Abstract

With the cost of living rising almost exponentially in 2022, reliable and affordable housing for college students is becoming increasingly rarer. This housing company is looking to capitalize on this gross unmet demand by constructing rental units that appeal to students at The George Washington University (GW). Focusing on studio apartments, this study examines how monthly rent, location, distance to metro, square footage, utilities, and amenities impact the apartment’s marketability. However, one key insight gained from conducting a pilot analysis is that the study may be examining too many attributes to result in a concrete conclusion. Therefore, decreasing the number of attributes may be ideal to ascertain precisely what are the most marketable and profitable apartment features.

Introduction

Rising housing prices and limited housing options are leaving students scrambling for almost anything with four walls. At many higher-institutions of education in the United States, the waitlist for on-campus housing is hundreds of names long. However, at GW, over 43 percent of students choose to live off campus. (Dashboard, 2022) This was an easy choice to make five years ago when most rental prices were less than those of dorm housing. Unfortunately, inflation and a world-wide pandemic caused the price of housing to soar over the last few years. In 2021, 35 percent of college students claimed they were unable to afford the high rent in their college town. (College, 2021)

To bridge this housing gap and aid the next generation in a manner that is financially feasible, the deciding factors in choosing off-campus studio housing for GW students in the WMA must be deduced. What is the best location for housing? Would students be willing to move further away from campus if rent was cheaper? Are they willing to pay a fee for certain amnesties, such as wi-fi included? These are all critical questions that can help determine what specific housing option is best to produce and take full advantage of any untapped market potential.

Survey Design

This survey is meant solely for GW students. Due to this, the survey was designed to commence with four eligibility requirements: Are you 18 years of age or older?, Have you read and understood the above information?, Are you a robot?, and Are you a GW student? All of these questions have solely binary options, yes or no. Once the respondent’s eligibility was confirmed, it became a priority to gather knowledge on what type of GW student they are. Student-specific demographic data such as their academic year and housing situation were collected.

The next part of the survey educated respondents so that they could better respond to the conjoint questions. This educational section listed all the attributes that the respondent must take into account. Some of the key product attributes were fairly straightforward such as monthly rent in US dollars, the amenities, distance to the closest metro, and utilities. However, for the more figurative decision variables, care was taken to provide concrete examples for the respondent to reference and better understand the attribute. For the location, possible neighborhoods the building could be constructed in were listed. In addition, example floor plans were provided for each iteration of square footage so that the respondent could better relate to a seemingly arbitrary number. The attributes were limited to either binary or linear factors with only three delineations. This was done to generate a limited number of alternatives for the conjoint questions as well as make the end analysis results more concrete. Detailed descriptions of the key product attributes studied are as follows:

  • Monthly Rent: Flat monthly rent rate in United States dollars. This does not include utilities or access to amenities unless otherwise stated. There are three possible rent rates, $1,500, $2,000, and $2,500. Rent is often the number one concern for apartment-hunters so it was paramount that it be included in this survey. As a building company, we would like to know how much people are willing to pay for rent in order to maximize our end profit.
  • Location: Miles away from GW campus in Foggy Bottom, including neighborhoods in D.C., V.A., and M.D.. Some may prefer to live in the city whereas others prefer the suburbs of Virginia. There are three possible distances to choose from, less than 1 mile away from campus, about 3 miles away from campus, and around 5 miles away from campus. Certain locations are cheaper to build in than others. Thus, it would be useful to know the optimal place to construct a new building.
  • Distance to metro: How long it takes to get from your apartment building to the nearest metro station based on the average walking speed of an individual. Three distances are available to choose from, 5 minutes walking, 10 minutes walking, and 15 minutes walking. Some may be willing to live further away if they are closer to a metro station with easy access to campus.
  • Square footage: The size of the apartment in square feet. There are three possible apartment sizes available, 500 sq ft , 600 sq ft, and 700 sq ft. An example floor plan is as follows:
    >
    Some people who perhaps own a pet or need a lot of closet space may prefer a larger apartment while others may prefer a smaller space. The size of one apartment unit can change how many total units are present in the building. Thus, altering possible revenue amounts and construction costs.
  • Fitness center: A binary yes or no attribute as to whether or not the building includes a free gym area for building residents. Having an in-building gym could save gym membership costs and travel time. Therefore, encouraging a respondent to agree on a higher monthly rent rate.
  • Twenty four seven concierge: A binary yes or no attribute that determines the existence of a desk manned by a trusted individual at all times of day to collect packages and verify legal entry. This added security can be a source of great comfort, especially to those living alone in the city.
  • Parking: A binary yes or no attribute that determines if parking is accessible to building residents. This amenity is not included in the rent. If a respondent owns a car, parking may become a deciding factor in deciding where to live. This is especially prevalent in the inner city where street parking is scarce and expensive.
  • Community space: Communal area for all building residents that can be used for work or relaxation. This is a binary yes or no attribute. A space to study or socialize can be of extreme value to those living alone and wanting a separate space to work or relax.
  • Utilities included in the rent: Building services provided within the monthly rental payment with no additional charge. This is a binary yes or no attribute where yes means “All included” and encompasses sewage, trash, electricity, gas, and water. Some may be willing to pay higher monthly rents if they don’t need to pay additional utility bills.

