Feature
Artificial intelligence (AI) is continuing to change the way real estate is bought, sold and managed. Investors in the industry have “mountains of both proprietary and third-party data about properties, communities, tenants and the market itself,” according to a McKinsey report. Accessing and applying data are where AI excels. AI can help real estate investors identify opportunities, speed up building and interior design, streamline marketing and help customers find what they need. Here, we look at several case studies on AI in the real estate sector.
1. Zillow
Zillow is a source of real-time real estate data for tens of millions of buyers and sellers every day. One of the website’s most popular features is its Zestimate, a real-time estimate of a home’s value. Zillow uses a large amount of data — public records, tax assessments, sales, images of the home, MLS listing data and more — to rapidly calculate this estimate.
The company built the algorithm behind Zestimate before most big data systems existed, and, as the industry evolved, it became increasingly difficult to keep up with the demand for fast, accurate estimates, according to a case study. It could take days to deliver a single, accurate home-value estimate to consumers, which was not fast enough.
Zillow moved to AWS to solve many of the backbone problems it was experiencing with the old system. The company used Amazon Kinesis to ingest the vast stores of data, run machine learning (ML) models on it and return nearly real-time Zestimates to customers.
“We can compute Zestimates in seconds, as opposed to hours, by using Amazon Kinesis Data Streams and Spark on Amazon EMR,” says Jasjeet Thind, VP of data science and engineering, Zillow.
“As a result, the Zestimates are more up-to-date and accurate, because they’re built with the absolute latest data.”
Results
- Didn’t have to manage and scale a fleet of servers
- Used the system to recommend houses to home buyers
- Used the system to target ads to home buyers
2. Houseal Lavigne
Houseal Lavigne is an urban planning company that creates high-fidelity, 3D viewing experiences that allow customers to experience the potential of a piece of land. Building these immersive experiences was once a time-consuming endeavor.
Creating an urban design concept from idea to client-ready took the company’s team of designers months, according to a case study. They had to capture and create a large set of details, so the customer could explore a realistic experience inside the 3D model. Viewers needed to see everything, from streets to the windows on houses, to believe in what they were experiencing.
To speed the creation of its signature immersive and interactive experiences, Houseal Lavigne turned to NVIDIA Omniverse Enterprise. Using Omniverse, the team created better and bigger experiences — with higher resolution and more detailed 3D illustrations that contain more assets — in hours.
“We were working on a recent development project around a Chicago suburb, and I was able to complete the project in four hours — from start to finish," says Devin Lavigne, principal and co-founder, Houseal Lavigne.
“Using the ArcGIS CityEngine connector, we bring our city planning geometry and data into Omniverse and incorporate building design data from Sketchup and materials and textures from Adobe Substance Painter. We can then stream this combined output to our clients using USD Presenter for interactive review — delivered in high-fidelity, real-time, path-traced rendering.”
Due partly to the advanced AI tools, the company appears much larger to potential clients and stays ahead of bigger competitors, in terms of the presentation experience it delivers to developers.
Results
- Improved communication and collaboration with clients through interactive reviews
- Improved decision-making process by clients
- Improved workflows on design projects with hundreds of assets and materials
3. JLL
JLL is a global real estate services firm that specializes in commercial property management and investment. The firm needed to help a client, a Fortune “500” professional services company, manage the flow of people into the many floors of the Chicago office tower it occupied.
With a hybrid workforce, the tenant was under-utilizing its space. The tenant wanted a vibrant office dynamic. The previous static tools it used for space allocation were slow and inefficient, according to a case study.
JLL developed an AI-enabled planning tool to process a large amount of data to suggest efficient space-use assignments. The tool collected utilization data, documented space-use constraints, such as reception areas and mail rooms, and integrated the expectations and disparate work styles of the workforce. The solution was designed to assign space that best accommodates employee preferences, energy and building maintenance constraints and the need for collaborative grouping of teams. It also worked dynamically to support changes.
JLL says it uses “new occupancy management methods for the hybrid workplace along with artificial intelligence to restore workplace vibrancy and better utilize space.”
Results
- Integrated a large range work expectations and styles in space assignments
- Made space assignments more efficiently and quickly
- Reduced building maintenance and energy costs by closing unnecessary space
4. Redfin
Redfin is a real estate brokerage firm that helps people buy and sell houses. The company has data on millions of users and hundreds of millions of properties.
Redfin worked with AWS to use AI, along with all of Redfin’s data and other AWS tools, to recommend homes to potential buyers, according to a case study. Redfin developed an AI-powered recommendation system called Redfin Matchmaker. The algorithm predicted, in real-time, which homes a customer may like.
“When Redfin recommends a home, customers are four times as likely to click on that house as they are on a home that fits the criteria of their own saved search,” says Bridget Dray, CTO of Redfin, at CIOReview.
This means that the AI was better at understanding what these home shoppers are looking for than the customers themselves. The AI could see the homes that are available, input from agents, what customers search and like and more.
Results
- Increased customer product engagement and click rates with recommendation technology
- Leveraged first-party customer data to develop solution
- Integrated multiple data sources for more complete data sets
5. NatWest Group
The process of home buyers obtaining a mortgage can be a complicated process for all parties involved. There is, for instance, a great deal of policy information that needs to be applied to each mortgage.
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NatWest Group, a leading banking and financial services firm in the U.K., worked with IBM to develop a customer service solution for its mortgage agents, according to a case study. IBM Consulting built an AI powered, cloud-based platform that gave mortgage call center employees real-time support. The system was called Marge and intentionally imbued with a personality, so team members treat her like a colleague.
While on the phone with a mortgage-seeking customer, members of the NatWest mortgage team typed keywords into a console that helped them answer customer questions and guide the interaction to find the right information and answers.
“Our colleagues have a level of confidence that they never had before, which, in turn, gives customers confidence in the decisions that they’re making for their future,” says MaryAnn Fleming, head of gomebuying services, NatWest Group.
Results
- 20% improvement in customer net promoter score
- 10% decrease in call times
- Adapted to changing industry regulations, products and processes
