Information Product Design — Airbnb Restaurant Proximity and Airbnb Safety Rating IPs

COMPANY INTRODUCTION

Airbnb is a vacation rental online marketplace company. Airbnb has seen rapid growth over the past couple of years, and it has positioned itself for even more growth opportunities in the future. It is interesting for us to work with a company that presents varieties of growth potential.

Airbnb provides consumers with in-expensive local homes and other interesting local venues that differ drastically from the traditional hotel experience. It also offers hosts an easy and cheap way to find customers. Our Information Products (IPs) will be designed to help Airbnb grow in the vacation rental industry. Airbnb stakeholders and management will be interested in this project.

BUSINESS PROBLEM

Airbnb has received many complaints and even potential lawsuits regarding the safety of their hosts properties. Airbnb needs to find a unique way to help consumers and hosts understand safety in the surrounding area. This unique solution is found in the Safety Rating IP.

Hosts have expressed a need to increase their property’s revenue, but do not have the overall knowledge required to do this task. Many hosts turn to Master Class which has a class that teaches consumers how to use price analytics to increase their annual profit using their Airbnb. Airbnb needs to create a tool for hosts to manage their properties and keep revenue in Airbnb. This is an opportunity to create something for hosts to subscribe to the service and both Airbnb and the host will profit. The tool for this analysis is the Host Portal Price Analytics IP.

Airbnb has fallen behind their competitors with interactive maps and local points of interests. Expedia, a direct competitor of Airbnb, has an embedded Google Map with a feed below of close by points of interest. In order to stay competitive, Airbnb needs to create an IP that meets the needs consumers need to find interesting activities near their Airbnb, starting with the Restaurant Proximity IP.

INFORMATION PRODUCTS INTRODUCTION

Restaurant Proximity IP:

Our Airbnb restaurant proximity IP will simply improve the convenience for travelers and identify the cooperation opportunity for Airbnb and Yelp.

Airbnb’s competitors such as Expedia, Tripping, Booking are providing a more convenient platform than Airbnb does. You can check out the nearby attractions, nearby restaurants, nearby transportation (bus, subway etc.) from the web page directly. However, Airbnb has not provided this function yet. As a user of Airbnb for a couple of times, I didn’t have a great user experience from its APP or webpage because the information of apartment surroundings was not sufficient. In this digital world, people value saving on time, convenience, and experience above all. They prefer to be efficient with their time, rather than wasting it on tedious tasks. Thus, they are more likely to spend their money on convenience. If Airbnb wants to win more Millennials and Generation Z to its customer base, improving user experience to its webpage and APP will help.

Airbnb Safety Rating IP:

This IP provides a detailed view of all relevant crime stats and price comparisons for each neighborhood in the desired locations. We will also include inputs (reviews and experiences) from other users. This will allow consumers to plan their trips accordingly based on safety concerns and budget limitations. For example, travelers might want to be extra cautious outside if the robbery rate is extremely high. Airbnb will also provide relevant and helpful tips to travelers living in high risk areas. The hosts will also be advised to deal with safety concerns.

For the Safety Rating IP, this is a unique IP to separate Airbnb from its competitors and addresses the Competitive Rivalry aspect of Porter’s Five Forces. This IP will allow customers to feel safe about booking in an unknown area. Also, this IP addresses the Buying Power and Threat of Substitution because the IP is unique and if customers want to feel safe, they will book with Airbnb over another travel site that does not have a safety IP.

Restaurant Proximity IP and Airbnb Safety Rating IP are the must-have if Airbnb wants to be competitive. These two IPs actually represent part of the Maslow’s basic needs, food and safety. When we go travel, the first thing we would want to check if the place is safe. The second is to find a place to eat. People are motivated to fulfill basic needs before moving on to other, more advanced needs. Therefore, providing a section that shows the surrounding information such as nearby restaurants, safety rating of the neighborhood is essential. These two functions simply will improve user experience at a higher level.

