About

Non Institutional landlords (Mom & Pops) face four main challenges: 1) they pay up to 25% of their revenue to middle men 2) they face lower performance due to sub-scale operations 3) they have a lack of liquidity and access to capital available to institutional investors and 4) they lack technology available to institutional investors.

Solution & Product

June uses advanced data science and machine learning software to identify undervalued rental apartments and increase their revenue by 30% on average. The Machine Learning algorithm detects arbitrage in the market by combining publicly available market listings, urban economic trends, and June’s proprietary tenant sales data to underwrite rental properties in real-time. Once June acquires a rental property, they pair it with a software rental product that has a proven price arbitrage. Specifically, June offers tenants flexible leases (30 day minimum, 18 month maximum stay), dynamic rental pricing software, customizable furnishing options (any apartment can be rented furnished, unfurnished, or partially furnished), and a fully automated digital renting experience (95% of tenants sign leases without an in-person tour). As a result, June is able to generate on average 30% higher revenue per property than the traditional market (based on the dataset of 7,000+ historical leases).

Innovation & Scalability

June utilizes advanced technology to develop long term moats. Specifically, they’ve focused on numerous areas to create competitive advantages: First, June invests in data science to identify mispriced rental properties. Its proprietary ML technology allows the company to act faster and generate higher returns than non-technical rental options. The algorithm has already shown higher accuracy than the industry-leading Automatic Valuation Model. June’s underwriting technology is extremely difficult to replicate due to the use of unique and proprietary in-house sales and leasing data. June’s AVM provides underwriting for any new rental properties that company brings to the platform as well as dynamic pricing for tenants that accounts for seasonality, lease durations, market trends, supply/demand ratio and many other factors. June currently processes more than 41 million rental listings and has classified more than 16.5M images. June’s algorithm constantly evolves and becomes more accurate as the company increases its footprint. For example, the Median Absolute Error Rate (MAE) has declined by 50% in the last 5 months. Second, they invest in automating internal operations to create efficiencies and grow contribution margins.

Traction & achievements

Team & Why Us

Daniel Mishin, CEO & Founder Avi Goldenberg, General Counsel Jeremy LePlante, Chief Revenue Officer Leon Khodorovski, VP, Finance / Controller Stacy Golovina, Managing Director, Greece Moiz K. Malik, Chief Product Officer Mauricio Zuniga, Chief Experience Officer

Funding Needs

Contact us for more information about this aspect.