Thanks to AI and ML, a traditionally conservative industry like real estate is becoming more innovative.
How?
AI and ML are driving innovation in real estate investing. “The marketing said that this pool felt like a lagoon. There is nothing lagoon-like about it but the bugs. Two customers make the sarcastic remark in a classic scene from American Beauty moments before Carolyn, the movie’s co-protagonist who works as a real estate agent, has a breakdown.
Few industries in the world are actually fueled by “human fa
more than real estate, which seems to breed a certain conservatism and a lack of enthusiasm for change. This industry was ranked as the second least digitised by the Morgan Stanley Digitalization Index just a few years ago.
But when the world changes more swiftly, it may be useful to use technology to compete in a crowded, rapidly changing market like real estate. Let’s look into the potential that digitalization, particularly the use of AI and ML in the real estate industry, may bring about.
How may AI be used in real estate?
Numerous solutions utilising AI are employed in the real estate industry to find the greatest investment opportunities, swiftly match buyers and sellers, automate property upkeep, and cut costs.
ML makes use of powerful self-learning algorithms to analyse this data, show the dynamics of the real estate market, and support decision-making.
Software bots driven by AI can automate and expedite a variety of office and marketing tasks
Case studies of AI and ML applications in real estate How can automation, analytics, and data extraction be done to the utmost potential using AI-related technologies? The three activities below in the real estate industry are about to undergo profound change as a result of machine learning and artificial intelligence.
1. Property assessment
For a real estate transaction to be completed successfully, a comprehensive evaluation of a property’s value and potential rental price is essential. Historically, this work was accomplished by combining “feel,” knowledge, and sometimes shaky data.
A market where individual ideas and tastes, as well as more general trends and dynamics, can be fairly mysterious and challenging to understand, makes such a focused investigation insufficient. These are the two scenarios in which AI and ML are relevant:
Data collection: AI-based cognitive technologies can acquire more sorts of data from more sources, increasing the amount of variables we take into account when doing our analysis. For instance, computer vision is used to scan satellite pictures, real estate photos, and other visual content. On the other hand, NLP can be applied to spy through social media, online articles, official reports, competitors’ marketing on real estate platforms, and more.
Data analysis: Machine learning algorithms can examine the aforementioned datasets, identify a more comprehensive set of variables and the common relationships or patterns between them (including underlying dynamics that “mere humans” would be unable to identify), and forecast how such metrics may affect the value of a real estate asset.
Realtors were able to keep an eye on so-called non-traditional data thanks to the deployment of machine learning-based data analytics tools, which, in McKinsey’s assessment, accounts for 60% of our predictive abilities. It’s crucial to take note of the expansion of the data sources and examined parameters. To provide a comprehensive picture that incorporates information on the properties, the legal and market status of an asset, financial and market developments, social factors (such as
2. Marketing optimization
The real estate market would become immovable—like a piece of real estate—without a useful tool to spot potential deals and open a line of contact between sellers and investors. AI and ML once again make a huge contribution by streamlining several marketing-related tasks: – Targeted advertising: The real estate sector can make advantage of this well-known machine learning marketing technology that is popular in e-commerce and many other sectors. Based on user data, browsing habits, and engagement on social media and online platforms, customers are categorised into categories. The most pertinent real estate advertisements are then directed at these groups based on their demands.
Chatbots: These persistent assistants provide potential customers with 24/7 support by replying to questions in natural language using AI.
Chatbots: These tireless assistants, powered by AI and natural language processing, provide potential clients with 24/7 help by immediately answering to questions and giving useful information on the most exciting real estate deals in a specific location.
Marketing automation: Bots can also automate a range of marketing tasks to broaden their reach, including the building of mailing lists and the posting of social media updates and other types of content.
3. Control over property
The investment process doesn’t end with the simple acquisition of a real estate asset. Instead, it calls for ongoing management and upkeep tasks to increase long-term profit and lessen any potentially negative ownership repercussions. The subsequent steps can
Predictive maintenance: Machine learning-based systems can gather data from sensors and cameras, identify deviations from the electrical system’s or any other infrastructure element’s typical functioning patterns, and then report such anomalies to management for prompt interventions. These systems work in conjunction with IoT devices scattered throughout properties. A similar procedure can be used to monitor and then improve energy use.
Fraud detection: Machine learning algorithms can identify fraud attempts, which are a different but no less damaging type of abnormality. Cross-checking bank transfers, criminal histories, and account data in search of anomalies can frequently uncover fraud efforts. Reduce business risk; tenant onboarding and KYC procedures are essential to achieving this. Finance & accounting: Similar to marketing automation, a variety of time-consuming clerical tasks can be carried out more efficiently and precisely by bots using RPA (robotic process automation) technologies and AI than by humans. A few of these include processing lease agreements, dealing with residents, cost management, consumption-based billing, tax reporting, NAV calculations, etc.
Afresh tech momentum from conservatism
The real estate sector has evolved over time to include a strong human and relational component in addition to the evident desire of profit, which may have hindered the digitization trend experienced by other industries. So much so that, according to a 2020 research by the National Association of Realtors, 41% of American sellers still choose a realtor based on recommendations from friends and family, while 89% of sellers in the country actually used a real estate agent to sell their house.
