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Feb 3, 2017 · 24m · mad

Modernizing Real Estate with Data Science // Ian Wong, Opendoor (FirstMark's Data Driven)

Ian Wong · 18m spoken Matt Turck · 23s spoken
0:00 / 0:00
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gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions

At DataDrivenNYC, Opendoor co-founder Ian Wong demonstrates how data science, automated valuation models, and direct balance sheet deployment modernize residential real estate transactions by eliminating seller friction and accurately pricing homes at scale.

How this conversation actually went

Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. Matt holds 1.6% of the talking time here. How this is scored →

Matt as informed peer 0.3 Guest teaching 2.0 Guest disagreement 0.8 Matt pushing back 0.0
05100:0010:0020:000:09–3:56 · Matt as informed peer 0/10 Ian Wong Introduces the Friction in Selling a Home Ian opens the presentation by describing the financial stress, 3-month timeline, and deal uncertainty in traditional home sales. As this is a keynote presentation, host Matt Turck does not speak.3:56–7:46 · Matt as informed peer 0/10 Data Science Challenge 1: Seeing the Home Ian outlines the first data science challenge of converting visual attributes into structured algorithm inputs using deep learning and crowdsourcing. The host remains silent throughout the segment.7:46–12:51 · Matt as informed peer 0/10 Data Science Challenge 2: Studying Neighborhood Nuances Ian details spatial heterogeneity, parcel-level wide data, and historical time-series modeling challenges. The presentation continues uninterrupted without host participation.12:51–15:30 · Matt as informed peer 1/10 Building the Technical Team and Opendoor's Customer Impact Host Matt Turck transitions out of the presentation by asking Ian to elaborate on his team slide regarding data science Venn diagrams. Ian passionately explains why standard data science skill definitions box people in.15:30–19:02 · Matt as informed peer 0/10 Q&A: AVM Commoditization, Resale Innovations, and Risk Hedging Audience members ask about AVM commoditization, resale operations, and risk hedging. Ian reframes the commoditization question by emphasizing Opendoor's balance sheet execution over software models.19:02–24:16 · Matt as informed peer 1/10 Q&A: Resale Testing, Urban Markets, and Pricing Trust Audience members inquire about pricing trust, seller recourse, and urban markets like New York. Ian explains that Opendoor prioritizes the national $230,000 average home market before tackling high-density urban hubs.0:09–3:56 · Guest teaching 1/10 Ian Wong Introduces the Friction in Selling a Home Ian opens the presentation by describing the financial stress, 3-month timeline, and deal uncertainty in traditional home sales. As this is a keynote presentation, host Matt Turck does not speak.3:56–7:46 · Guest teaching 1/10 Data Science Challenge 1: Seeing the Home Ian outlines the first data science challenge of converting visual attributes into structured algorithm inputs using deep learning and crowdsourcing. The host remains silent throughout the segment.7:46–12:51 · Guest teaching 2/10 Data Science Challenge 2: Studying Neighborhood Nuances Ian details spatial heterogeneity, parcel-level wide data, and historical time-series modeling challenges. The presentation continues uninterrupted without host participation.12:51–15:30 · Guest teaching 2/10 Building the Technical Team and Opendoor's Customer Impact Host Matt Turck transitions out of the presentation by asking Ian to elaborate on his team slide regarding data science Venn diagrams. Ian passionately explains why standard data science skill definitions box people in.15:30–19:02 · Guest teaching 3/10 Q&A: AVM Commoditization, Resale Innovations, and Risk Hedging Audience members ask about AVM commoditization, resale operations, and risk hedging. Ian reframes the commoditization question by emphasizing Opendoor's balance sheet execution over software models.19:02–24:16 · Guest teaching 3/10 Q&A: Resale Testing, Urban Markets, and Pricing Trust Audience members inquire about pricing trust, seller recourse, and urban markets like New York. Ian explains that Opendoor prioritizes the national $230,000 average home market before tackling high-density urban hubs.0:09–3:56 · Guest disagreement 0/10 Ian Wong Introduces the Friction in Selling a Home Ian opens the presentation by describing the financial stress, 3-month timeline, and deal uncertainty in traditional home sales. As this is a keynote presentation, host Matt Turck does not speak.3:56–7:46 · Guest disagreement 0/10 Data Science Challenge 1: Seeing the Home Ian outlines the first data science challenge of converting visual attributes into structured algorithm inputs using deep learning and crowdsourcing. The host remains silent throughout the segment.7:46–12:51 · Guest disagreement 0/10 Data Science Challenge 2: Studying Neighborhood Nuances Ian details spatial heterogeneity, parcel-level wide data, and historical time-series modeling challenges. The presentation continues uninterrupted without host participation.12:51–15:30 · Guest disagreement 1/10 Building the Technical Team and Opendoor's Customer Impact Host Matt Turck transitions out of the presentation by asking Ian to elaborate on his team slide regarding data science Venn diagrams. Ian passionately explains why standard data science skill definitions box people in.15:30–19:02 · Guest disagreement 2/10 Q&A: AVM Commoditization, Resale Innovations, and Risk Hedging Audience members ask about AVM commoditization, resale operations, and risk hedging. Ian reframes the commoditization question by emphasizing Opendoor's balance sheet execution over software models.19:02–24:16 · Guest disagreement 2/10 Q&A: Resale Testing, Urban Markets, and Pricing Trust Audience members inquire about pricing trust, seller recourse, and urban markets like New York. Ian explains that Opendoor prioritizes the national $230,000 average home market before tackling high-density urban hubs.0:09–3:56 · Matt pushing back 0/10 Ian Wong Introduces the Friction in Selling a Home Ian opens the presentation by describing the financial stress, 3-month timeline, and deal uncertainty in traditional home sales. As this is a keynote presentation, host Matt Turck does not speak.3:56–7:46 · Matt pushing back 0/10 Data Science Challenge 1: Seeing the Home Ian outlines the first data science challenge of converting visual attributes into structured algorithm inputs using deep learning and crowdsourcing. The host remains silent throughout the segment.7:46–12:51 · Matt pushing back 0/10 Data Science Challenge 2: Studying Neighborhood Nuances Ian details spatial heterogeneity, parcel-level wide data, and historical time-series modeling challenges. The presentation continues uninterrupted without host participation.12:51–15:30 · Matt pushing back 0/10 Building the Technical Team and Opendoor's Customer Impact Host Matt Turck transitions out of the presentation by asking Ian to elaborate on his team slide regarding data science Venn diagrams. Ian passionately explains why standard data science skill definitions box people in.15:30–19:02 · Matt pushing back 0/10 Q&A: AVM Commoditization, Resale Innovations, and Risk Hedging Audience members ask about AVM commoditization, resale operations, and risk hedging. Ian reframes the commoditization question by emphasizing Opendoor's balance sheet execution over software models.19:02–24:16 · Matt pushing back 0/10 Q&A: Resale Testing, Urban Markets, and Pricing Trust Audience members inquire about pricing trust, seller recourse, and urban markets like New York. Ian explains that Opendoor prioritizes the national $230,000 average home market before tackling high-density urban hubs.

