Lean Real Estate and Market Experiments


#1

​Hello,

I am a founder of Fractal Spaces, a tech startup that aims make real estate lean by bringing data analytics and market experimentation tools to real estate developers. The goal is to avoid risk and potential problems that come from market assumptions that haven’t been really validated. We are developing a software to help developers understand the market, reduce their investment risks and make better decisions. At the core, our product enables developers to conduct experiments (virtual and physical) on the market and validate their assumptions about demand and location viability.

This is how it works. A real estate developer can access our database of market data before they start a project. They can explore up to date real estate market prices and understand the viability of their planned locations. We are building data sets that show consumer sentiment and preferences for that location, the demographics of potential customers and pricing opportunities. Once they find a potential location and have project sketch, they can start designing their experiments with our service. We collect the data during the experiment and prepare it for the developer.

We then assist with designing the real estate experiment to test the developer’s market assumptions. They select the KPIs they want to track that are most relevant to the investment. They can then run virtual experiments online or perform physical experiments with our unique micro-spaces, designed to mimic the future development project on a smaller scale. These micro-units are essentially prefabricated living spaces, with an interior that copies the planned real estate project. Think of them as a small prototype of the actual project. We then place our micro-units on potential development sites and measure the market response, location viability and pricing. The developers set the parameters they need to know and we will take care of the rest.

Furthermore, while the micro-spaces sit on the land, they can accrue rent for the developer at the same time. This serves to offset the fact that there is this “dead time” of several months during development projects from the point where the land is acquired until actual construction begins.

We use our data analytics tools to understand the experimental data we collect from placing our micro-spaces on the land and tracking key metrics. We can help the developer see what market assumptions they have validated and if they are building something that a lot of people will use. They can then make better investment decisions and adjust their project to the market to make sure they are fully meeting customer demand.

We are currently in beta, but we would love to get feedback from real estate developers and technology enthusiasts on this forum on our software and the idea! Here is a link to our website for more info:

www.fractalspaces.xyz

Let me know if you have any questions or comments!


#2

Most of us here are interested residential real estate side. Mainly bay area focused, but out of state investors are also here. Is it possible to apply some kind of filter by zip code or home type (Condo, Town home, Multifamily, Single family and finally commercial)?


#3

Hello Jil,

Yes, it’s possible to use a geographical filter to search by location. We are also working on being able to search by property type.

Also, we are currently working with data from areas on the West Coast, in particular California, Washington and Oregon. In terms of East Coast we are working with urban areas in Massachusetts and New York.


#4

Do you also cover rental as well as sales data? Reason, I consider rental like dividend cash flow and appreciation as growth of real estate.


#5

We are currently only looking at rental data to help understand the projected revenue of the project and rental price movement in the area. Like I mentioned before, our service then offers the ability to conduct experiments and get an actual validation of the projected revenue and demand for the specific location.


#6

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#7

Hello MM5,

This blog is really nice and informative. Thanks for the sharing.


#8