Host Portal Price Analytics IP:

This IP provides information on the average rental prices based on six criteria, Neighborhood, Property Type, Room Type, Number of bathrooms, Number of bedrooms, and number of accommodations. Hosts can check the dashboard to see if their pricing strategies are appropriate given their house type and relevant features. Master Class currently offers a class on improving the earning potential of host’s Airbnb properties through a similar class, this is a Threat of Substitute for Airbnb. However, Airbnb can provide better and more accurate information on their own hosts properties. It is an opportunity to gain more revenue for both Hosts and Airbnb in general. The IP also addresses the Power of Suppliers force because it is adding another supplier of this information to the market. Airbnb should see less hosts taking this Master Class and transferring over to Airbnb’s Host Portal Price Analysis tool. The cost of using the tool is less than taking the Master Class. The Master Class is a one time series, the IP would provide life time value to the host.

DATA COLLECTION & PREPARATION

We use software such as R and Python to do the data wrangling, cleaning and processing for the two IPs. Then we combine two IPs and use XGBoost regressor to analyze the factors such as the number of nearby restaurants and safety rating that might influence the listing price. We use Tableau for our IPs visualizations. The detailed data processing steps, merging process, the sample of the original datasets, the final dataset can be found at the end.

IP: Airbnb Restaurant Proximity

The datasets used for the first IP — Airbnb restaurant proximity — are from Kaggle Yelp

Dataset and Inside Airbnb Database.

First of all, the Yelp Dataset can be found from Kaggle, this dataset from Kaggle is retrieved from Yelp Web API directly. Therefore, the source is reliable. There are five different datasets such as business, checkin, review, tips, users under the Yelp dataset. For the purpose of this project, we only use business dataset. This dataset tells us the general information of each merchant. This Yelp dataset can be found from this link. https://www.kaggle.com/yelp-dataset/yelp-dataset?select=yelp_academic_dataset_business.json

The second dataset we used for the Airbnb restaurant proximity IP is from Inside Airbnb

Database — Toronto. This dataset can be found from this link. http://insideairbnb.com/get-the-data.html

IP: Airbnb Safety Rating

The dataset is merged from two different datasets. The Airbnb dataset which is the same from above. The second dataset is the “Neighbourhood Crime Rates” data found on Toronto Police Service Public Safety Data Portal, Neighbourhood Crime Rates | Toronto Police Service Public Safety Data Portal. This data is from 2014–2020 and includes crime counts and crime rate per 100,000 people for assault, auto theft, break and enter, theft over, homicide and shootings. For our purposes, we selected the crime rates per 100,000 people for assault, auto theft, break and enter, theft over and homicide during 2020.

IP: Host Portal Price Analytics

The Inside Airbnb Dataset was used to develop this IP. After delving into price analysis to determine the major factors when pricing an Airbnb property, we discovered that safety of the neighborhood and restaurant proximity had very little to do with the actual price. For example, some of the most expensive Airbnbs are in Bayview Corridor, but the crime rate is also on the higher side. This means that there is another determining factor when calculating price.The Host Portal Price Analytics IP explores some of those other factors. However, it could be just because the city we are using is Toronto where is one of the top 10 safest cities in the world. Safety rating can be an important factor for the listing price if we have chosen other cities such as New York or London.

If you are interested in checking the plots that are generated by the regression, please check the below. We only include the private room type and share room type because the R square of these two regressions are above 80%, which is decently high. But the R square of the entire room type and hotel room type is low, respectively.

INFORMATION MANAGEMENT

Each of these IPs will need to be managed as part of a well designed production process. This process starts with assigning an information product manager for each IP. These products managers will assure the quality and flow of the information products.

IP: Airbnb Restaurant Proximity

This IP requires a partnership with the data collector, Yelp to manage the information efficiently and keep it up to date. The process would be Yelp, or the IPM (Information Product Manager) for Airbnb, would take real time data from Yelp and upload the data to Airbnb’s centralized data warehouse. If uploading real time data is proving to be too challenging, data can be transferred from Yelp on a daily basis. The IPM must create appropriate measures and feedback to ensure the quality of the data at this stage. Once the data is at Airbnb’s data warehouse and the quality is assured, it is cleaned and the IP interface is reloaded each night.