The above product attributes will not only determine which specific decision variables most impact the marketability of studio apartments, but also help to reduce the extent of housing insecurity among GW students.

For each conjoint question, three options are presented to the respondent. In total, eight conjoint questions are posed to each respondent. Each option displays an example floor plan as seen in the introduction section above. This is supposed to help the respondent visualize the apartment and make it more relatable. An example conjoint question is pictured below.

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Example conjoint question copied from survey demo.

After all the conjoint questions have been answered, other demographic questions that could potentially affect an individual’s housing choices are posed. These questions include the following: Do you have any pets?, Do you own a car or other personal vehicle?, Are you required by a school program to reside on campus?, and What is your annual household income (from all sources) before taxes and other deductions from pay?. Pets could encourage someone to go for a larger apartment while whether or not someone owns a car could potentially affect one’s choice of parking availability, location, and proximity to a metro station. In addition, annual salary could have a direct effect on preferred monthly rent.

A copy of the survey is pasted in the appendix section at the end of this report

Data Analysis

Description

The team collected a total of 209 responses. This number was reduced by a variety of factors that will be mentioned in the data cleaning section. The survey consisted of 2 attention questions and 8 other questions that will be used for analysis. After all data cleaning was completed, demographic information was investigated. As seen in table 1, most participants were undergraduate upperclassmen students.

Table 1- Respondents School Year
School Year n
Gradute Student 35
Lower Classman 29
Upper Classman 43

A question was formulated to ask participants if they had previous experience renting an apartment. For our final sample, most of the participants had previous experience with apartment shopping as seen in Table 2.

Table 2- Apartment Renting Experience
Experience Renting an Apartment n
No 31
Yes 76

As students have several options for their living situation, a question was developed to ask participants about their living situation. As seen in Table 3, most participants live off-campus.

Table 3- Housing Situation
Housing Situtaion n
At Home 16
Off-Campus 73
On-Campus 18

As part of the survey, the participants were asked to answer a question regarding previous or current apartment shopping experience.

Data Cleaning

The raw sample of the new final survey consisted of 209 respondents. The data cleaning portion was essential for finding the desired sample group. Based on the setup, we eliminated any entries that did not have a session ID. The next step joined the three parts of our survey into one file by session ID. After this step the respondents were required to meet the following criteria 18 or Older, GW student, and understand consent. Along with this, the survey included questions that asked if the respondent was a robot. In the case that students speed through the survey and click yes to the robot question, they are filtered out.

After the cleaning was completed, the sample size was reduced to 180. However, it appeared that some respondents did not complete the survey completely thus these individuals were removed. Two practice questions were used as attention testers. Each question had three choices with an obvious right choice. All the attributes were the same except for one. In the first question, with all attributes constant, the respondents should have chosen the cheapest option.

In the second question, the only changing attribute was the size of the room. The respondents should have chosen the largest room. Due to this, the sample size was reduced to 109. This data was converted into long format which allowed survey data to be joined with the choice data. Thus, this translates the choice data in order to account for which alternatives were chosen throughout all the collected surveys. After the cleaning phase was completed, a .csv file was created and saved. This file will be used for future modeling and analysis in the next sections.

