Author: rnewman0

AptHero – The AI Solution Simplifying Apartment Rentals – Is Asking for $185K (Team: Deep Learners)

rnewman0

Opportunity:

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.

 

Solution:

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.  

 

Pilot:

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:

  1. Street and neighborhood information to provide highly requested visual prowess
  2. AI-powered dynamic, criteria-based matching without sifting through hundreds of listings
  3. Highly capable fraudulent ad detection
  4. 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.

 

Competitors/Risks/Feasibility:

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.

 

Funding:

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.

 

Sources:

 

Team Members:

Sam Steiny

Rosie Newman

Gergana Kostadinova

Javier Rodriguez

Pitch: Crowdsourced AI Security

rnewman0

Opportunity:

Crime has substantial societal costs: local government resources, medical bills, increased spending by companies and individuals on security, and depressed property values, to name a few. The global security market was valued at $70.02 billion in 2016 and is projected to grow substantially in the next eight years. Rising terrorism and mass shooting concerns in the US and abroad has led to a surge in the adoption of security systems. Additionally, technological proliferation, smart city infrastructure, and advanced analytics are transforming the industry.

The market is sizeable arguably because people have high willingness to pay to protect their loved ones and their property. Robberies, shootings, kidnappings, and even missing pets generate high levels of physical and emotional stress and significant costs for the victims and their communities. Effective use of analytics for crime detection and prevention is thus the key to saving and improving lives.

 

Solution and Feasibility:

Our proposal is to tackle security challenges by aggregating data from public and private cameras and sourcing image, video, and voice recordings from individuals. Currently people witnessing a crime are often willing to make recordings but there is no centralized platform for affected communities that can aggregate the incoming data. This causes delays in law enforcement reaction and some evidence likely never gets reported. A centralized crowdsourced data platform augmented with data from private surveillance cameras and public cameras such as ATM and supermarket surveillance can be effective in crisis situations such as location a runaway robber, terrorist, or missing people or pets. At later stages, the company will be able to identify high risk areas with limited coverage and augment the system capabilities by installing cameras or sound sensors there. Local governments or businesses interested in the area can subsidize the cost of the new sensors.

Further capabilities for crime prevention that the company will develop is to use AI and machine learning to identify faces of criminal suspects in the data streams. Machine learning can be used to set alerts based on detecting things like people with face masks, firearms, or unattended bags in crowded areas. Similarly, machine learning can be used to scan the data for the faces of missing people or images of runaway pets.

The number of security cameras in North America effectively doubled between 2014 and 2016. Along with an increase in mobile devices, the proliferation of drones and satellites will provide additional data for the platform.

 

Pilot:

Crime detection and prevention solutions will be of interest to local authorities. We plan to pilot the technology by partnering with local authorities who are interested in using AI to help improve security. For example, in New Orleans, Mayor Mitch Landrieu has proposed a $40 million crime-fighting surveillance plan, which will combine municipal cameras with the live feeds from private webcams operated by businesses and individuals. As such, we plan to team up with New Orleans and other cities to demonstrate our capabilities. Other areas for expansion include airports, crowded public spaces, and college campuses.

 

Competitors:

There are several companies which are using AI to help detect and predict crime. ShotSpotter, for instance, already uses sensor data to detect and alert authorities in the case of gun fire. ShotSpotter can provide information on the type of weapon and the likely location where it was fired with accuracy within 10 feet. The company had an IPO in 2017 and claims to have presence in over 90 cities in the US. Hikvision, a Chinese security camera producer, uses facial recognition to search for criminals or detect suspicious activity such as unattended bags. This technology is being used in the US in varying situations, such as by the Memphis police (for crime) or by the U.S. Army (to monitor a base). Many of the competitors are focused more on sensors and security cameras, which will have more power and impact if they can be utilized along with image, video, and voice recording from individuals. As such, our centralized crowdsourced data platform will provide a richer and more holistic view of the data. Further, many of our competitors are very specialized and niche. Over time, we plan to expand our capabilities to focus not just on crime but also on good Samaritan acts such as finding missing items or returning lost pets.

 

Risks:

In the wrong hands, the technology can be used for the opposite purpose and actually aid crime. Criminals can identify when people leave their homes on vacation or target individuals. This will be mitigated by building in special access rights into the platform so that the general public can upload images and videos but special groups such as law enforcement officers can utilize the data.

