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Role Of Machine Learning In Real Estate Sector

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The real estate industry is demonstrably making advances in adapting to technology and is slowly moving towards a data-driven approach, with the use of AI and ML in enterprises invariably simplifying core processes and improving the overall quality of work. Research suggests that Artificial Intelligence could generate more than $15 trillion in the global economy in the next decade, and investors are taking note, and early stage investors in particular are increasingly looking for new businesses who develop technological solutions that facilitate processes.

Aryaman Vir, founder and CEO of MYRE Capital, shares his knowledge of how artificial intelligence and machine learning are revolutionizing the real estate sector and its advantages over algorithms for robotic process automation throughout the process.

The most recent large-scale technology introduction that has dramatically improved the efficiency of the real estate sector has been the integration and application of Robotic Process Automation (RPA) algorithms. The main limitation of RPA is that it mimics human behavior in the face of events that are deterministic in nature.

These algorithms are unable to deal with variable results. RPA solutions are largely based on predetermined events and actions and therefore depend on the breadth of the results and situations being considered, says Aryaman Vir. Technology that can complement RPA to mitigate deficiencies.

AI and ML involve the simulation of human intelligence processes through machines and automation instead of the simulation of human action through RPAs. Eliminate prejudice and subjectivity in the decision-making process. The fundamental difference is that RPA uses structured logic and inputs, while AI uses unstructured inputs and develops its own logic.

Vir adds, Artificial intelligence sounds futuristic, but the real estate applications of this technology are underestimated and are still in their infancy. Some of the recent areas in which AI and ML are being explored are:

1) Undervalued Real Estate Identification

Determine relationships and correlations across a variety of parameters and variables, since well-designed AI algorithms constantly test the effects and feasibility of various parameters, the actionable insights gained can trigger a “butterfly effect”, i.e. the effect in which a small factor leads to unpredictable variations and to a great extent that can result in a future complex system.

AI can help determine property prices using the predictive analytics algorithm. Predictive analytics algorithms determine the likelihood of certain events occurring by examining available information. This ultimately helps to determine the real intrinsic value of a property.

2) Building maintenance and cost optimization

One of the oldest problems in the real estate sector is ensuring a high level of occupancy. In order to optimize both parameters: rental and building maintenance, various solutions for artificial intelligence are being investigated.

Rent is a function of the final cost of maintaining a building. If a landlord can keep costs down, it usually results in lower rents for the tenant, making them more committed to the facilities.

With the help of AI, underloaded and overloaded areas in a building can be identified and consequently a wide range of data points can be evaluated in order to develop an optimal monetization strategy or cost reduction methodology. According to the Forbes Technology Council, using predictive AI can save billions of dollars, and reduce costs by up to 40%.

According to the CRE 2020 Innovation Report, artificial intelligence has 46% more potential for significant cost savings and operational efficiency compared to the previous year and will have a significant disruptive effect of up to 43%. Estate provides efficient property management, budgeting and forecasting, performance management, valuation process and transaction process.

3) Qualified Prospect Generation

An essential part of the real estate cycle is identifying, managing and closing a qualified prospect. Traditionally, this process has mainly been driven by manual processes. With the advent of AI algorithms, PropTech companies are using such algorithms to complement the investment team, optimize their time allocation and increase their conversion rate. Instead of chasing after every lead, AI algorithms evaluate every lead on the basis of 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.

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 are recognizing the immense potential of AI.

The introduction of AI will have a positive effect on efficiency, administrative costs, transparency and, above all, on the valuation of assets. The artificial intelligence revolution has arrived and will remain.

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