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PROPOSING A MEASURE TO EVALUATE THE IMPACT OF THE SHARING ECONOMY: A CRITICAL ANALYSIS OF SHORT-TERM RESIDENTIAL

RENTALS

A Thesis Presented

By

Bruno Semensato Rosa

to

The Department of Mechanical & Industrial Engineering

in partial fulfillment of the requirements for the degree of

Master of Science

in the field of

Engineering Management

Northeastern University Boston Massachusetts

June 2016

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TABLE OF CONTENTS

1 ABSTRACT ..................................................................................... vi

2 INTRODUCTION ............................................................................ 8

2.1 Overview .................................................................................................... 8

2.2 Problem Statement ................................................................................ 11

2.3 Research Questions ............................................................................... 13

2.4 Research Objectives .............................................................................. 13

2.5 Thesis Organization .............................................................................. 14

3 LITERATURE REVIEW AND BACKGROUND ............................... 15

3.1 Value Creation ........................................................................................ 15

3.2 Inventory .................................................................................................. 17

3.3 Meeting Supply and Demand ............................................................. 19

3.4 Companies Adapting a Business Model to the Sharing

Economy.................................................................................................... 21

3.5 Problems of the Sharing Economy and STRR Regulation ...... 23

4 ISSUES OF CONCERN: ENGINEERING MANAGEMENT

CONCEPTS .................................................................................... 25

4.1 Balance of Supply and Demand ....................................................... 26

4.2 Flexible Work Force ............................................................................. 29

4.3 Mass Customization: The Uniqueness of the Sharing Economy

..................................................................................................................... 32

4.4 Environmental Concern and Social Aspects ................................ 33

4.5 Quality Control ...................................................................................... 36

4.6 Economic Distribution ........................................................................ 39

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5 MACRO CONCEPTUAL FRAMEWORK OF THE SHARING

ECONOMY .................................................................................... 40

6 TEST BEST BED: APPLYING THE SE QUANTITATIVE MODEL TO

THE STRR ..................................................................................... 44

6.1 Stakeholder Threats and Opportunities Diagram ..................... 45

6.2 Test Bed Question and Objective ..................................................... 46

6.3 Steps of the Method .............................................................................. 47

6.3.1 Company Selection.............................................................................. 47

6.3.2 Cities Selection .................................................................................... 49

6.3.3 Variables Analyzed per City................................................................ 50

6.3.4 Validation of Variables and Further Considerations........................ 54

6.3.5 Data Collection and Processing.......................................................... 55

6.3.6 Analysis ................................................................................................ 57

7 DATA ANALYSIS AND RESULTS OF THE TEST BED ................... 58

7.1 ISEP: Index of Sharing Economy Principles................................ 58

7.2 Sensitivity Analysis ............................................................................... 62

7.3 Demonstration of Calculations for Boston .................................. 63

7.4 Considerations ....................................................................................... 68

7.5 Recommendations for Cities to Improve ISEP ........................... 69

7.6 “Number of Penalties” Criteria ......................................................... 71

7.7 Analysis of ISEP methods................................................................... 72

8 CONCLUSIONS ............................................................................. 74

9 REFERENCES ................................................................................ 77

10 APPENDIXES ............................................................................... 84

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LIST OF TABLES

Table 1 - Cities and Raw Data .................................................................................. 56

Table 2 - Auxiliary Variable .................................................................................... 57

Table 3 - Normalized Data ....................................................................................... 58

Table 4 - Normalized Data per City and ISEP ....................................................... 60

Table 5 - Sensitivity Analysis of Threshold Factor ................................................ 63

Table 6 - ISEP Boston Calculation Step 1 ................................................................ 64

Table 7 - ISEP Boston Calculation Step 2 ................................................................ 64

Table 8 - ISEP Boston Calculation Step 3 ............................................................... 64

Table 9 - ISEP Boston Calculation Step 4 ............................................................... 65

Table 10 - Boston Top 20 Hosts................................................................................ 66

