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.
Combining deep learning and computer vision algorithms, the Quantarium Valuation Model has become the highest ranked automated valuation model in the US. Further, their quality image data, accurate classification and feature detection capabilities, a massive footprint data lake, analytics services, and more, Quantarium serves a range of industries connected to real estate, including marketing, insurance, proptech, and banking.
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.
PipeCandy eCommerce Leads & Insights for Fulfillment tracks company data and company shipping details: shipping volume, which companies they use to ship products, whether they ship internationally or not, and so on. You can find company leads easily with this dataset with filter capabilities.
Wikiroutes Transit Data provides public transport information—routes, stop points, and more—via crowd-sourcing. The data is constantly updated and can be easily converted and integrated into your own software system.
Wikiroute’s Transit Data is used by individuals, private companies, and government agencies of all types and sizes.
TrackStar’s Predictive Credit Technology uses fifteen years of financial dispute data to create predictive models of future borrowing potential. With this data and AI technology, your bank or other lending company can mitigate the risk of fraud, improve existing customer relations, and reduce your operating costs.