Real estate decisions used to be impressionistic, not data-driven. However, this has changed with the advent of big data—in particular, buyer sentiment data available online.
People increasingly use the internet to find homes and investment properties. And it’s no wonder: the data revolution makes finding data on neighborhoods, real-time traffic estimations, areas of late night activity, noise levels, restaurants, outdoor activities, parks, and customer reviews easy to find, and eliminates some of the typical buyer confusion. The massive amounts of data on available houses online mitigates the risks of investing in a property with unknown history.
Realtors, investors, and home-seekers also use data analysis to predict risk and market trends with predictive analytics, fast property evaluations, market trend analysis, and location scouting.
Realtors use market data history to make accurate property appraisals and evaluations to set prices. Consequently, home buyers and investors can use the same data to put forward counter-offers of their own.
Not only are vast amounts of data available to be evaluated, but machine learning models allow these evaluations to be made in minutes. As a result, realtors can move on current consumer trends faster.
Finally, realtors and investors use geospatial data to collect and display location information. In this way, house hunting and location scouting become much more accessible.
AI holds great promise for the field of real estate; it has already changed so much about the way agents, mortgage lenders, developers, and home-buyers work.
Machine learning programs process large amounts of data to evaluate home values and rental prices. These programs use geospatial, traffic, and neighborhood data as well very detailed information on house data, like square footage and upgrade details.
In fact, value appraisals and rent prediction models can even collect data on how much vegetation is on the lawns in the neighborhood.
Realtors use deep learning algorithms to both identify and market to potential buyers and renters. These algorithms track and measure online activity—particularly social media activity—to find the most relevant leads.
Underwriting based on risk calculations can be evaluated with AI, saving investors and more a lot of time working on this issue.
Developers also identify construction sites with AI platforms that specialize in hyper-local zoning regulations.
Zillow uses AI to transform digital photos into property value estimates (“Zestimates”) for free. More than this, the Zillow AI program predicts property values with almost no errors.
Compass’s AI alerts real estate brokers the moment a potential lead’s online behavior indicates they are most interested in buying a property. The program even drafts emails for agents to send to customers.
Rex uses online and social media data to market properties to potential customers.
Stirista offers data that politicians and advisors can use to reach out to constituents and donors. Stirista has (so far) 150 million registered voter details sorted into 360 data points, including donation history. With this contact, demographic, web, and behavioral information, Stirista Political Data enables targeted advertising and outreach.
Stirista can also provide historical campaign data at all levels to help politicians plan effective political strategies.
Exante Global Flow Analytics supports alpha generation and risk management by extracting comprehensive price signals from detailed capital flow analysis. Exante complements hard data and raw model outputs with timely, narrative-based content, focusing on key global thematics and risk scenarios. Additionally, Exante maintains dialogues with their clients, providing bespoke coverage and service.
Acxiom Infobase provides customer insight data for targeted marketing campaigns in a wide variety of industries