Author: EIC | Economic Intelligence CenterPublished in Bangkok Post/Asia In Depth: Asia Focus section, 3 July 2017
Traditionally the answer to the question of the three most important things in real estate has been ‘Location, Location, Location’, but in the current Big Data era a more accurate answer might be ‘Location, Demographics, Behavior’. Those involved in real estate today should now consider using the extremely large data sets known as Big Data to analyze consumer behavior by converting multi-dimensional data assets into useful information. For future real estate trends, Data analytics and Artificial Intelligence (‘AI’) present new business opportunities in real estate technology that can provide useful insights raw from data, helping individuals and corporates visualize properties through a variety of prisms.
Location Intelligence (‘LI’) is redefining real estate, taking it from involving just buying a property to being more about lifestyles. Today's home buyers are changing the way they choose homes, considering more than just location, physical home features, or prices. Location intelligence could transform real estate industry for home buyers or investors by determining a wide range of contexts related to a property, empowering users by letting them foresee traffic levels for commuting to/from work, employment rates, average neighborhood income, crime rates, or perhaps even the risk of natural disaster. For instance, Starbucks in US has adopted data-driven location analytics for optimal site selection for its lifestyle stores. According to a Location Intelligence Market Study 2016 by Oracle, respondents in the USA and Europe composed the largest market share for these services, around 87%, while Asia accounted for only 10%. However, there is high growth potential for the Asian real estate market given the fact that the size of the market in Asia is predicted to grow by more than half of the global real estate market by 2030.
Chatbots are another type of strategic tool for real estate agents that can help agents handle initial contacts and screen potential clients. “Trulia bot for Messenger” from well-known US online real estate service agent Trulia, assists clients with daily rental listing updates matching their criteria. Clients can also ask the Trulia bot for a property’s community information, which will show them crime stats and interesting demographic details, like median age, number of people who are single and married in the area, and how many homes in the neighborhood are renter-occupied or owner-occupied. In banking, chatbots can answer client questions related to home loan products. After OCBC Bank in Singapore introduced “Emma”, a chatbot specializing in home and renovation loan services, more than 10% of the chat sessions were converted into OCBC mortgage loans. The global market for chatbots is expected to grow from 628 million dollars in 2016 to 7,900 million dollars by 2024, or around 32% each year. Again, the Asia-Pacific market is on course for the rapid implementation of the technology due largely to the massive smartphone or internet user population, led by China and India.
Window shopping for houses is another effective way for home buyers to pre-screen a property before checking it out in person. The Kunversion Platform from U.S. brokerage firm Inside Real Estate leverages powerful image-recognition algorithms. This platform allows home buyers to view and receive live property updates from multiple listing services based on more than just common search criteria - they can even see similar properties based on how a home appears. The Sign Snap application that was developed by U.S. Real estate agent Realtor.com provides prospective home with information such as square footage, number of bathrooms, or the size of the backyard. Clients access the Realtor.com database by snapping a photo of the home’s for rent or for sale sign with their phone, and can then browse photos, property details, price data, and open house dates.
Beyond finding a new house, house valuation is a significant factor affecting home buying decisions. Home buyers try to search for the best locations and calculate returns if they want to sell their house in the future. Today many companies provide service tools that can accurately predict property values. Zestimate is a well-known predictive analytics platform from U.S. real estate and rental marketplace company Zillow. It uses a store of massive home sale data to predict the value of similar homes and estimated monthly rental prices. CoreLogic is another large U.S. provider of advanced property information, with an office in Singapore. They provide predictive analytics solution services that simulate data to predict home-price changes that can help investors track residential real estate trends and home price risk nationwide.
For future real estate technology, people searching for property can personalize their preferences through automated platforms using machine learning or AI. Intuitively, it not only facilitates smoother user experiences, but also the core dedicated services for interactions that continue to play a vital role for humans. However, the key insight is that the story behind the data allows more understanding on what happened in the past (descriptive analytics) leading to projections and plans that will influence future outcomes (prescriptive analytics). To do so, collaboration between either developers, brokers, banks and data scientists is essential because data scientists can only provide raw data, but never can explain its true meaning without data analysis. Thus, pairing with data scientists will be essential to creating synergy for real estate business intelligence.