speaking balance: gold is Matt, purple is the guest (3 minute bins)

0:00 · Matt 0% · guest 100%0:00 · Matt 0% · guest 100%3:00 · Matt 0% · guest 100%3:00 · Matt 0% · guest 100%6:00 · Matt 0% · guest 100%6:00 · Matt 0% · guest 100%9:00 · Matt 0% · guest 100%9:00 · Matt 0% · guest 100%12:00 · Matt 5.9% · guest 94.1%12:00 · Matt 5.9% · guest 94.1%15:00 · Matt 0.2% · guest 99.8%15:00 · Matt 0.2% · guest 99.8%18:00 · Matt 0.3% · guest 99.7%18:00 · Matt 0.3% · guest 99.7%21:00 · Matt 2.8% · guest 97.2%21:00 · Matt 2.8% · guest 97.2%24:00 · Matt 34.3% · guest 65.7%24:00 · Matt 34.3% · guest 65.7%
Sharpest disagreement ▶ 15:45 Rejecting the AVM commoditization premise

Ian directly challenges an audience member's question by rejecting the premise that automated valuation models will simply become commoditized, pointing out that risking balance sheet capital is Opendoor's true differentiator.

 Hardest push from Matt ▶ 14:18 Steering topic to team recruitment

Matt interjects at the end of the presentation to steer Ian back to a skipped slide, asking him specifically to comment on data science talent hiring.

 Biggest teaching moment ▶ 14:30 Dismantling data science Venn diagrams

Ian educates the audience on why popular skill Venn diagrams restrict data scientists, arguing that software engineering and business problem-solving are far more critical than pure modeling skills.