The IPM must monitor data consumer needs to ensure that the IP is meeting those needs. For example, the IPM receives feedback that travelers want to know about more than just restaurants near their Airbnb. The IPM reviews other competitors and realizes they already have this feature. The IPM would need to adjust the IP to include all relevant activities near Airbnbs by adjusting the data sets criteria from Yelp.

Improvements and adjustments would be suggested by the IPM based on feedback and measure for consumers. However, some immediate improvements that can be implemented would be overlaying the maps so that the restaurants and the Airbnb are on the same map. Including a short list of highly ranked restaurants below the maps.

Restaurants would also benefit from this IP to target marketing to Airbnb customers. Airbnb may also be able to find a strategic partnership with local restaurants using this IP. This would increase overall user experience and keep Airbnb up to date with competitors who already have this kind of IP.

Data collector: Yelp, restaurant customer that review on Yelp. Data custodian: Airbnb. Data consumer: Travelers

IP: Airbnb Safety Rating

This IP would require partnerships with local police and government officials to implement properly. The IPM would ensure the data quality, again through measure and consumer feedback. The IPM and local government would only need to upload this data on a monthly basis at first to meet the needs of the consumers. If trends showed consumers had a need for real time updates, similar to the ones Northeastern students and faculty received for NUPD, the IPM would need to work with the local police departments to provide real time updates. This is why local data warehousing would be important.

Data collector: Toronto Police Department. Data custodian: Airbnb. Data consumer: Travelers

IP: Airbnb Host Portal Price Analytics

This IP would not require a partnership with an external source. Airbnb would still need an IPM, however their data collector and the data consumer are the same, Airbnb is processing the data internally and producing the IP for the processed data. The IPM would again ensure the quality of the data through feedback and metrics established by the IPM. This IP could be real-time because as new or existing hosts update listings, the information can be processed in Airbnb’s IP because there is no external sourcing.

Currently Airbnb charges hosts 3% of the overall income at a fee for utilizing Airbnb’s service. Creating this IP provides hosts with an opportunity to gain more revenue for their properties. Airbnb could charge an extra 1% as a fee for using this IP service. In turn, increasing the host’s and Airbnb’s total revenue.

Data collector: Airbnb host. Data custodian: Airbnb. Data consumer: Airbnb host

ANALYSIS

IP：Airbnb Restaurant Proximity

Currently, almost all of Airbnb’s competitors such as Tripping, HomeToGO, Vrbo are following the model and style from Airbnb. When you search any of these websites, you would feel you are on an Airbnb website. The location section of Airbnb website is only showing the home sign on the map, but when you click it, nothing is shown. Our IPs will enable travelers to receive the essential information of the home such as average scoring rating, nearby restaurants that are within 25 mins walking distance. Moreover, when they click the restaurant sign, the essential information of that restaurant will be shown, and a small Yelp pop-out window that is for that restaurant will be also shown. Travelers can check more information of that restaurant such as reviews and menu. This will substantially reduce the searching time for travelers. Our IPs, without doubt, will improve customer experience. This will increase the site traffic and possibly revenue if Airbnb is the pioneer of doing this. This will attract them to stay on Airbnb websites.

IP: Airbnb Safety Rating

The crime IP will use various crime data to provide the travelers with overall safety levels for different areas in the city. We used Crime and Safety Grade — Niche as a guideline in developing our own safety rating formula. Their formulation is as follows: Assault Rate (20%), Robbery Rate(20%), Murder Rate (15%), Burglary Rate (10%), Vehicle Theft Rate (10%), Composite Crime and Safety Score (7.5%), Larceny Rate (7.5%), Drug Related Death (2.5%), Excessive Drinking (2.5%), Firearm Related Deaths ( 2.5%), and Premature Death Rate (2.5%). Assault, Murder(Homicide), Burglary(Break and Enter), Vehicle Theft will have the same proportions in our formula. We will tune up Robbery from 20% to 25% because we think it is more relevant to travelers. We will also add TheftOver with 0.20 weighting which is theft of dollar value over $5000. Travelers in foreign countries will often spend a lot of money shopping so TheftOver would be an important measure of overall safety. The average price and the average crime level of each neighborhood will be shown to the travelers. This will allow them to make informed decisions based on the price and crime of each area.