Privacy concerns also have to be addressed. These concerns will be mitigated by ensuring transparency about the data sources and using data sources that the police can already get access to. Despite data privacy concerns, in crisis situations people are often very willing to assist authorities as much as they can to speed up the recovery of their loved ones.

 

Sources:

 

Team Members:

Sam Steiny

Rosie Newman

Gergana Kostadinova

Javier Rodriguez

Fun Run

rnewman0

Opportunity:

The global athletic footwear market size was valued at $64.3 billion in 2017 and the use of fitness apps has grown by 330% in the last 3 years. With fitness becoming an increasing part of a young professional’s life, finding ways to improve the efficiency of a workout or prevent injury are key to increased fitness success.

Solution:

Fun Run uses machine learning and sensors embedded in running shoes to track particular metrics and improve a runner/walker’s stride. In addition to tracking steps, heart rate (in the foot) and typical fitness tracking metrics that are currently available in the marketplace, Fun Run uses weight distribution to measure a user’s posture and stride. It also scans the running surface and analyzes data on both the surface type (soil, sand, concrete, etc.) and condition (dry, wet, icy, etc.). By continuously tracking and learning performance, the app can make recommendations based on a user’s goals. For example, if a user is concerned about hip pain, the app can let the user know if he or she is putting too much pressure on one side, then recommend relevant stretches/therapies and adjustments to the way they run in order to correct it. The algorithm can also use predictive analytics and alert people of the potential for injury or excessive soreness before people have experienced any pain.

Feasibility and Commercial Promise:

Athletes are increasingly interested in tracking their fitness. Of people who exercise at least monthly, 30% have a wearable fitness tracker and 29% use a mobile app to track fitness stats. Another ~25% plan to use these features in the future [1]. Despite significant commercial promise, the activewear market is saturating. 37% of fitness junkies state that brand is important, and 90% opt for high-end activewear brands [2]. As such, branding and high-performance will be integral to the success of Fun Run. Some of the features we plan to utilize may be expensive in the short term, such as sensors to analyze surface type and condition. The cost could be brought down by pulling in data from other sources (e.g., weather websites) or by relying on manual entry of some data.

Pilot:

A pilot would be rolled out at university track and field programs. We would work closely with a team of physicians and sports science staff at the University of Chicago (or comparable University), to certify and monitor all data and recommendations. We would seek to have each tailored recommendation and pain management solution be created and reviewed by our physician team to ensure proper legal protocol and medical viability.

Our pilot would seek out volunteer athletes who can provide large usage data sets (e.g., long distance runners) that would allow us to collect data on running strides, foot placement, weight pressure, shoe type and other relevant pieces of information. Additionally, we would bring the volunteers in for an initial screening process to create a baseline model of their postures, existing pain or medical issues, bone structure, etc. which would allow us to make better assessments from the running data we collect.

We would analyze the recommendations from our analytics software with our physician team to gauge relevance, accuracy, and benefit to the athlete above the normal utility of simple pain-based care.

Competitors/Risks:

A number of devices in the market measure biometrics such as heart rate and/or activity indicators such as steps and speed. They mainly allow people to track progress. Our product will be a leader in using AI to generate predictions based on such data combined with weather, running surface, and more granular biometrics information such as weight distribution. A competitive product on the market is Lifebeam VI, the self-proclaimed “first true AI personal trainer.” The product is a voice-activated Bluetooth biosensing headset with AI personal trainer. VI is focused on making you a proficient runner and is priced at $249.99. VI doesn’t analyze posture and weight distribution and doesn’t seem to be able to warn about potential injuries or excessive stress on your body. Other potential competitors are Google and Apple who can build on their personal assistants to also function as personal trainers. Existing user base and data will be a big advantage for them.

Sources:

[1] Mintel Report. Exercise Trends. US, October 2016.

[2] Mintel Report. Activewear. US, October 2016.

http://www.netimperative.com/2017/09/health-fitness-app-usage-grew-330-just-3-years/

https://www.engadget.com/2017/04/24/ai-personal-trainer-vi-headphones-running/

Team Members:

Sam Steiny

Rosie Newman

Gergana Kostadinova

Javier Rodriguez