 Matt holds his own ▶ 14:18 Zeroing in on data science team construction

Matt demonstrates industry awareness of tech hiring challenges by singling out the Venn diagram slide as the key topic for follow-up elaboration.
the scores for every segment, with the reasoning behind each
ChapterTopicMatt as informed peerGuest teachingGuest disagreementMatt pushing backWhy
Ian Wong Introduces the Friction in Selling a Home 0100 Ian opens the presentation by describing the financial stress, 3-month timeline, and deal uncertainty in traditional home sales. As this is a keynote presentation, host Matt Turck does not speak.
Data Science Challenge 1: Seeing the Home 0100 Ian outlines the first data science challenge of converting visual attributes into structured algorithm inputs using deep learning and crowdsourcing. The host remains silent throughout the segment.
Data Science Challenge 2: Studying Neighborhood Nuances 0200 Ian details spatial heterogeneity, parcel-level wide data, and historical time-series modeling challenges. The presentation continues uninterrupted without host participation.
Building the Technical Team and Opendoor's Customer Impact 1210 Host Matt Turck transitions out of the presentation by asking Ian to elaborate on his team slide regarding data science Venn diagrams. Ian passionately explains why standard data science skill definitions box people in.
Q&A: AVM Commoditization, Resale Innovations, and Risk Hedging 0320 Audience members ask about AVM commoditization, resale operations, and risk hedging. Ian reframes the commoditization question by emphasizing Opendoor's balance sheet execution over software models.
Q&A: Resale Testing, Urban Markets, and Pricing Trust 1320 Audience members inquire about pricing trust, seller recourse, and urban markets like New York. Ian explains that Opendoor prioritizes the national $230,000 average home market before tackling high-density urban hubs.

Statements from this episode (12)

Disclosure
Opendoor eliminates friction by purchasing homes directly from sellers for cash
“We'll actually make an offer for your house and buy it from you so that you don't have to endure three months of stress and headache.”
Ian Wong Feb 3, 2017 ▶ 1:50
Assertion Partly supported
The average US home takes three months to sell, with 14% failing
“The average home takes over three months to sell, and there's a one in seven chance to deal with fall through”
Ian Wong Feb 3, 2017 ▶ 2:01
Assertion Partly supported
US residential real estate generates $100 billion in annual transaction fees
“Every year, 1.4 trillion dollars of assets change hands, generating a hundred billion dollars in fees.”
Ian Wong Feb 3, 2017 ▶ 2:27
Disclosure
Opendoor uses deep learning and crowdsourcing to extract data from home photos
“And so practically speaking, what we do is a combination of deep learning, so we have a couple boxes, and Amazon has running CARES and is kind of turning all these photos into structured data, and also a lot of crowdsourcing.”
Ian Wong Feb 3, 2017 ▶ 6:59
Opinion
Crowdsourcing is a completely underappreciated technique in data science
“But I'm really hoping that just in 17, as a site know, is going to be the comeback year for crowdsourcing, because we need it. And it's totally underappreciated as a technique.”
Ian Wong Feb 3, 2017 ▶ 7:17
Insight
Real estate valuation is a wide data problem, not a high-n problem
“And this is not the traditional big data problem. In the traditional big data problem, You have basically a lot of data, right, so a lot of n. Really in this problem we have very wide data.”
Ian Wong Feb 3, 2017 ▶ 8:48
Insight
Human emotion causes residential real estate transactions to cluster around round numbers
“People like to close in round numbers. So you see these striations, and it's because people like to close for 200 K or 201 K, when in fact, you know, that's just totally just human emotions at play.”
Ian Wong Feb 3, 2017 ▶ 11:50
Insight
Data science Venn diagrams underplay the importance of software engineering and business
“I think that's the minimum bar to be a sufficient data scientist. A, B it totally underplays the importance of software engineering, and C, that's actually just a small part of what it means to be a data scientist. I think it totally neglects things like busin…”
Ian Wong Feb 3, 2017 ▶ 14:44
Disclosure
Opendoor offers home buyers a 30-day satisfaction return guarantee
“With us, we even have something called a thirty-day satisfaction guarantee. So you can actually return the home to us if you don't like it.”
Ian Wong Feb 3, 2017 ▶ 17:41
Assertion Not checkable as stated
Real estate financial hedging instruments lack sufficient liquidity for practical transactions
“There are not that many available out there, and they're not liquid enough where they're attractive from a, you know, to actually transact on.”
Ian Wong Feb 3, 2017 ▶ 18:18
Disclosure
Opendoor flexes its seller transaction fee based on the home's risk profile
“Now, the offer that a seller gets has two numbers in them. One is the fair market value. So this is what we think your home would sell for on the market. And the other one is a fee. And that fee actually flexes depending on the risk of the home.”
Ian Wong Feb 3, 2017 ▶ 18:33
Assertion Supported
U.S. homes outside major urban centers consistently sell 3% below list price
“When you list a home in the, anywhere else in the country, you actually close for three percent less than you list for, right?”
Ian Wong Feb 3, 2017 ▶ 22:44
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