Airbnb Restaurant Proximity IP & Airbnb Safety Rating IP

Benefits to the hosts:

From the regression analysis, the numbers of amenities and some types of amenities such as greetings from the host, free street parking influence the price. Therefore, the host can provide a short introduction to their area when the travelers arrive home. The better customer service to the travelers, the higher chance to have more travelers to book the place, then increase host’s revenue. Also, the host can check the amenities summary that shows the common and uncommon amenities currently provided by the hosts. This helps the host to check whether they need to add more amenities because others are already providing to the travelers.

Benefits to the travelers:

Intuitively, when we travel to a new place, the first thing we do is to find a place where it is safe to live, a place to eat. This ensures we can survive from that place. Therefore, travelers will like our two IPs because they can check the nearby restaurants, and the information of each restaurant such as average rating, total review counts etc, they also can check the safety rating of the neighborhood. They don’t need to go to google map to find the nearby restaurants and then go to yelp to check the restaurants information, they also don’t need to go to governmental websites to check the safety rating of the neighborhood. But instead, they can receive all this information from the Airbnb APP.

Benefits to Airbnb:

1. Increase revenue. These features added to the current Airbnb app will simply improve customer experience. As we mentioned how this feature will benefit the travelers, travelers will like this APP more, then they will use Airbnb more than other travel APPs. Accordingly, revenue will be increased.

2. Second, in order for Airbnb to stay competitive in the market and satisfy the Competitive Rivalry Force, they need to develop this IP, because some features are already used by Airbnb’s competitors such as Expedia. Airbnb is already a popular site and will be more attractive to customers if this IP is developed. This will stop customers from using other sites to find out which restaurants are near their Airbnb and decrease the Threat of Substitution and the bounce rate of Airbnb’s booking website.

3. Potential partnership with Yelp and the restaurants with high ratings. We believe our Restaurant Proximity IP will not only benefit Airbnb, but also Yelp and the restaurants with high ratings. Currently, consumers are using Google or Yelp to check restaurant information. But once Airbnb partners with Yelp, travelers will use Yelp to check for restaurants, instead of going Google. The website traffic of Yelp will definitely be increased, thereby potentially increase revenue for Yelp.

As to the restaurants with high ratings. Once this feature is added to the Airbnb map, the system will recommend the top rating restaurants. Intuitively, customers like good restaurants. Airbnb can definitely take commission from these restaurants if they want to be added to the map.We also recommend Airbnb to add a function that enables travelers to make a restaurant reservation link from its app or website.

IMPLEMENTATION AND REAL LIFE CASES

In our presentation to Airbnb, we would use example based demonstrations to showcase how each of our three Information Products will add value to Airbnb. The proposed examples are below. For the demo of these IPs, please check the attached tableau file on Canvas.

Crime and Safety IP — Designed For Travelers

Bay Street Corridor has the highest number of crimes committed per 100,000 people and is decently expensive. It has an average price of $152 and 986 crimes committed per 100,000 people. Using our IP, travelers who don’t need to be in this area could easily identify nearby areas that have better safety ratings and cheaper rental prices. For example, the Church-Yonge Corridor is right next to Bay Street Corridor (within 20mins walking distance) with an average price of $128 and average crime occurrences per 100,000 people of 816. For the travelers who are more concerned about safety and have a higher budget, there are also two beautiful neighborhoods within walking distance of Bay Street Corridor that have significantly lower crime rates. Namely, Annex (avg. price = $174 and crime rate = 321) and Waterfront Communities (price = $195, crime rate = 375). Everything is designed with the traveller’s experience in mind. In the end, the travelers will have more information to make informed choices and will be extremely satisfied with the Airbnb experience.

