The apartment rental industry is a $163bn industry, serving over 35 million Americans. Over the last 5 years the industry has had a growth rate of 5% and continues to show this level of growth. 2017 was the highest year of new apartments being built in the last 10 years (346,310 apartments), which creates a tremendous opportunity in the future for an increased high rate of apartment renting. Additionally, there has been an increase in apartment rentals since the recession and housing crisis. It seems people are more fearful of owning homes and making such a commitment, that rentals have increased. At the same time, the age of marriages has increased, which is often when home buying occurs, leaving more people single for longer and therefore prolonging their time in the rental market.
Another opportunity is the amount of spam or fake listings that plague the apartment rental industry. Even sites that claim they are highly reliable (Zillow, Trulia, etc.) each post advice to their users as to how to avoid scam listings, because the platforms are unable to stop these scammers entirely on their own. In 2017, researchers at NYU reviewed more than 2 million for-rent posts and found 29,000 fake listings in 20 major cities, about half of which the sites were not able to detect themselves. Being able to detect and eliminate these scams would simplify the rental experience greatly.
Finally, in reviewing the set of data gathered in relation to the apartment rental process, we see a large opportunity for improvement. Just under 60% of those surveys had moved more than 1 time in the last 5 years, and a majority used primary sites like Craigslist, Zillow and ApartmentList. Of those surveyed, we found there was quite a bit of overlap between what users value most and what they find most frustrating in the process of finding an apartment, which indicates a clear needs gap. In particular, it is clear that people could use improvements when it comes to determining the following: the safety of certain neighborhoods, nearby bars and restaurants, noise level, nearby grocery stores, and parking options.
AptHero uses artificial intelligence and machine learning to address the major deficiencies of the rental process. We use the power of technology to provide more complete information to users, curate listings based on their preferences, and address the significant problem of fraudulent listings. These three aspect of our service are currently not addressed by competitors. The major aggregators of rental listings such as Craigslist currently only offer basic filter capabilities such as price, neighborhood, and some apartment and building specific amenities. Other criteria important for consumers such as noise levels, traffic and crime patterns, proximity of playgrounds, etc. can only be assessed through multiple manual searches or are completely unavailable. The manual work involved in carefully comparing even ten properties becomes quickly overwhelming so people turn to real estate agents for help. Agents can have conflicting interests and are only available on certain days and times. Moreover, whole markets such as San Francisco completely rely on online search without the benefit of agents.
AptHero allows users to easily visualize a wealth of data about the surroundings of a property to help them make better selections. Users can select to see relevant information for a given listing such as the proximity of schools, parking, pet parks, playgrounds, bars and restaurants, grocery stores, or display on the map an address that they will visit frequently such as their office location or their child’s daycare. In addition, we aggregate data on crime, traffic patterns, and noise levels. Finally, users have access to price trends. When users select properties that they like, the algorithm learns dynamically about their preferences and suggests other listings that may be of interest. This greatly shortens the search process, increases user satisfaction, and benefits property owners and managers by showing their listings to the most interested users.
AptHero’s other value-added service is detecting fraudulent listings which is an unmet need expressed by our survey respondents. The algorithm learns over time how to flag fraud. It starts with a training dataset of fraudulent listings and continuously scans listings for suspicious elements such as vague details, asking for deposit without the ability to view the property, asking for deposit without asking for background information, owners who are “out of the country,” rent “too good to be true,” request to wire money, etc.
Our pilot will be focusing on developing and proving out the key features of our platform that would transform the apartment search market. We have a demonstration of demand based on both personal and surveyed feedback on the lack of a cohesive platform that demonstrates the following characteristics:
- Street and neighborhood information to provide highly requested visual prowess
- AI-powered dynamic, criteria-based matching without sifting through hundreds of listings
- Highly capable fraudulent ad detection
- Transparent pricing
First, we will work to develop an AI powered curation algorithm. This would require users to initially select criteria and review a select number of listings after which the algorithm would dynamically learn and curate apartment listings. Our pilot would build this initial algorithm using mock selection data and volunteer data.
Second, we would in parallel build an AI powered fraud detection software. Human generated rule sets are the primary practice today, but we seek to create a system that will learn, predict, and act on elimination of fraudulent postings. With craigslist being the most highly used platform, it is also the most prone to fraudulent listings. While competitors have fraud detection, fraud has key characteristics: long tail of many unique cases, quick pattern changes, and a highly dynamic opponent. We seek to combat with a machine learning tool that will reduce false positives, manual reviews, and over-intrusive countermeasures.
Third will be the development of a transparent pricing model using big data. Analogous to True Car’s successful price transparency, we would collect pricing information on apartment leases and pass it on to consumers cost-free based on actual transactions relayed by brokers or available through other platforms. With weekly updates, customers can enter in the zip code they are looking for and get a dynamic pricing report that they can bring to a broker to eliminate the hassle of price opaqueness.
Lastly, we would partner with tools such as Google Street view to use AI curated feeds of neighborhood views and feels that would not only provide mock views from the home, but also views of attractions and key locations nearby.
There are many real estate websites in the United States. For example, companies such as Zillow and Trulia received 36 million and 23 million monthly visits, respectively, as of July 2017. However, these companies have a very broad focus and are targeted towards real estate agents as opposed to the rental market. For example, 71% of Zillow’s revenues come from Premier Agent, which is a suite of marketing and business technology products and services geared towards real estate agents and brokerages. Zillow only made 10% of its revenues on rentals by advertising to property managers and other rental professionals (on a cost per lead, cost per click, or cost per lease basis). Beyond having a broad scope, many of these more established real estate websites are tedious to use and aren’t personalized to individuals.
There are several newer companies that are trying to leverage AI in the real estate market. For example, REX Real Estate analyzes hundreds of thousands of data points to identify likely buyers. The company analyzes data such as recent purchase decisions and history of home ownership in order to target potential buyers with ads. However, REX Real Estate is focused solely in the buyer/seller market rather than the rental market.
Further, companies such as Airbnb are utilizing AI in the short-term rental market. Airbnb leverages AI in three key areas: “search ranking and matching of hosts and guests; empowering hosts to understand how factors such as pricing affect their business; and keeping the community safe from issues like fraud or abuse.” Airbnb helps demonstrate the value of using AI for listings and while there is some risk that the company could leverage its technology in the rental market, the company is dedicated solely to the travel market. Airbnb markets itself as the “global travel community that offers magical end-to-end trips” and offers short-term rentals as well as tools for a better trip such as experiences and restaurants. The needs of customers on Airbnb versus the rental market are significantly different – in terms of pricing (e.g., daily rate versus monthly), location needs (e.g., focus on tourist sites with Airbnb versus neighborhood, traffic patterns, parking, safety, schools, etc. in the rental market), and how much time consumers invest in the process (e.g., review a couple of listings on Airbnb versus looking in person with the rental market). As such, the customer needs as well as the requisite algorithms are substantially different.
We are asking for $185,000 to cover 1 design engineer, 1 application engineer, and 1 data scientist. This will provide AptHero enough runway to create a pilot product for our first city. With this money, we believe that AptHero can position itself as solely focused on the rental market, starting small and niche, and marketing ourselves as the rental company that is using AI to simplify apartment rentals.
- eBiz MBA as reported in Statista. United States, July 2017, estimated unique monthly visitors