Modeling

\[ {u}_j = \beta_1 x_j^{\mathrm{monthlyrent}} + \beta_2 x_j^{\mathrm{location}} + \beta_3 x_j^{\mathrm{distanceToMetro}} + \beta_4 x_j^{\mathrm{squareFootage}} + \beta_5 \delta_j^{\mathrm{fitnessCenterYes}} + \beta_6 \delta_j^{\mathrm{fitnessCenterNo}} + \beta_7 \delta_j^{\mathrm{twentyFourSevenConciergeYes}} + \beta_8 \delta_j^{\mathrm{twentyFourSevenConciergeNo}} + \beta_9 \delta_j^{\mathrm{parkingYes}} + \beta_{10} \delta_j^{\mathrm{parkingNo}} + \beta_{11} \delta_j^{\mathrm{utilitiesIncludedInRentYes}} + \beta_{12} \delta_j^{\mathrm{utilitiesIncludedInRentNo}} + \beta_{13} \delta_j^{\mathrm{communitySpaceYes}} + \beta_{14} \delta_j^{\mathrm{communitySpaceNo}} + \varepsilon_j \]

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Results

WTP Logit Model

Below are the results from the original logit willingness to pay (WTP) model, which includes a summary table representing mean willingness to pay in dollars for each attribute, the upper and lower bounds of the mean willingness to pay of each attribute as determined by its 95% confidence intervals, and the set of attribute willingness to pay plots. For conceptual understanding, continuous attribute mean WTP values (price, location, distance to metro, square footage, fitness center, 24/7 concierge, parking, community space and utilities included), may be interpreted as the average dollar increase in consumer willingness to pay per unit increase in attribute value; discrete attribute mean WTP values for may be interpreted as the average consumer’s willingness to pay in dollars for having a closer commute or the apartment size, and the average consumer’s willingness to pay in dollars for having a cheaper apartment for a longer distance of a walk or being closer to campus with a smaller apartment size. Using this method to measure what the consumer values the most out of all the attributes whether it is the apartment size, the utilities that come with the apartment or the commute.

mean lower upper par label
location 103.4055166 -1988.50883 1968.64755 location Location
distance_to_metro -21.6358460 -779.28930 847.82454 distance_to_metro Distance to Metro
square_footage -0.7833694 -35.52266 33.23492 square_footage Square Footage
fitness_center_yes 256.0477455 -16990.20277 17931.82391 fitness_center_yes Fitness Center
twenty_four_seven_concierge_yes 429.8706537 -5397.70755 5070.56357 twenty_four_seven_concierge_yes Twenty Four Seven Concierge
parking_yes 182.0917957 -5727.30648 5328.23085 parking_yes Parking
utilities_included_in_rent_yes -118.9028574 -4823.28294 5088.79381 utilities_included_in_rent_yes Utilities included in Rent
community_space_yes 424.5951996 -7674.96889 7840.68599 community_space_yes Community Space

The first willingness pay plot shows that there is a significant willingness to pay for location. The average willingness to pay per unit increase in location is 214 per mile. Therefore, this represents how much more people are willing to pay for 1 mile radius compared to a 3 or 5 miles aways from campus. The second plot represents a similar trend for Distance to Metro. The average to how far the metro walk is. People are willing to pay $92 more for every minute they save on walking, whether it is between a 5, 10 and 15 minute walk from campus. The third plot represents the square footage . The fourth WTP plot which is very surprising, is the amount of people willing to pay for a fitness center with a mean WTP value of $7746. Our initial assumption would have been the location from campus and apartment size. On the other hand, it was less significant compared to the fitness center results. The third plot represents the willingness to pay for the square footage of an apartment; people are more willing to pay more for an apartment that is smaller than having to walk more. Following that the following plots represent the 24hr. Concierge as well as the parking. The following four graphs, 24hr concierge, parking, community space and utilities have a similar WTP, not as significant as the fitness center. This does make sense because GW is a city school and most students would rather not have to park their cars as well as have to work about having a car in the city. Community space as well, since gw offers many spaces to study and work at, therefore it is not a necessity to have at a building. After looking at all the WTP of every attribute that is shown in the surveys, the fitness center plays the most role in deciding what kind of apartment building they are willing to stay in. This could be an indication of the current fitness center GW has. It is not in the best condition to be used by students therefore, they are willing to pay more for their own gym in the apartment building.