Host Portal Price Analytics IP — Designed For Hosts

Let’s assume James, a property owner, who has an entire apartment with 1 bedroom and 1 bathroom for rent in the Bay Street Corridor. James wants to rent out his apartment on Airbnb. He has passed the vetting process and now enters all his information into Airbnb’s website. This data, once submitted, is analyzed and quality is checked, it can be loaded in the IP for not only James to view but all other users as well.

James could get valuable information about prices based on other similar properties by using our IP. In this case, assuming the property accommodates 2 people, then the average price charged for the entire place would be $109. If he has been charging $150 and wondering why no one books the place, then by using our IP he will know he has been overcharging. Meanwhile, if he has been charging $80 then he could raise the price to $109. This IP will improve the overall profit level for James.

This IP could also help hosts to predict the marginal increase in prices given certain modifications to the properties. Using the same example from above, now James wants to modify his apartment and add an extra room to improve profit but he has no idea how much he can charge for a 2 room apartment. Using our IP, he will easily identify that the average price for a similar property would be $94. The extra modification will not improve price and will not be worth the construction costs. In the end, our IP just saved James time and tons of money, while increasing James’ and Airbnb’s revenues.

Airbnb Restaurant Proximity — Designed For Travelers

Grace is visiting her friend who is studying at the University of Toronto. The University of Toronto is located in the Church-Yonge Corridor neighborhood. Based on this neighborhood, Grace is looking for a place close to the university and has a 95 average review score, and she wants to rent the entire home. After filtering out all the requirements, she receives many results. But because she also wants good Asian food such as Japanese sushi or Korean with at least a 4-star rating. After filtering out this requirement, she only receives a few results from the restaurants. Now Grace wants to find a place based on these good restaurants. Because Grace wants to find a home where it takes no more than 10 mins walks to the restaurant, which is kind of less than 0.4 miles distance. But because Grace also wants to live closer to the public transportation, she decides to choose an elegant private house-heat of downtown home which is next to the Sushi & BBbop restaurant. The distance between the home and the restaurant is 0.2 miles.

Outstanding features:

1. When you click the name of the home or the name of the restaurant, a small box that includes the basic information of the home or the restaurant will pop out, respectively.

2. When you click the home name, a google map small window of the home location will pop out. This is for people who want to check more detail of the home surroundings. If you don’t want to see the google map window, you can simply close it.

The same function of the restaurant side. A small window of the Yelp page of that restaurant will pop out. This is for people who want to check more detail of the restaurant such as reviews, menu. If you don’t want to see the Yelp page, you can simply close it.

The detailed steps of data preprocessing for the two IPs.

1. Data collection

Airbnb Restaurant Proximity IP

First of all, the Yelp Dataset can be found from Kaggle, this dataset from Kaggle is retrieved from Yelp Web API directly. Therefore, the source is reliable. There are five different datasets such as business,checkin,review, tips,users under the Yelp dataset. For the purpose of this project, we only use business dataset. This dataset tells us the general information of each merchant.

This dataset can be found from this link.

https://www.kaggle.com/yelp-dataset/yelp-dataset?select=yelp_academic_dataset_business.json

The original datasource can be found from this link.

Yelp Dataset

ii) The second dataset we used for the first IP is from Inside Airbnb Database — Toronto.

This dataset can be found from this link.

Inside Airbnb. Adding data to the debate.

Crime and Safety IP

The third dataset is used in conjunction with the Airbnb dataset to develop our Airbnb Safety Rating IP. The dataset is Neighbourhood Crime Rates (Boundary File) | Toronto Police Service Public Safety Data Portal found on the Toronto Police Service Public Safety Data Portal.