Table 11 - ISEP Boston Calculation Step 5 .............................................................. 66

Table 12 - ISEP Boston Calculation Step 6 .............................................................. 67

Table 13 - ISEP Boston Calculation Step 7 .............................................................. 67

Table 14 - Nashville Proposition Step 1 ................................................................... 70

Table 15 - Nashville Proposition Step 2 ................................................................... 70

Table 16 - Nashville Proposition Raw Data Step 3 ................................................ 70

Table 17 - Number of Penalties per City .................................................................. 72

Table 18 - Analysis of Isep Methods ........................................................................ 73

Table A1 - Number of Apartments X Population.................................................... 84

Table B1 - Top 20 Hosts ............................................................................................ 85

Table C1 - San Diego Example……………………………………………………………..........86

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LIST OF FIGURES

Figure 1 - Source: The Future of Finance Bart de Waele (2015) ............................. 9

Figure 2 - Media and the Problem ............................................................................ 12

Figure 3 - Exemplifying the Level of “Sharing Economy” .......................................18

Figure 4 - Ride Sharing Extra Fee and Driver’s Rating .......................................... 27

Figure 5 - Macro Conceptual Framework of the Sharing Economy ...................... 42

Figure 6 - Stakeholders Conflict Diagram ............................................................... 46

Figure 7 -Nights Booked in Airbnb .......................................................................... 48

Figure 8 - Airbnb Listings Growth ........................................................................... 49

Figure 9 - Cities Selected .......................................................................................... 50

Figure 10 - Normalized Variables Per City ............................................................... 61

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1 ABSTRACT

The following thesis contributes to the analysis of the Sharing Economy

through an application of Engineering Management concepts. The sharing

economy, also known as “collaborative consumption,” “trust-economy” or “peer-

to-peer economy” is based on the idea that individuals borrow, use and/or rent

assets from each other (such as: physical products, spaces, and skills). The

Sharing Economy is based on the existence of high value assets that are under-

utilized. Technological digital platforms intermediate the process of sharing and

bring safety and effectiveness to the operations. An overview of the sharing

economy shows it has begun to change society and is leading to new business

models. This thesis makes three main contributions: a) development of issues of

concern based on engineering management concepts that characterize the SE, b)

the development of a macro conceptual framework outlining its foundations,

main characteristics, principles and overall benefits, c) the development of an

index to measure the impact of the misuse and abuse of SE platforms from the

perspective of the principles defined in the framework. A test bed approach is

used to validate the proposed model of Short-Term Residential Rentals (also

known as house-sharing) and identifies threats and opportunities of the SE to

their main stakeholders: hotels, long term residents, real estate brokers,

landlords, short-term renters. The conclusions of this thesis demonstrates how

engineering management concepts such as mass production, balance of supply

and demand, quality control and measuring techniques of complex systems assist

to define the Sharing Economy. This work also analyzes strengths, drawbacks

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and challenges of the Sharing Economy by evaluating the viability of ISEP (index

of sharing economy principles) as a parameter and a tool for governments,

communities and stakeholders. The thesis proposes that ISEP can be used to

assist the regulation of the SE and reduce the impact of the possible economic

and social problems.

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2 INTRODUCTION

2.1 Overview

Although most of the successful sharing economy (SE) companies were

created around 2010, it was around 2014 that they started gaining a significant

market share and impacting society. According to Stein (2015), there are at least

10,000 companies in the sharing economy. Airbnb, a house-sharing company,

was one of the major pioneers, and the ride-sharing company Uber is valued at $

41.2 billion, which makes it one of the 150 biggest companies in the world (bigger

than Delta or FedEx). The sharing economy allows people to run their own taxi

services, car rentals, hotels, restaurants and, as it will be argued in this thesis,

brings many advantages to its players.

Rachel Botsman was one of the first academics to study the

phenomenon of collaborative consumption and she points out that the “new

technologies enable us to unlock the "idling capacity" of resources—the

untapped social, economic, and environmental value of underutilized assets.”