Simulating Market Share for Design Groups

Having analyzed the willingness to pay for each attribute considered in the design of apartments, we aimed to simulate the perfect apartment that would be found in DC and available for students by using the logit utility model and the survey data. To create the following, we used hypothesized product attributes to the real market values of our competitor’s attributes. Specifically apartment buildings in the areas as 1500 Arlington, The Avenue and The York and Potomac Park. We simulated three different choices, as alt 1, 2, and 3.

In the figure above, the WTP of the competitors mentioned above is shown. The willingness to pay for the Ave is higher than for 1500 Arlington and The York and Potomac Park respectively. All of the above competitors have a personal gym, therefore it is hard to compare with the most significant variable that has affected students. The Ave also is the most applicable because it has a 24hr concierge as well the closest apartment building to campus. Location is the second most most important attribute to students who have taken this survey. Therefore, the Ave makes sense since it’s less than a mile away, while 1500 Arlington and The York and Potomac Park are further.

R-Squared Sensitivity Analysis

After iteratively running eight market share simulations to test the relationship between the levels of product attributes and market share, we used the combination of attributes from the last iterative simulation and further investigated the sensitivity of market share relative to changes in apartment rent and the resulting sensitivity of location in market size for around 200 people. Representative sensitivity plots with 95% confidence lower and upper bounds of the mean market share and revenue are respectively shown below. It is shown from the plots that R2 is the coefficient of determination. The R2 value tells us how much of the variance in the model is explained by variance in rent. The discount rate decreases from 0 to 0.0003 over the rent price.

Multi-Attribute Sensitivity Analysis

Having analyzed the sensitivity of our apartment’s market share relative to changes in prices and location, we conducted a multi-attribute sensitivity analysis by compiling the sensitivity of product market share to changes in each attribute in the tornado plot below. Using the “ideal” attribute combination as a basis and previously analyzed ranges of attribute values, the tornado plot shows from top to bottom the attributes to which the market share is more sensitive. Which attributes drive consumer choice the most to the least sensitive, i.e, which attribute consumer choice the most to least in finding a perfect apartment. As shown in the tornado plot below, fitness center is the most dominating attribute for capturing market share. However, perhaps the most significant finding of this report is that this tornado plot demonstrates the decision whether or not to have apartments further from campus, or closer to the metro or the size of it. Therefore, the initial thought of comparing distance and price is nullified, wherein our results indicate that having a fitness center at a competitive-to-low price are the design decisions on which we should focus for developing an apartment building that dominated the apartment market.

Final Recommendations and Conclusions

At the beginning of the project, we hypothesized that there is a competitive market advantage for having to live near campus instead of further away. However, as outlined in our compilation of results and culminated in the multi-attribute sensitivity analysis, the decision of where the apartment should be located and what amenities would the building come with is shown. The most important attribute that has affected all students is whether it has a fitness center or not. The next closest factor is finding out the size of the apartment and then the location. This information is very important to be given to GW because knowing that students are trying to find better fitness centers shows that the one on campus is not as helpful for the students. This would better the extr-cirriculars are GW as well. This could also be a factor, because the fitness center was closed for a period of time during Covid so this has limited a list of students to go workout. As a result, students are finding the most effective way to have a fitness center in the building. Another attribute this crucial for all students is the size of the apartment which is understandable after living in dorms the first 2-3 years of college. Another big competitor is GW housing, they have provided the space as well as the location but they do not have fitness centers in the dorm. This is a great factor and an advantage to the ‘ideal’ apartment building. Our result, culminating multi-attribute sensitivity analysis, demonstrates that location, price and the availability of having a fitness center are dominating attributes that drive consumer choice the most and determine the apartment building market share. Thus, our main decision is to have an apartment that is about 1 mile away, about 700 sq ft, contains a fitness center and does not necessarily need a 24/hr concierge nor parking. The following apartment is the most realistic apartment building in order to meet the rent requirement. The closer the apartment is to campus the more expensive it is. Therefore, by taking away from having a garage as well as having a concierge significantly decreases the rent of the apartment. This recommendation is very robust, as it is consistent with the high mean willingness to pay for all attributes listed above. If our company can maintain a competitive low price while delivering a high end apartment building that meets all of GW students’ needs, this would meet the project market share and revenue estimates and they are only set to increase.

