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Industries like real estate - which are tasked with finding qualified clients in a crowded and ever-changing market - have a lot to gain from adopting machine learning and AI in real estate prospecting. With Real Estate Machine Learning, companies can achieve unparalleled real estate marketing efficiency. However, the large amount of real estate data for machine learning, which is necessary to create an effective machine learning model, can pose real estate data challenges, making it expensive, and most real estate companies don’t have the ML expertise needed to build an accurate and cost-effective model.
This is the challenge DataFlik aimed to solve when they approached NineTwoThree to build a machine learning model that generates prioritized lists of motivated sellers faster and more accurately than a human expert can, saving real estate wholesalers key time and money.
DataFlik was able to augment the capabilities of their lean in-house team with NineTwoThree’s ML expertise to overcome the challenges of developing machine learning models and build a truly impactful solution.
Real estate wholesalers looking to find new sellers typically grapple with real estate lead generation challenges. They spend a significant amount of time and money finding the contact information for potential sellers in specific real estate markets. This leads to the need for a motivated sellers list, which is vital for them.
This is an important process for real estate agents to go through to find new leads, clean up their existing lists and bad data, and follow up with cold leads. Agents can do this legwork themselves or pay for real estate prospecting, another significant cost.
Once they have an accurate list, agents send out hundreds to thousands of letters to find the right person that is willing or needs to sell their property.
Unfortunately, traditional real estate prospecting and research involve human analysis of historical data to create very broad lists that result in very low conversion rates. Agents typically send out hundreds of mailers and only receive a few responses. Finding motivated sellers whose homes are not listed on the market yet is something of an art, and this method has a high expense but low return.
While the real estate industry has traditionally been slow to adopt new technology, the last few years have witnessed significant digital disruption. 2022, in particular, saw the PropTech Investment 2022 reaching its highest, signaling propTech growth trends.
PropTech startups are poised to capitalize on a massive industry that is finally embracing technology.
Machine Learning in PropTech is revolutionizing the way businesses operate. Predictive models for property sellers have become an essential tool for companies. DataFlik aimed to use these predictive models and leverage the Real Estate Machine Learning Proof of Concept to create a robust system.
DataFlik aimed to leverage machine learning to generate more accurate lists of potential sellers by creating models to predict properties that are more likely to sell. Instead of using human analysis of a small number of data points, ML would allow DataFlik to process a large amount of data using a model that could be continuously tuned to increase accuracy.
Even with professional prospecting and skip tracing services, mailing lists will often include houses with low chances of conversion, outdated listings, and other data that is inaccurate or untimely.
DataFlik’s prospecting tool aimed to filter out these results, and use AI to predict houses that aren’t yet going on the market, are going into liquidation, and other potential opportunities that other real estate wholesalers would not be aware of. The end goal was to deliver a prediction model to provide real estate wholesalers with valuable insights into potential prospects before they are publicly listed.
DataFlik hit the market just as the real estate industry began to see greater adoption of technology. Last year saw the highest value of PropTech investment on record, with the US investing $61.1 in the first half of 2022 alone.
Using real estate data points for ML is pivotal. For the DataFlik Machine Learning Model to be accurate, it had to consider a myriad of these data points ranging from financial stability to sports interests.
Creating an effective machine learning model for real estate prospecting requires a large amount of data. Producing fast results which such a large volume and breadth of data is complicated and technologically demanding. There are millions of properties in the US and each property has about 1,700 data points.
Data points relevant to DataFlik’s model included courthouse records, demographics, geographic insights, household interests, life events, purchase behaviors, sports interests, short-term loan shopping, financial stability, and more.
The first challenge we ran into with DataFlik is that the real estate data needed to achieve the desired model is extremely expensive. In order to justify the purchase of this data we needed to prove that the proposed model would be capable of achieving its goal - before we could purchase the data needed to train the model.
The challenge of justifying data expenses in machine learning projects is a common one.
The sheer amount of data can be daunting as well. 72% of organizations report that production-level model confidence will require more than 100,000 labeled data items and 10% will require more than 10 million data items.
Since ML is so valuable for processing massive amounts of data, there are many common use cases where a company would be interested in building a model to process this data before investing in the cost of the data itself. Unfortunately, this is also why 33% of AI or ML projects stall during the proof of concept stage.
A “normal” machine learning project starts with setting goals based on guided data analysis sessions with experts to understand how a human expert would make predictions. The next stage involves cleaning and preparing all available data for modeling.
We then create and evaluate several models based on the hypothesis made from meeting with the experts and coming up with the best-performing model. Then the top models are fine-tuned and re-trained until a high level of confidence is reached.
“We got connected with NineTwoThree because we just needed to expand our team, but I didn't really have the resources to build the development team myself. Our team interacts with NineTwoThree every day so we’re very combined together at this point. If we didn’t have them, we wouldn’t be as far as we are now.” - Tyrus Garrett, Co-Founder & CEO at DataFlik
DataFlik came to NineTwoThree with the goal of building a model as proof of generating an effective motivated sellers mailing list - without developing the full model - to justify the purchase of such data. NineTwoThree created a “mini model” that served as a proof of concept and de-risked the expensive data purchase for DataFlik.
Creating this mini model started with interviewing a human expert in real estate prospecting to help identify the most powerful data points for producing a working baseline model. Traditional prospecting relies on human intuition and a much smaller set of data points to make predictions. This serves as a limited starting point for our baseline model.
Based on the data needed to make this ML model, we requested sample data sets directly from vendors and asked questions to ensure the data was valuable.
Working with an AWS-certified partner can greatly speed up processing time and produce high-quality results quickly and efficiently compared to what could take data scientists months.
Once the mini model proved the superior accuracy and efficiency of using machine learning DataFlik was able to justify investing in the 42 billion data points used to train the full model.
NineTwoThree created a model leveraging ML-Powered Real Estate Data, which brought a paradigm shift in how real estate wholesalers approached their potential sellers.
“The real estate market becomes more competitive and saturated every day, squeezing margins each year. DataFlik's product performs better each month with our proprietary algorithm driven by our AI and machine learning technology. The system continuously adapts to target the owners most likely to sell as the market changes in real-time.”
DataFlik’s AI models have significantly outperformed competing seller lists and gives their clients a competitive advantage. DataFlik’s users see an average of 8 times better ROI versus other known lists. The growth of PropTech and AI in the real estate market allows agents and wholesalers to avoid spending unnecessary money chasing unqualified leads.
In 2022, DataFlik’s achieved a growth rate of 634% in monthly recurring revenue. They plan to release several new products in the next year and grow their team considerably. With NineTwoThree’s help, DataFlik overcame the data challenges of training a machine learning model and and the lean startup is poised to expand into an exciting new phase of growth thanks to the success of their ML-powered real estate data.
“Carrying out the actual implementation of your visions is super hard to do for most non-technical founders. When I started working with NineTwoThree, they really helped optimize my visions to make them realities and they did it in a way that was very organized, scalable, and effective.” - Tyrus Garrett, Co-Founder & CEO at DataFlik
“Our goal as NineTwoThree is to give founders like Ty the tools they need to innovate,” said Andrew Amann, CEO, and Co-Founder at NineTwoThree. “We’re very pleased with the growth we were able to achieve with DataFlik and their team.”
DataFlik has plans for new products and considerable growth in 2023. Visit DataFlik.com for more info on their plans to further disrupt the real estate industry and visit NineTwoThree.co for more insights on machine learning development.