She affirms that “idling capacity is everywhere: empty seats in cars; unused

holiday homes or spare bedrooms; underutilized Wi-Fi; unoccupied office

spaces; latent skills and capital; and of course underused consumer goods.”

Exchange of the right information at the right time is the key to make the match

between the “providers” with the “wanters” and this is one of the main idea that

the SE relies on.

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Technology is improving and creating a variety of services and a variety

of new business models as seen in figure 1. New technologies such as

networking Services, big data, mobile devices, self-fed data system and effective

microtransaction payment systems and an online reputation score system

creating trust among strangers. This, and other elements, have enabled the

right environment for the nurture of the SE. Some practical examples are

people sharing their apartments when going on vacations, sharing their cars

when parking in an airport, or sharing their passenger seats when driving

throughout the city. Musicians rent music gear directly from other musicians

through websites such as GearLoad, and high-end household items such as

photo cameras are rented on a peer-to-peer basis. Others important service

industries have also been impacted by the sharing economy. Some platforms

such as Eatwith and Feastly offer social dinning services so people can share

dinner experiences with others.

Figure 1 - Source: the future of finance Bart de Waele (2015)

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The Economist (2013) compares the sharing economy to what happened

to online shopping in the USA 15 years ago. “At first, people were worried about

security. But having made a successful purchase from, say, Amazon, they felt safe

buying elsewhere”. Strangers have never been able to connect in such a quick

and trustful way. Fedrizzi (2015) defends that technology and sharing economy

are bringing people back to the pre-industrial times when relationships and social

capital were more valuable than financial capital. People not only use these

services to make or save money, but also to establish connections and

relationships. For example, some shared economy services such as Couch

Surfing do not allow people to charge their visitors. The logo of the company

describes its goal concisely enough: “Stay with locals and make travel friends.”

Tanz (2014) claims that an actual Internet revolution is taking place. The

traditional Internet helped strangers meet and communicate online; however, the

modern Internet can link individuals and communities in the physical world and

is finally allowing Americans to trust each other.

Peer-to-peer commerce is not something new in its essence (Marshall,

2015), but the Internet has changed the nature of this kind of commerce and has

provided the tools for it to work on a large scale. History shows us that

commerce between human beings in ancient times happened only between

friends, friends of friends, or neighbors. Many years later, commerce was

extended between strangers through trusted intermediates. Subsequently, many

people moved to urban areas where commerce started taking place between

people and companies with the aid of market protection systems such as banks,

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insurance companies, associations, and buying policies etc. More recently, Ebay

was one of the leaders to intermediate business through an online platform and

was one of the creators of the bidirectional posttransaction review system among

their customers (Zervas, Proserpio & Byers, 2013). Since the sharing economy is

still a new topic in academia with little coverage, this thesis will first outline

engineering management concepts to assist the analysis of the Sharing Economy.

2.2 Problem Statement

There is some discussion in literature whether or not some companies

should be classified as part of the Sharing Economy and what the main

singularities of this new kind of economy should be. Since it is a recent topic,

there is a gap of knowledge in certain areas. Therefore, one of the problems that

will be explored in this thesis is the characterization of the Sharing Economy, its

principles, and how it is distinguished from other kinds of economies. This will

be done through the exploration of issues of concern and through links with

supply chain and engineering management concepts.

One of the motivations of this thesis is the problems of abuse and misuse

of the short-term residential rentals (STRR). Certain hosts in some cities started

to violate the good principles of the SE causing problems for other stakeholders.

Hosts who occasionally rent their homes, or a spare room, while traveling, are

being replaced by “professional hosts” and real estate brokers. The STRR was

chosen because along with ride-sharing it is one of the biggest industries of the

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Sharing Economy. Furthermore, as it will be discussed later, the media has

recently started to talk about problems that these platforms have caused in some

cities. However, since there are many stakeholders affected by it, and many

variables that play a role in it, this industry lacks further studies, analysis and

academic expertise.

