Data-driven product owner / product designer / UX designer / product manager / entrepreneur.

Focusing on sustainability - of designs, of systems, of the environment.

Paper Property helps private real estate investors source, analyize, and plan the acquisition of their off market real estate investments. Unlike Zillow and Trulia, Propertied focuses primarily on the numbers of a deal.

Before Paper Property, real estate investors found their deals off of places like Craigslist and through private emails to one another. Many deals got lost with ineffective marketing efforts, and profitable deals went undone...

The lack of standardization meant that each investor had to develop their own system to keep track of deals sent around by email, sometimes receiving the same deal with two different prices. Investing was moreso a game of operations design than of real estate expertise.

Propertied functions as the one place for private investors to advertise their off market deals directly to one another, just as agents market theirs to primary homeowners on Zillow or Trulia.

The lack of standardization meant that each investor had to develop their own system to keep track of deals sent around by email, sometimes receiving the same deal with two different prices. Investing was moreso a game of operations design than of real estate expertise.

Propertied functions as the one place for private investors to advertise their off market deals directly to one another, just as agents market theirs to primary homeowners on Zillow or Trulia.

The Design Process

Step 1

The challenge

todo

Step 2

Existing products

List of existing competitors

Step 3

Personas

Primary persona(s) - two sided marketplace

- buyer - seller

Secondary persona(s)

- deal manager - marketer
Step 4

Current investor workflow

Most private real estate investors I surveyed follow this flow of logic when working through their off market deals.



  1. What is the location?
  2. Match of primary criteria, which could be any of:
    • type of deal
    • price range
    • availability for increase in value
  3. Does the secondary criteria match well?
  4. Due diligence
  5. View property
  6. Additional due diligence
  7. Prepare deal logistigs
    • repair estimate
    • secure funding
  8. Make initial offer
  9. Negotiations with seller(s)
  10. Set up deal closing
  11. Complete deal closing
  12. Manage investment, which could be for example:
    • repair the property
    • rent out the renovated property
    • sell the renovated property
Step 3

Triggers of use / App entry points

Below is a list of the most common motivations a user would have to come back into the app.

Now that we know what goals a user may have, we can deduce what common objects and features we may need to build.



  1. to find a new deal
  2. to check the status of an existing deal
  3. to sell a new property off market
  4. to review old deals
  5. to analyze old deals for trends
  6. to view a deal sent by another user
  7. research property they found in real life
  8. research a given address
  9. search and filter deals in a given neighborhood
Step 5

User needs & Product unique value

- automation of matchmaking - based on the numbers of a deal - mobile app (other investor platforms are web based) - allows networking and following of user profiles, but listings are property-centric, rather than blog or personal profile-centric
Step 6

Assumptions

- User prefers to use this app from their mobile device, to receive push notifications, use location data for their current location, to add contacts from their phone, and to upload property photos from phone for new listings
Step 7

The Design

- todo, show wireframes and Achieved in this design so far: - user can upload a property by property data - user can search posted properties
Step 8

Monetization strategies

Step 9

Roadmap of upcoming features

hypothesis/ at the end of this roadmap, this product will be ____ - gov databases of historical property data - photo ordering and editing - uploading of video and 3D tours of spaces - uploading of PDFs into property profile - automatic booking of property showings (sync with a showings API) - wider search algorithm - featured listings (per area, show up higher in the ranking) - map view of properties - test out what data users want to preview on the map vs on the list - comps and assumptions in the comps - due diligence of deals - compare several deals to one another - user-designed categorization of deals - automation of the email/text message marketing to existing buyers’ lists, uploaded by the user, based on uploaded buying criteria - suggested: similar deals, based on similar data to currently displayed listing
Step 10

Next steps

- user testing and iteration on flow - Visual design on top of wireframes - hypothesis of MVP: ____ - tech stack options: ____ - suggested rollout plan - product roadmap (link) - Edge cases not yet designed for: ___