Therefore, lowering the price while ensuring a high end apartment is an inherent opportunity for increase in demand. However another opportunity for increasing demand is to additionally invest in another apartment building that is a 5 minute walk from the metro station. This is an alternative to living away from campus. This is also seen through the alternative recommendations throughout the report. This would lower the cost of a real estate development for buying a piece of land and building on it. As the land that is closer to GW is much more expensive. For example, finding an apartment in Arlington is much cheaper since it’s considered to be in Virginia but still only one stop away from GW. Also the total commute could be less than someone living in DC. It is still the attributes that drive the consumer choice in finding a perfect apartment building with the right amenities for a marginal increase in market share.

Limitations

The following is a list of information sets that would be most useful to collect next in order to address the analysis’s limitations and aid in decision-making.

  • Option for Roommate: Many students may not be able to afford a room by themselves. It could be essential to include the option of roommate to the existing survey, which lacks this choice.
  • Parent Income: Students with a greater parental income might be more inclined to spend more on housing since their parents may assist with payments. Our present poll does not account for this factor.
  • Older students may be willing to pay more for an apartment and may want a larger residence. Our present survey does not account for age.
  • Student part time job: Students with part-time employment may be able to afford more costly alternatives.

The following is a list and description of the most significant unknown variables that could influence our findings:

  • Off-Campus Rooms Availability: Without knowing how many rooms are accessible off-campus and without including that information into our research, we may obtain inaccurate results. For instance, a rise in off-campus housing choices may account for a sudden fall in demand.
  • Ongoing Temporary Programs: Including details on the ongoing program might have a significant effect on our findings. Many schools provide seasonal activities that might cause demand spikes or declines.
  • Pandemics: It is a significant risk for providers of services, particularly housing. COVID-19 affected, for instance, several university housing units. Similarly, influenza outbreaks might be an unknown element that influences our findings.

Works Cited

Appendix

Conjoint Survey Part 1 - Consent and screening

Welcome to our survey!

Hello! Thank you for your interest in our survey. Your feedback is important to provide insight into student housing at and around The George Washington University (GWU). Your opinions are important to us. Please know that all data gathered from those surveyed will remain strictly confidential. The following questions are targeted solely towards GW students seeking the ideal studio or one bedroom apartment for themselves.

General Background Questions

Are you a GWU student?

  • Yes
  • No

If so, what year are you in?

  • Undergraduate, lowerclassmen
  • Undergraduate, upperclassmen
  • Graduate
  • I am not a GWU student

Have you ever rented an apartment in the past?

  • Yes
  • No

What type of housing do you currently live in?

  • On-campus housing (dormitories owned by GWU)
  • Off-campus housing (apartments or townhouses not owned by GWU)
  • At home with parents
  • I am not a GWU student
Conjoint Survey Part 2 - Choice questions

Great work!

Now that you’ve shared a bit about yourself, we’d like you to consider a scenario in which you can choose a studio or one bedroom apartment from a selection of housing options with different attributes.

Let us learn about these attributes:

Education information

Monthly Rent: Flat rent rate. This does not include utilities or access to amenities unless otherwise stated.

Location:

  • Less than 1 mile from the Foggy Bottom campus - Includes neighborhoods such as Foggy Bottom and Dupont Circle.
  • About 3 miles from the Foggy Bottom campus - Includes neighborhoods such as Arlington, Adams Morgan, and Georgetown.
  • More than 5 miles from the Foggy Bottom campus - Includes neighborhoods such as Ballston, Columbia Heights, and the Navy Yard.

Distance to Metro (Minutes Walking): How long it takes to get from your apartment to the nearest metro station based on average pace of a person and speed of a vehicle.

Square Footage:

  • Less than 500ft^2. About 3 times larger than the average parking spot. An example floor plan is as follows:
    >
  • About 600ft^2. About the size of a 3-car garage where you can park 3 cars and still walk about them easily. An example floor plan is as follows:
  • More than 700ft^2. About ¼ the size of a regulation basketball court. An example floor plan is as follows:

Amenities:

  • Fitness Room: Gym area for building residents. Rooftop: Rooftop area accessible to building residents for rest and relaxation.
  • 24/7 Concierge: Building has a front desk manned at all times of day.
  • Parking: Parking accessible to building residents and is not included in the rent.
  • Community Room: Communal area for all building residents that can be used for work or relaxation.

Utilities Included in Rent: Services provided with rent payment and no additional charge. “All included” encompasses sewage, trash, electricity, gas, and water.