The rapid spread and exponential growth of short-term residential rental

platforms is not only an unfair competition to hotels, (because hosts and

platforms of the STRR do not pay high taxes as hotels) but has also started to

cause other problems to different stakeholders. According to some researchers

such as Malhotra and Van Alstyne (2014), “short-term rentals create shortages of

affordable long-term housing when nightly rates exceed monthly rentals.”

Therefore, especially in some cities such as San Francisco and NYC which have

been facing consecutive rent increases in the last decades, long term residents

have less negotiation power because the landlords and third party corporations

realized they make more money renting their units on a short-term basis.

Figure 2 - Media and the Problem

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Several newspapers worldwide have investigated this particular problem,

such as Coldwell (2015) in Figure 2, which affirms Airbnb, which used to be a

“cool” home sharing platform, has now turned into a commercial giant and how

high-profit landlords and third-party management companies are undermining

its founding principles.

2.3 Research Questions

Engineering management is an interdisciplinary field of engineering that

aims to design and manage complex systems. The SE is a new field that has little

literature and few references, although it has important economic and social

impact around the world, as shown in the overview section above. This scenario

leads to the research question below:

“How can engineering management concepts such as balance

of supply and demand, quality control, mass production, and

measuring techniques of complex systems assist in the analyses of

the Sharing Economy challenges?”

2.4 Research Objectives

General objective:

To study the viability of applying engineering management concepts to

analyze and measure the social and economic impact of the Sharing Economy.

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Specific objectives:

a) Develop a macro conceptual framework outlining foundations,

principles, results and benefits of the Sharing Economy.

b) Design a stakeholder conflict diagram to show the impact (threats

and opportunities) of the SE over hotels, long-term residents,

landlords, real estate brokers, short-term renters and STRR

platforms.

c) Construct a set of variables that measure specific aspects of the

impacts of SE, and apply it in a test bed of short-term residential

rental platforms in a sample of North American cities.

d) Design a single index, ISEP - Index of Sharing Economy Principles-

to indicate evidence of misuse and abuse practices in order to

maintain a better balance of the stakeholders affected by STRR

platforms.

e) Evaluate the viability of the propositions in this thesis as a tool for

governments, communities and stakeholders to regulate the SE,

reducing the impact of eventual economic and social problems.

2.5 Thesis Organization

Chapter 2 starts with the literature review and background by

characterizing the Sharing Economy, its principles and some of its singularities.

Chapter 3 explores issues of concern from the engineering management field

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showing how these problems contribute to assist the SE analysis, using concepts

such as Balance of Supply and Demand, Flexible work force, Mass Customization,

Quality Control, and other concepts. Chapter 4 develops a macro conceptual

framework outlining the foundations, principles, and benefits of the Sharing

Economy. Chapter 5 presents the stakeholders diagram of threats and

opportunities; it develops a quantitative method (ISEP - Index of Sharing

Economy Principles) using statistics tools and applies it to a test bed on short-

term residential rentals. Chapter 6 performs data analysis and results. Finally,

chapter 7 explains the conclusion in regards to the evaluation of the impact of the

sharing economy and the contributions of the engineering management concepts

to this new business model.

3 LITERATURE REVIEW AND BACKGROUND

3.1 Value Creation

In 2008, for the first time in history, most people in the world began to

live in cities (United Nations, 2008, as cited in Rosa, 2016). As the cities grow,

they take up more than ever a central place in the world, with greater economic,

political and technological power. Among numerous problems, urban mobility is

one of the most important. The fact that an increasing number of people live in

cities makes it easier to share goods and experiences. According to Logan Green

(as cited in Stein, 2015), his main goal of creating Lyft was to fill millions of

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unused car seats to save the environment and decrease traffic problems.

Furthermore, he claims that isolation is the worst form of punishment; therefore,

technology needs to be used to connect people in a safe and efficient way.

Sharing economies enables existing infrastructure to be used more efficiently.

There is also the environmental perspective in which we need and have the

capability to improve our use of finite resources (Botsman, 2013), especially in a

society in which a car can sit unused for twenty-three hours a day, on average

(Gansky, 2010).

