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CityBldr The “Smart Brokerage” That Helps You To Determine Your Property’s Worth

Kritika Rawat



CityBldr, headquartered in Seattle, WA, is the first “Smart Brokerage,” leveraging AI and machine learning to determine whether your property is worth more to a developer, builder or investor. 


CityBldr enables property owners to connect with the people willing to pay them the most for their properties, while also allowing developers access to opportunities previously unavailable to them. Read on for excerpts from the conversation.

1. What are the core services that CityBldr offers as on moment? And what future offerings can we expect? 

We make help owners and buyers of underutilized urban properties make a market.  In the future we’ll provide services to cities as well.

2. How exactly is technology helping you grow your business? And what are the real implementation of Machine learning in your offering? 

We couldn’t have run this business before the commoditization of compute (the cloud). Too much data being processed and we would have spent our entire Seed round on servers and crunching data.

Our algorithms predict new buildings every day in cities across the country and create 3d visuals of the future of cities. We’ve been doing this for 3 years at some level of scale, and now the ML models are beginning to see patterns across cities.

3. While building up your brand, could you share three biggest hurdles that you have faced till now. And going forward what specifically would be your biggest challenge? 

As far as brand is concerned, initially, making the decision to remove vowels from our company brand was simultaneously useful and challenging. CityBldr is defensible as a brand, but not spelling it CityBuilder has meant we’ve had to redirect traffic that funnels to the way the brand sounds. Eventually we had to purchase and point that traffic to our main page.

Secondly, operating a marketplace is a challenge for brands, and it’s been no different for us. We want to offer value to both buyers and sellers in the market, which means we’ve had to create trust with both constituents in the market. We started by creating that trust with sellers, and now we’re focused on doing the same with buyers.

Going forward our biggest challenge will be continuing to build that market trust, and being diligent not to offer any brand promises we can’t follow through on.

4. Unlike broker/agent model, where an agent helps buyer mediate talks with seller. Here, your promised offerings just might be making it difficult for developers to acquire a certain asset a cost since customers with you by their side makes more aware decisions. How, do you find favour from developers in such cases? 

Developers purchase the majority of their sites off market. We’re bringing many more off market properties to the table, so that creates options for developers.

5. Could you put some light on your business model? 

It’s like any marketplace. We connect buyers and sellers. In this case, the product being bought or sold is underutilized urban property.

6. Can you name a startup in PropTech domain today which has really caught hold of your attention? 

Landis. I met their CEO Cyril Berdugo at Stanford when I was giving a talk to the GSB a few years back and the guy really caught my attention with his smart questions and eloquence. We’ve stayed in touch over the years and he’s since launched Landis, a startup that turns renters into owners. Great timing for a business model like this with homeownership at a multi-decade low, home equity being the largest component of middle class net worth growth and a growing divide between the haves and have nots. 

7. Which are the cities you are most active in? How do you freeze upon a demographic location to start your services? Since it is not just the demand but historical data too that might be helping you do your job better? 

West Coast cities, with a strong recent uptick in NYC, DC, Texas, Florida and Nashville.

Demographic trends tell story of the future of city optimization, because the stories of cities are about changes to its inhabitants. The largest demographics in the US are baby boomers and millennials so we watch their movements closely and try to understand why they go where they go, then predict how that’s likely to continue and how the cities they move to will need to change to accommodate that growth. 

Historical data is the input for machine learning algorithms. Ideally, demand is the output.  Being directionally correct (and incrementally more so) is the goal. 

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