Example Questions

We’ll now begin the choice questions. On the next few pages we will show you three options of apples and we’ll ask you to choose the one you most prefer.

For examples please see the following two practice questions.

Example Question 1)

If these were your only apartment housing options, which would you choose?

Option 1

Monthly Rent: 2000 ($) Location: 1 Miles from Campus Distance To Metro: 10 Minutes Walking Square Footage: 500 Ft^2 Fitness Center: Yes 24/7 Concierge: Yes Parking: Yes Community Space: `Yes Utilities Included In Rent: Yes

Option 2

Monthly Rent: 2000 ($) Location: 3 Miles from Campus Distance To Metro: 10 Minutes Walking Square Footage: 600 Ft^2 Fitness Center: Yes 24/7 Concierge: Yes Parking: Yes Community Space: `Yes Utilities Included In Rent: Yes

Option 3

Monthly Rent: 2000 ($) Location: 5 Miles from Campus Distance To Metro: 10 Minutes Walking Square Footage: 700 Ft^2 Fitness Center: Yes 24/7 Concierge: Yes Parking: Yes Community Space: `Yes Utilities Included In Rent: Yes

Example Question 2)

If these were your only apartment housing options, which would you choose?

Option 1

Monthly Rent: 1500 ($) Location: 5 Miles from Campus Distance To Metro: 10 Minutes Walking Square Footage: 600 Ft^2 Fitness Center: Yes 24/7 Concierge: Yes Parking: Yes Community Space: `Yes Utilities Included In Rent: Yes

Option 2

Monthly Rent: 2000 ($) Location: 3 Miles from Campus Distance To Metro: 10 Minutes Walking Square Footage: 600 Ft^2 Fitness Center: Yes 24/7 Concierge: Yes Parking: Yes Community Space: `Yes Utilities Included In Rent: Yes

Option 3

Monthly Rent: 2500 ($) Location: 1 Miles from Campus Distance To Metro: 10 Minutes Walking Square Footage: 600 Ft^2 Fitness Center: Yes 24/7 Concierge: Yes Parking: Yes Community Space: `Yes Utilities Included In Rent: Yes

Great work!

We will now show you 8 sets of choice questions starting on the next page. only 1 of 8 is shown here as an exemplar

[mc_button type question with the following three options]

(1 of 8) If these were your only options, which would you choose?

Option 1

Monthly Rent: 2500 ($) Location: 3 Miles from Campus Distance To Metro: 5 Minutes Walking Square Footage: 600 Ft^2 Fitness Center: Yes 24/7 Concierge: Yes Parking: No Community Space: `Yes Utilities Included In Rent: No

Option 2

Monthly Rent: 2500 ($) Location: 1 Miles from Campus Distance To Metro: 10 Minutes Walking Square Footage: 600 Ft^2 Fitness Center: Yes 24/7 Concierge: Yes Parking: Yes Community Space: `No Utilities Included In Rent: Yes

Option 3

Monthly Rent: 2000 ($) Location: 5 Miles from Campus Distance To Metro: 5 Minutes Walking Square Footage: 700 Ft^2 Fitness Center: No 24/7 Concierge: Yes Parking: Yes Community Space: `No Utilities Included In Rent: No

Conjoint Survey Part 3 - Demographic questions

Personal Preference

Thank you for your feedback! The next section will ask you two questions about your housing preferences in specific. Please select your first priority when looking for housing. - Price - Amenities - Location - Size

Please select your second priority when looking for housing. - Price - Amenities - Location - Size —–

Nice job!

We’re almost done! We’d just like to ask just a few more questions about you which we will only use for analyzing our survey data.

  1. Do you have any pets?
  • Yes
  • No
  1. Do you own a car or other personal vehicle?
  • Yes
  • No
  1. Are you required by a school program to reside on campus?
  • Yes
  • No
  1. What is your annual household income (from all sources) before taxes and other deductions from pay?
  • Less than $10,000
  • $10,000 - $20,000
  • $20,000 - $40,000
  • $40,000 - $60,000
  • $60,000 - $80,000
  • $80,000 - $100,000
  • $100,000 or more
  • Prefer not to say

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The End

Please let us know if you have any other thoughts or feedback on this survey.

Your feedback will help us make future improvements :)

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Your completion code is: CXT8KLGX