How Will Artificial Intelligence Change The Commercial Real Estate Industry?
Since the start of the industrial revolution, the world has seen skills shift in a way that has resulted in ever-changing jobs and newer skills required to keep pace with emerging technologies. According to McKinsey, these technologies "will bring numerous benefits in the form of higher productivity, GDP growth, improved corporate performance, and new prosperity, but they will also change the skills required of human workers."
The ongoing adoption of AI and automation technologies will transform the way people work and cause a disruption, particularly in industries like real estate, finance and banking. I believe this wholeheartedly; I co-founded a firm that enhances workflows for commercial real estate companies using AI. But the research also backs this up: A PwC report predicts that AI could contribute over $15 trillion to the global economy from 2017 through 2030. Furthermore, Gartner found in 2019 that 37% of organizations have already adopted some form of AI into their operations.
The real estate sector is in a great position to leverage AI and automation technologies to increase productivity, reduce costs and minimize errors. The number of workers who are engaged in manual tasks that only require basic cognitive skills will likely decline. On the other hand, the number of technology and management experts will continue to grow as the demand for social and creative skills will increase dramatically.
A recent report by the Altus Group states that while 75% of CRE executives believe automation will eliminate jobs, 71% of respondents believe it will introduce new roles to the industry — in particular, roles that revolve around greater value-add tasks. The big challenge I see for companies is to be lean and adopt the latest technologies to keep up with this evolving industry.
Where can real estate companies best leverage AI?
• Marketing and sales: Companies could potentially use AI for lead management to display personalized promotions or messages to prospective buyers or lenders. AI could also be used for analyzing existing market data to recommend properties for brokers to buy or sell, based on the broker's past sales records and intent. Natural language processing (NLP) algorithms could be used to understand what matters to customers in a given zip code and what's unique about a given house from listings. Analysis of customer interactions during the lending or house-buying process would be useful for customer service analytics. AI-powered chatbots could also be used to answer any question from a potential prospect in a jiffy and make personalized offerings based on user preferences, without the need for a sales agent.
• Financial and loan modeling: Manual and repetitive processes, such as financial modeling and data entry, can be automated using extraction and classification technology, which would help real estate analysts save time, increase productivity and minimize errors. This would also help free up employee time to perform more value-added activities, such as improving customer service and performing creative tasks. In addition, due to pattern recognition abilities, AI can also be useful in forecasting risk and in manual property appraisal by taking into account factors like transportation and infrastructure improvements, changes in the environment, local neighborhood and other amenities that have an impact on property value.
• Data management: As a company grows, it has to deal with massive datasets of lease abstractions, property appraisal reports, offering memorandums, legal papers, tax reports, ownership history records, zoning regulations and so on. As the amount of data grows, AI could be used to classify and store documents and even proactively alert when a new appraisal is due or a property explodes in popularity, for example. Machine-learning enabled search could help people quickly analyze the massive amount of search data that exists by pre-processing and tagging useful meta-data for all property-related documents in a company's repository.
How can companies keep up?
According to the Altus Group report, two of the biggest challenges facing firms in collecting or utilizing more data to drive decision-making are a lack of internal capability and lack of appetite from the company to invest in the required technology. To solve for these challenges, consider a PwC report's digital transformation strategies for real estate organizations, which include these four steps:
1. Plan and strategize: Examine the effects of adopting technologies on your growth strategy, operations and workforce. Further, develop a plan for your organization's digital transformation.
2. Access customer and operations data and analytics: Utilize customer and operations data to drive your adoption to generate insights to drive growth and efficiency.
3. Explore potential partnerships: Explore possible technological integrations and partnerships that can help your organization fuel growth.
4. Focus on consumer feedback: Focus on customer interactions and feedback that would help you better understand technological trends shaping demand. This would help you create an environment of "innovative thinkers."
The time for digital transformation of the real estate industry is here. We are looking at exciting possibilities with widespread adoption and acceptance of AI as a powerful tool in real estate. Providing customized customer service, automating repetitive and manual tasks and proactive document management are just some of the tasks where AI will play a big role. Real estate companies adopting a "technology-first" attitude will be critical to sustaining and enhancing their competitive advantage to grow in this rapidly evolving industry.