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70 BIG DATA AND BIAS "Research has long shown these computer models are loaded with biases and flaws," GeekWire co-founder John Cook said. Following Zillow's move, the consensus among experts is that iBuying firms are putting too much faith in machines to do what humans can do better. Models usually aggregate and average MLS listing data at the MSA or ZIP code level. But with scant supply on the market, there are few comps to consider. And, as we all know, location, location, location is what matters in real estate. Pricing today happens at the micro-market, neighborhood, and block level, if not at the individual house level, with a range of aesthetic, social, and other factors playing a role, such as natural light, intuitive layout, finish level, etc. "e system may capture that there are three bedrooms, but does it capture that they are laid out in a way that makes sense?" NYU real estate dean, Sam Chandan comments. Looking at suggested pricing based on city or town averages is highly inaccurate. You need qualified professionals with experience at the local level to assess pricing and offer customized expertise on localized buying patterns and preferences. A BACKWARD VIEW Another important consideration in Big Data models is the timing of the data. By the time it gets to the Big Data engine, data is backward-looking—anywhere from 30 to 60 or 90 days old. at may be fine for a stabilized market, but not for a fast-moving market like the present. "All the AI and machine learning in the world isn't yet up to the task of the complexity of valuing a home in a rapidly changing market," MoxiWorks CEO York Baur notes. e pandemic has forever changed the role of the home where we now work, eat, play, teach, and learn. Space has become critical and layout needs are dramatically different. "at shift in buyer preferences is extremely hard for a machine-learning model to incorporate," BiggerPockets data and analytics VP Dave Meyer notes. Cue Zillow's move to abandon the home- flipping business, a cautionary tale on the limits of Big Data. Its algorithms were just not able to account for the fluctuations in consumer needs and pricing that we have experienced over the past two years and to accurately predict future home prices and selling speed. Consumers are tired of shelling out 6% to an agent who may or may not provide any real value in the transaction—and spending too much time and money on a traditional listing. While iBuyers want to give consumers an easier solution, the danger lies in relying on a computer to decide how much a home is worth. iBuyers may have experienced significant growth, but the question remains if that can continue in a market that is volatile and rapidly changing. THE RIGHT STUFF So, what role should data play in a real estate decision? At New Western, we believe that data should help accelerate comparative market analyses (CMAs) but that local agents must serve as feet on the street to provide hyper-local intelligence and insight to buyers and sellers. Many experts agree with us: "What this says to me is that we need to stop over- applying technology in an effort to replace humans, and instead focus on applying technology to make humans better," Baur notes. We do see a place for Big Data and AI/ ML in assessing opportunities for our agents. ese technologies can be combined in a way to find matches between seller opportunities and buyer preferences that result in the most likely candidate rising to the top of the list. Big Data has an important role to play in lead, opportunity, and deal scoring. FACE THE FUTURE A recent KPMG study shows growing interest in digital transformation in real estate—from cost efficiencies to enhanced decision-making. Beyond pricing, data can be used to track demographic and employment trends and help developers identify and develop compelling properties. Add to that apps that use data to project potential income and earnings from a property. "A developer can thus quickly access hyperlocal community data, paired with land use data and market forecasts, and select the most relevant neighborhoods and type of buildings for development," reports McKinsey & Company. At New Western, we too leverage data science analytics to look at predictive indicators and assess opportunities for market expansion. But we still rely on people to dig deeper and make the final decision on where we'll expand next. Big Data can also play a role in how properties are marketed. Agents can use search engine and advertising data to refine and target relevant audiences. e sales process is yet another area where data can be used to create models measuring visitor interactions on competitor websites in addition to tracking interaction with advertising. Data can also be analyzed to evaluate buyer preferences and strength by looking at credit scores, mortgage pre-approvals, and other public records. While real estate is certainly ripe for disruption and applications like this, many variables must be factored for pricing models to become more effective. e challenge will be how to identify predictive indicators to assess markets—and the current market volatility is going to make that difficult for the time being. ere is a huge role for technology and automation but it's not the primary one. People will always come first. Information is one piece of the real estate puzzle. But it will always require humans to assess the data along with personal instinct, intuition, and experience to make educated business decisions. Data is a means to the end … it's not the end. Darren Bordeaux is CTO of New Western. Feature By: Darren Bordeaux There is a huge role for technology and automation but it's not the primary one. People will always come first.

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