The sharing economy contributes efficiency by optimizing the use of

assets. In the same way, Google Maps warns about traffic jams and provides

alternative routes, contributing to a better traffic balance on the roads. Airbnb

uses spare rooms that were previously empty, so the housing stock is used more

intensively. People are comfortable doing business directly with companies and

mostly using companies’ resources. However, people started to realize that new

peer-to-peer business models based on the use of mobile Internet are allowing

individuals to be connected directly to other users in a safe efficient way.

On the supply side, individuals can benefit from the sharing economy by

renting their under-utilized inventory, which would otherwise be sitting idle and

on the demand side, “consumers benefit by renting goods at lower cost or with

lower transactional overhead than buying or renting through a traditional

provider” (Zervas et al., 2014). The fact that in our advanced society many people

have a “powerful computer device connected to the world” inside their pockets

makes some traditional jobs and functions no longer necessary, avoiding these

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overhead costs. Activities and functions that do not aggregate value in the

customer’s perspective are more easily removed. As an example, many people

were willing to dispense with services provided by a hotel receptionists and room

cleaning teams in order to get a lower daily price. This, added to the more

efficient system, could be one of the reasons why Airbnb is usually able to offer

cheaper accommodations than hotels. Among other reasons, the inefficiency of a

market or a specific economy creates a vacuum for the rise and success of the

sharing economy.

3.2 Inventory

In Industrial Engineering, lean concepts are widely used, and inventory is

one of the areas that draws the most attention. Excess of inventory is considered

a waste and many techniques have been developed in attempt to decrease its

levels (Shah & Ward, 2007). In accordance with this concept, the sharing

economy can be divided in two levels. The first one consists of companies who

own inventory of certain goods and make them available to a range of customers

through creative use of technology, such as ZipCar and the bike share systems

available in many cities. According to Sundararajan (2013), this business model

is not very different from traditional ones because these companies need to

acquire, manage and monetize their inventory (which for Zipcar is around 10,000

vehicles). The second level, in which companies such as Uber, GetAround and

Airbnb are classified, are the ones that in fact disrupted the traditional business

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model and would be the ones that performed best in regards to lean concepts. It

is true that both layers confront the idea of ownership and that people are

shifting from that to access models, such as rentals, on-demand availability and

subscriptions (Owyang, Samuel & Grenville, 2014). Among other authors, Paul

Graham (2013) makes an even deeper suggestion that ownership was just a hack

or an inefficient way of consuming. His reasoning is that we did not have the

technology and infrastructure to share properly. With regards to inventory levels,

technology usage to manage information and provide matches, and on demand

access, a chart as seen in Figure 3 was developed measuring the levels of Sharing

Economy among companies in the transportation and accommodation

industries.

Figure 3 - Exemplifying the level of “Sharing Economy”

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The more efficient approach is also linked to a sustainable perspective.

The sharing economy is able to contribute to a higher occupation rate of

apartments, higher occupation rate of cars, and to many other goods and

products, especially the ones with high values. “Using resources efficiently

simply makes sense, and providers, clients, as well as society as a whole can

benefit” (Teubner, 2014). According to a United Nations report (2013), the world

population will grow to approximately 9.2 billion by 2050 and cities tend to

become denser, making the sharing of resources easier and even more important

in an ecological mindset (Madden, 2015).

Another company, which found an interesting gap in the market,

connects two different business that were apparently disconnected. In one side,

there are people who need long-term parking in airports and on the other,

approved traveling members who need long-term rental car after landing in an

airport. Based on a special agreement with a car insurance, FlightCar’s work is

based on this simple concept of merging two different markets (parking lot and

car rental) into one, and helps to solve a major problem of parking space in

airports. For the car owner, besides getting free parking, he has his car washed

and makes some extra money if it is rented.

