Whether from leasing agreements, credit documents, insurance forms, regulatory building codes, online and mobile app activity, or IoT device sensors, real estate businesses are inundated by data. A trove of vital information about who we are, how we live, and what we want resides within these documents. The difficulty, however, is in the collection of this information, and combining them to form a bigger picture to support business decisions and drive revenue.
By supporting the tedious process of data collection, cleansing and analysis, AI is driving revolutionary change in the way the real estate industry operates. Machine and deep learning, algorithms and predictive analytics are supporting real estate businesses as they improve their property forecasting and investment decisions, get a clearer understanding of customer needs, automate time-consuming, repetitive tasks, assist with facility management functions and conduct self-service showings. AI is even enabling property developers and governments to design more efficient, smart buildings and cities.
Property sales and marketing
Success in real estate was once predicated by an agent’s personal knowledge, gumption and experience gained over time. Today however, AI tools are helping to give agents a head start by enabling better access to data and statistics across a wider range of data points, as well as helping them to cultivate leads, pinpoint valuations, and better meet clients’ needs. Thanks to advances in AI, many time-consuming and repetitive tasks such as providing property details, scheduling viewings, finding leads, or managing the paperwork for a lease application, are now being reduced through automation and predictive analytics.
KELLER WILLIAMS INTRODUCES KELLE, AN AI-ENABLED VIRTUAL ASSISTANT
Keller Williams’ new virtual assistant (Kelle) is a first in the real estate industry. Similar to Amazon’s Alexa or Apple’s Siri, Kelle’s first iteration is designed to help agents focus on clients by taking care of a variety everyday business tasks.
AI bots may power intelligent search platforms, help to answer queries on lease terms or home details, perform virtual tours, or even help clients find appropriate office spaces. This provides a 24/7 service to support lead generation.
AI will enable brokerages and agents to send more personalized, dynamic newsletters and new listings content to subscribers based on their property or area of their interest — improving chances of subscribers opening and reading the content, and driving conversion rates.
By using regression analysis data about similar homes, automated valuation models (AVMs) are able to provide realtors with concrete evidence to present to owners to ensure their properties are correctly priced and not left to sit on the market indefinitely without an offer. Not only will they enable data driven appraisals, AVMs may also be used to calculate insurance quotes and mortgages.
Property management and investment
In property management, multiple parties are often required to look through each document whether for legal, credit, or insurance purposes. Deep learning helps to extract relevant information that can then be usable at any given time. It supports automated data processing by collecting unstructured data from various types of documents and different formats, and converting it into structured data that can then be shared and understood universally for different applications, in different languages. This makes it easier, for example, to manage international portfolios, and provide information consumable by any business unit whether for credit, insurance, legal or marketing and sales.
CTO & CO-FOUNDER FERNANDO HIGUERA DISCUSSES JACK, AN AUTOMATED CONVERSATIONAL AI TOOL FOR PROPERTY MANAGEMENT
Jack is a conversational platform for automated messaging and voice experiences with tenants/residents for all property types and portfolio sizes.
Design, construction and augmented reality
VR and augmented reality is a fast growing area with significant demand in both work and leisure spaces. One key pain point in VR however, is the processing of unstructured data such as 3D images and videos. AI learns and helps to accelerate the process of cleansing such data, and translating them into information that can be understood by other applications. This will allow product designers, architects, developers, property owners and buyers to visualize and interact with their real world spaces in new ways.
THE FUTURE OF HOUSE HUNTING
As the IoT trend picks up momentum, sensors in our physical space will become commonplace, making more data about how we live, play and work available. AI will be essential to automating both data cleansing and the running of intelligent building systems to boost cost and energy efficiency.
Smart buildings will be able to power building automation systems to control temperatures, talk to autonomous cars, share power with other buildings and more. In addition, a retailer, for example, might be able to know how much foot traffic occurs at any given time in a certain section of a store, and use this information to improve staffing levels or merchandise layouts. As more property developers begin to adopt AI technologies, smart cities are likely to become the norm, with the physical environment learning and adjusting itself to what is needed. This might reduce our reliance on power grids, improve air quality, reduce traffic and wastage of precious resources.
EVOLUTION OF SMART BUILDINGS AND THEIR PLACE IN THE INTERNET OF EVERYTHING
Jeremy Towler provides an explanation of what a Smart Building is and how it can be integrated within everyday life. He also looks at the smart grid, the information dimension and the need for skills development.
The rapid advances and widespread acceptance of AI technology will not only provide operational cost-savings and efficiency benefits, it will provide a dramatic competitive edge in terms of service and speed, and reduced resource wastage.
Ultimately, this represents both a huge opportunity for early movers and startups, and a significant threat to traditional companies and realtors who are too slow on the uptake to stay relevant amid new market conditions and customer expectations.