HelloData.ai: AI for Real Estate

Published by
Nico Lassaux
March 5, 2023
HelloData.ai: AI for Real Estate

The Real Estate Data Problem

As industries adopt data-driven products, they seem to follow a particular path. First they have to aggregate enough data to actually analyze, then they start leveraging business intelligence products that use that data, then they start using artificial intelligence products trained on that data for better decision-making.

To analyze anything, you first need a large volume of well-structured data. In real estate, companies like CoStar, Trepp, CoreLogic, Zillow and ESRI laid the groundwork here.

The real estate industry stayed in the “data collection” phase longer than other industries because it has few widely recognized standards. It is highly local, with local tax assessors and government bodies all tabulating records differently, and ownership is extremely fragmented, with no single owner holding more than 1% of the market for any asset type. Compare that to Google Chrome, which has 90% browser share and can easily implement standards that impact how nearly everyone searches the internet. It's no wonder it took a while - but robust data is now widely available in real estate.

After the data is flowing, you typically see the development of business intelligence products to deliver insights from this data. We think real estate is in this phase right now.

Because the industry never really established universal accounting or underwriting standards though, bringing the data together to drive business intelligence may take a while. Fortunately, companies like Cherre, NavigatorCRE and RevolutionRE are working to make all the different datasets “talk to each other” so real estate companies can analyze their own data alongside the market data they purchase from third-parties.

The last phase is using AI and machine learning to inform decision-making. Although there are plenty of real estate AI startups, the industry hasn’t achieved broad adoption of this technology yet.

The bulk of the algorithms used in real estate today are for data extraction and organization, because we’re still working on the problem of bringing all our data together. Real estate’s fragmented nature has created an even bigger issue for PropTech startups, though: way too many products.

The Real Estate Product Problem

The real estate technology industry didn’t have many startups until recently. Based on data from JLL and Crunchbase, a decade ago there were only 100 – 200 PropTech firms that raised $1-2 billion in venture capital funding. Today, there are well over 1,000 PropTech startups, and those companies raised more than $24 billion in 2021 alone. Except for 2020 during the pandemic, the growth of the real estate tech industry has been incredible, as you can see in the graph below:

Statista: Value of investment into PropTech companies worldwide from 2010 to 2021

This proliferation of startups has happened in many industries, but the real estate data problem makes it challenging for our industry. PropTech products generally don’t integrate well with each other because they use different data formats, different financial analysis conventions, etc. And the current real estate data industry leaders border on monopolies… they have no incentive to advocate for industry standards or make integration easier because they profit from hefty implementation fees.

The result is many, many point solutions that don’t play well with each other and take significant resources to learn and integrate. Products are only partially solving problems, so you need to log into many different systems and copy and paste data between them to get results.

We’ve spoken with so many real estate professionals who actually dread having to learn another product. It makes sense because at this point – adding yet another login to the mix often adds more data management and integration problems than it solves.

The Solution: Meet Real Estate People Where They Work

When we first set out to launch HelloData.ai, we tried to come up with the ultimate startup idea in real estate data science. We were the team that built Enodo, a real estate predictive analytics startup that we built and sold to a publicly traded company in less than 3 years. We wanted to go big!

But we knew from experience that an “all-in-one” solution would fail in real estate because of the data and product fragmentation issues.

And from talking with potential customers, we know people are exhausted with all the logins and subscriptions.

We really didn’t want to be the 9,000th interface people have to log into every day. What’s more, as data engineers and data scientists, we don’t even like UI development! Yes, a nice UI looks great in marketing material… but why build something you don’t enjoy working on for people who won’t want to use it?

So instead of building yet another interface for people to learn, we chose to focus on data science and bring the benefits of AI where real estate people work every day.

The HelloData.ai Vision: Deliver the power of AI for real estate, without changing what works.

We want to minimize workflow disruption and maximize value. If you have to take time to learn how our products work vs just getting what you need, we failed. If you must log in more than once to get the full value from our products, we failed. We’re building a platform that arms real estate with AI, but respects the tried-and-true methods of an industry that has created more millionaires than any other in history.

Our philosophy was informed by 7 years working in real estate technology and hundreds of conversations with real estate investors, developers, lenders and brokers. Here’s how looks in practice:

  1. Minimalism: Solve One Problem Perfectly – In a sea of point solutions, a product that tries to solve too many problems is difficult to integrate. Too many overlapping features, too much complexity, too little time to figure out how it works. Instead of going down this path, we decided to build APIs that are each designed to solve one problem perfectly, and to make it extremely easy to integrate these APIs into existing workflows. We believe each of our products should be easily understandable to someone who knows nothing of the real estate industry, and each should be describable in a single sentence. For example:
  2. AnyExtract.ai – Extract Structured Data from Any Real Estate Document with AI
  3. QualityScore.ai – Assess Property Condition & Curb Appeal from Listing Photos
  4. LiquidRent.ai – Transparent Revenue Management System that works with any Property Management Software
  5. RentSource.ai – Automate CMAs & Rent Surveys with Daily Price & Availability Data
  6. Ubiquity: Integrate with the Software they Use Every Day – Soon after we launched our APIs, we built integrations with Excel and Google Sheets so people could easily pull data from our products directly into their underwriting models. Now we are working on email integrations for each product, where people can send documents to a dedicated email and get results back in Outlook/Gmail. And of course we have Zapier integration on the roadmap, so we can provide real estate market insights in Salesforce directly (many real estate companies run on Salesforce). People shouldn’t have to learn anything new or change their processes to work with HelloData.ai.

3. Research: Learn About Real Estate Workflows Firsthand – We offer contracting services because it helps us understand what problems people are facing in real estate. Too often, startups build complex products without understanding in enough detail how customers work today. They also tend to raise money and hire large teams too early, before they truly achieve product-market fit. Contracting lets us learn more about how real estate business work, while earning enough income that we don’t have to raise money. In some cases, we also have the opportunity to embed our APIs in their workflows as part of contracting engagements, which leads to recurring revenue as well.4. Fairness: Charge Only for Value Received – Way too many startups see how much money this industry has and try to charge massive fees for their products. But real estate people are smart. They can smell BS. They can tell if something will actually help them and they have a keen sense of value. So we only charge based on usage. No hoping people forget about their subscription and just keep paying us. Our clients pay for the value they receive and nothing more. Integrity and work ethic are the most important attributes in this industry, and we don’t want to collect a dime we didn’t earn.

At HelloData.ai, our mission is to deliver the benefits of AI in real estate, without changing what has made this industry one of the most successful in history.

We do this by solving single problems very well, integrating our products where people work, and giving people exactly what they need exactly when they need it. And we constantly work to understand this industry and respect the intelligence of tech savvy real estate entrepreneurs who know a good deal when they see it. We’re going to be the biggest provider of AI for real estate that no one ever thinks about, but everyone runs their business on.

So come work with us and see the difference HelloData.ai can make for your business!

Data Scientist Nicolas Lassaux, with expertise in real estate analytics, was pivotal at Enodo and Walker & Dunlop. Co-founder of Hello Data, he's elevating real estate decisions through innovative data use. Passionate about running, cycling, and music.

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