s photo, fill a quick online questionnaire, and the ML model will generate several beautiful images to creatively decorate your space. There are AI tools that accurately measure a particular space and suggest / recommend furniture that fits perfectly.

Nowadays, AI-based home design tools enable you to create exquisite interior designs and intelligent floor plans without expertise in design software.

5. Automated property valuation

Determining the property value in real estate has always been a challenging task. Present appraisal techniques are mainly based on earlier selling price but fail to take into consideration other factors that contribute towards property value such as environmental changes, infrastructure improvements, transportation costs, etc. Machine learning-based tools help in resolving this issue by calculating property value keeping in mind all the above-mentioned factors. This gives accurate price estimates to brokers who can then make a steady initial offer to prospective buyers adhering to market standards.

The way ahead

Machine learning and AI have influenced healthcare, banking, and other sectors. It’s slowly creeping into the real estate market and posing a ‘broker vs bot’ challenge. Time will tell who wins the race but machine learning is here to stay and rule the real estate business. If you want to be part of the ongoing technological momentum, switch to our no code machine learning platform Skyl.ai and achieve efficiency.

Try out the demo to automatically classify your real estate photos with Skyl.ai inference API.

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