3.3 Meeting Supply and Demand

For readers who are not familiar with the sharing economy, this paper

will demonstrate the operations of one of the biggest divisions of the SE which is

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the ride-sharing industry. It consists of a transportation service in which vehicles

are operated and owned by independent contractors and trips are booked over

the Internet by a third-party mobile application that allows drivers and

passengers to be matched directly. The following information used was provided

by drivers of Lyft, which is one of the main ride-sharing companies operating in

the US. Drivers selected by Lyft have the option to work in shifts or on demand.

If they work in shifts, they need to choose a shift time (consists of 3 hours) and as

long as they keep a certain response rate (around 90%) and get at least one

passenger for a certain period of time, they will be guaranteed to get paid a

certain amount of hours.

If the driver works on demand mode, he gets a percentage of the fare

charged to the passenger. Lyft decides which percentage of the fare will pass to

the driver. In that way, the company is able to adjust their percentage in order to

gain a better market share and to meet supply and demand. It is important to

note that ride-sharing companies have great interest in meeting supply and

demand because if a passenger cannot find a ride in a reasonable period of time,

he or she might change to another car sharing application or even use another

form of transportation.

Ride-sharing companies have been using a variety of marketing tools in

order to increase their customer base. They offer passengers credit for each

friend invited to the platform, which stimulates word of mouth. These companies

have also been establishing partnerships with restaurants, bars and clubs offering

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free or discounted rides for their locations, which opens a new interesting

strategy that could be extended to other industries as well.

3.4 Companies Adapting a Business Model to the Sharing Economy

What is the impact of the shared economy in traditional business models

and brands? Strong (2015), managing director of GfK UK Technology, states “The

sharing economy has cleverly made established brands left dangerously out of

touch. If they do attempt to criticize the business model, then they can appear

like dinosaurs out of step with the hip new economy”. Some traditional

companies have started to have a new kind of competitor, forcing them to

reinvent themselves, their business models and their values in order to compete

in this new business environment.

Although traditional companies in the internet era have already started

to adapt their business models taking advantage of SE principles, it is still not

deeply studied in academia. During CSCMP’s (Council of Supply Chain

Management Professionals) annual conference in 2015, one of the main lecturers

was Dave Clark (2015), Amazon’s senior vice president of worldwide operations

and customer service. Clark announced Amazon’s newest service, Amazon flex,

which consists of a delivery network based on the sharing economies principles.

The service allows people interested in a part-time job to deliver packages for a

fee, working with their own car and on their own schedule. Clark highlighted that

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the sharing economy will be one of the most important factors to help Amazon

overcome its supply chain challenges and high ambitions, such as the expansion

of the 1-hour delivery program to new cities. When there is a commitment to

deliver a variety of goods within one hour from the “purchase click” of a

customer, no wasted time can be accepted, and delivering through a local channel

from a local supplier is definitely an interesting idea.

Through Amazon’s delivery network, that is beginning to grow, they

expect to overcome crushing holiday demands that are, according to Dave, a

common issue within their industry. Customers increasingly want their

purchases at a specific time and are no longer willing to accept excuses such as

strikes and disruptions. Furthermore, in this way Amazon has more control over

an important part of their supply chain and no longer needs to depend on third

part partners. Companies are able to come up with efficient, time saving

methods to improve their supply chains. In this context, throughout sharing

economies, a new quality benchmark is being set.

This example opens a discussion in the ways companies, based on

traditional internet business models, are adapting to the Sharing Economy.

Some big companies are leveraging or acquiring small and medium sized firms to

do the work. As an example, Avis has recently purchased Zipcar, and competitor

companies such as Enterprise have created their own version called Enterprise

CarShare. According to Avis’s Chief Executive, Ron Nelson (as cited in Stokes,

Clarence, Anderson & Rinne, 2014), he was first dismissive of car sharing but

then realized it would be an important complement to their traditional business.

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Meanwhile, Trip Advisor, has recently made high investments on their “vacation

rental” business segment and is already one of the main competitors of Airbnb,

which is usually cited as the benchmark of short-term residential rentals.

