A Prop-tech AI product that predicts and protects security deposit default risks for home rentals in S. Korea.
#Prop-Tech #Legal-Tech #AI/DATA #B2C #WEB #Android #iOS
#VC INVESTMENT #GROWTH
Safehomes — A Prop-tech AI product that predicts and protects security deposit default risks for home rentals in S. Korea.
One of the main characteristics of South Korean rental market is that security deposit amounts are extremely high, generally more than 10~20 times more than the price of a monthly rental price. Compared to 0~3 months worth of rental payment as a security deposit amount in the USA, the security deposit norms in Korea presents a unique challenges and risks for the residents.
Another big factor to note is that property investment in Korea had always been in high demand, to a degree where housing price bubble has been a constant threat to the economy.
In a nutshell, almost EVERYONE aims to buy properties whether they have the money or not. And with such high demand for property investment, aggressive, risky and unaffordable mortgage/property loan practices had of course been a serious issue.
The (1) high demand for property investment and the (2) risky loan practice behavior — have concurrently worked together to exacerbate the higher rental security deposit problem, as the landlord wants to use the security deposit to pay back some of the loan capitals on their property to lower the interest payment.
These characteristics create a very unique and detrimental risk phenomenon for the tenants. If a landlord who took an aggressive loan for the property defaults, the property gets seized and sold at auctions. And during such process, the expensive amount of security deposit that the tenant paid to the landlord simply vanishes, leaving the tenant without home and huge chunk of life savings.
Such security deposit default accidents have been happening very frequently for the past decades, and with over $500 million worth of damages occurring annually, it is considered the most common and detrimental housing rental risk that quite literally manufactures thousands of victims a year.
We wanted to stop from security deposit default accidents from happening anymore. Many people were suffering from the aftermath, and the risks were too high and prevalent for the problem to continue to persist without any effort to stop it.
The best solution would have been to change the laws and regulation, and/or lowering the average level of security deposits. But as easy as they sound, they are extremely difficult to achieve, and are certainly not something a mere start-up could aim to fix (even the most renowned politicians and economists are currently failing to address the issue).
Instead, we directed our focus in preventative aspect.
So we figured, if we can somehow prevent tenants from signing a dangerous contract, then we won’t even have to worry about security deposit default accidents.
We focused on the preventative resolutions, and came up with the following hypothesis:
As the CEO was already a seasoned professional who had extensive training and experience with property title analysis and risk assessment, we decided to first test out our hypothesis before building an expensive AI product.
For our first prototype, we:
Once a customer submitted the form, our team would do the following within 1 business days:
We partnered with 7 real estate agents who were in the process of searching for rental properties for their clients, and they helped to refer 80 clients to our landing page.
As our prototype service was free of charge, all 80 clients proceeded to utilize our service to assess potential risks for the rental agreement they were planning on entering, and we were able to prevent 32 clients from entering into dangerous rental contracts.
After all the of service had been delivered to our clients, we asked for 5 minutes of their spare time to do quick phone interview about our service experience.
Throughout the after-service interview, we asked numerous questions and received the following feedback results from the 80 clients:
The market test through our prototype (landing page & concierge service) was successfully completed with strong performance analytics, and it was very safe to conclude that our hypothesis was correct.
Immediately following the successful market test, we raised a SEED round VC investment and proceeded to creating an AI product that could automate the reports. As the first step, I led the MVP feature ideation and UX/UI prototype to guide the engineering team for our product development.
For this project, we set up two different engineering teams to develop our MVP:
Once our engineering teams were set up, I prepared a detailed PRD to develop our product, and extensively led the project management for the next 6 months until our first beta product was released.
After 2 weeks of extensive QA, we released our first beta product and aggressively executed our Go-To-Market strategies to quickly infiltrate the SOM (Service Obtainable Market).
As we already had beautiful success with utilizing the real estate agent network, we put major emphasis on scaling our acquisition channel through real estate agent partnership, offering them affiliate rewards for every clients they refer to us.
Within the next 6 months, we were already generating over $25,000 of monthly sales, and acquired about 1,500 paying customers per month. We knew our product has achieved product-market-fit, and was ready to scale further.
The biggest focus on our scaling effort was to increase the CLTV (Customer Lifetime Value).
Our product targeted people who are in the process of searching for a housing rental, and once our customer has found a new home, it would take at least two years in average for them to return to our service, as they would not be looking to rent another one until their new rental agreement expires.
This meant that no matter how satisfied our customers were with our service, we would only have one chance every two years to monetize off of them, and we desperately needed to figure out ways to increase the sales revenue per each customers; the opportunity costs were too high to just simply let our satisfied customers sit around.
With such goal in mind, we set out multiple initiatives to (1) optimize the pricing strategy to increase the revenue per service, and (2) create new features and business models where we could charge monthly subscription fees to our existing customers.
As a result, we successfully increased our pricing level to the maximum level, and added a paid subscription feature that monitors the property risk status and give real-time notifications while the tenant is dwelling in the property (should the landlord take out aggressive loans without notifying the tenant, the tenant is exposed to new security deposit default risks).
As of Nov. 2023, the company and the product has scaled exponentially, along with product and revenue performance. It is currently preparing for Series-A round investments, and I strongly believe this product will bring enormous, positive impact to S. Korean rental market in the next three years.