2. Clean and integrate the data sets, as necessary.

Airbnb Restaurant Proximity IP

1. Yelp business dataset

From the data summary above, we know that there are some problems under the “categories” variable. For this reason, we unlist the column, then filter out the categories only in restaurants. Under the “city” variable, we subset to only contain Toronto because we are only interested in Toronto. Under the “is_open” variable, we subset to only contain the restaurants that are still open. In the end, we only select variables of name,address,latitude, longitude,stars as our variables from this dataset. This cleaned dataset contains 5,462 observations and 5 variables.

2. Airbnb dataset

As mentioned from the data summary above, there are missing values from the dataset. However, it does not affect our project because those columns with missing values can be excluded for the purpose of this project.The columns — latitude and longitude — we use to join with the Yelp dataset have no missing values. However, at this moment, we keep the dataset as it is. There are 15,832 observations and 74 variables.

3. Toronto Crime Dataset

The dataset has no null values and has very high usability.

3. Explain how the data elements are combined to form the new information products.

Airbnb Restaurant Proximity IP

To form the first IP — Airbnb restaurant proximity, we use the cleaned Yelp dataset to join the Airbnb dataset by using latitude and longitude. However, no matching data came out if we use the original latitude and longitude from the two datasets. For this reason, we create two new columns latitude1 and longitude1 that are copied from the original columns of latitude and longitude from both Yelp and Airbnb datasets. Then we only keep the first two decimal points, and remove the rest of the decimal points. It is because no matter how we change the last three decimal points, it will not make the tenants walk much longer(always within 25 mins walk). For example, we use “- HEART OF TORONTO ‘’ apartment and “Banknote Bar ‘’ restaurant as an example. The original latitude and longitude from the restaurant is 43.64385,-79.40236,respectively. The original latitude and longitude from the apartment is 43.64183,-79.40117,respectively. We convert these numbers into 43.64&-79.40 for the restaurant ,43.64&-79.40 for the apartment. Then these created two columns with two decimal points can be used to inner join together. To confirm our logic is correct, we use both the original latitude and longitude to check on Google maps, we can see that it only takes 4 minutes from the apartment to the restaurant. The map screenshot can be checked below. However, based on the algorithm from our calculation, it takes 4.5 minutes to walk. For our convenience, we round all the numbers into integers. One minute difference will not create a huge problem.

After we join the two datasets, we receive 4,742,144 observations and 81 variables. However, since we are only interested in the apartment name, restaurant name, restaurant address, the average restaurant rating. In the end, we group by apartment name, restaurant name, restaurant address and calculate the average rating for each restaurant. This returns us 76,051 rows and 4 variables. From this, we can see the near-by restaurants (always within 25 mins walk) based on each apartment, and we can see the restaurant address and the average rating for each restaurant. Some restaurants might show up more than one time because they might belong to a chain restaurant, but they have different locations. Or some restaurant names might show up more than one time under different apartment names. It is because different apartments can be around the same near-by restaurant.

The missing values from host_neighbourhood are treated as ‘unknown’ because it is not appropriate to remove them due to the size. We keep the missing values from restaurant_address and postal_code as they are, it does not either impact the regression analysis or visualization.

Crime and Safety IP

After inspecting the variables, I’ve decided to use the 2019 rates for all 6 of the major crime indicators. All 6 indicators will be relevant and helpful to the consumers of Airbnb. There are 140 neighbourhoods in Toronto, I’ve combined the Airbnb dataset and the Crime dataset based on the neighbourhoods variables in both datasets. Each listing in the Airbnb dataset now corresponds to the 6 crime indicators based on the neighbourhood of the listing. I’ve also included a “safety” column which is the weighted average of the 6 crime rates. For now it is just a placeholder, we need further research to develop a more refined algorithm in determining the average safety rating for each neighbourhood.

These are the columns for the combined dataset.

id, name, description, neighbourhood_cleansed, price, safety, Assault_Rate_2019, AutoTheft_Rate_2019, Robbery_Rate_2019, BreakandEnter_Rate_2019, TheftOver_Rate_2019

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