3.5 Problems of the Sharing Economy and STRR Regulation

One of the reasons behind the unacceptance of sharing economy

companies by some entities is in regards to the drain of what used to be locally

distributed revenue. It is understandable that when brick and mortar business

start to operate in new territories or new countries, many investments are made

in the local market such as the acquisition of inventory. As it will be discussed,

an intrinsic characteristic of the sharing companies is to be a thin layer that sits

over vast supply chains. The SE does not need to acquire major assets, inventory,

or even install manufacturing facilities in order to operate in new territories and

countries. Therefore, besides the hiring of personnel, a small local office and

overhead costs, little investment is needed and made in the local market. The SE

takes advantage of assets that already exists, makes little local investments and

drains back to the headquarter environment part of the revenue that used to

belong to the local community. Vitali, Glattfelder and Battiston (2011) have

studied this current “rich club” phenomenon and have affirmed that a small core

of large transnational companies and finance institutions controls the majority of

mechanisms of wealth generation. Although Vitali et al. (2011) were not referring

to the SE, which was still on it first steps at that time, there is evidence that the

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SE is intensifying this phenomenon in the perspective that local markets such as

local hotels and taxi driver companies are losing market share to Airbnb and

Uber. Uber charges its drivers a commission between 20% and 30% for its

services depending on the city (Huet, 2015). It is hard to estimate how much of

this is invested back in local markets, especially because this value varies

depending on how mature the company is in the local market and its strategy,

some authors estimate it would be less than 5% of the fare. This might be one of

the reasons why many governments, especially in developing countries, are

skeptical about the Sharing Economy companies from foreigner countries. The

physical customs and traditional techniques are no longer enough for them to

manage the balance of trade. Therefore, their level of control over the economy is

decreased. This is particularly dangerous for developing countries in which local

companies might have limited leverage to compete against giant global

corporations. This specific drawback is a good suggestion to be further explored

in future papers due to its high complexity and relevance.

To what extent is the SE economy able to balance supply and demand?

Will there be significant amount of people quitting their full time and traditional

jobs in order to venture in the SE and then figure out that the market has become

saturated? In a recent public speech, Hillary Clinton, although acknowledging

that the SE is creating exciting opportunities and unleashing innovation, express

her concerns in regards to workplace protections, the vulnerability of SE workers,

and what a good job will look like in the future (Rogers, 2015). Is the SE a

dangerous way to replace secure jobs for gigs? What are the consequences of SE

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in the long run? These are important questions and concerns that needs to be

addressed in future works in order to have a better overall analysis of the SE.

Gottlieb (2013) says “the expansion of home-rental websites presents

local governments with a controversial policy debate, requiring them to more

clearly choose a direction and decide whether to ban, encourage, or limit short-

term rentals through regulation”. According to Palombo (2015), Airbnb’s

operation raises legal and regulatory questions in regard to taxes, liability, and

zoning and cities, such as San Francisco and New York, where regulation is

already a response to complaints from hospitality and tourism industries. Miller

(2014) proposes a mechanism to regulate the STRR called “transferable sharing

right” (TSR), which charges a fee for STRR in order to compensate neighbors

where short-term rentals occur.

4 ISSUES OF CONCERN: ENGINEERING MANAGEMENT CONCEPTS

As previously discussed, issues of concern will be explored throughout

this section which will lead to the creation of the framework. These issues are

based on important characteristics of the SE such as the balance of supply and

demand; flexible work force; mass customization; environmental Concern and

Social Aspects; Quality Control; Economic Distribution.

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4.1 Balance of Supply and Demand

One of the main characteristics of sharing economies services is the

ability to quickly map supply, map demand and create incentives in order to

balance both. In many industries, meeting supply and demand is one of the

main challenges. A variety of statistics tools has been developed and many

studies have been done in order to better forecast demand of a specific product or

service. However, brick and mortar businesses have this intrinsic characteristic

of having a fixed capacity. A restaurant offers room for a specific number of

tables and customers. An ice cream store has a limited capacity of workers any

given day unless forethought is made to prepare for an increase in consumers for

an expected occasion. Hotels that usually get sold out in summer vacation season

sometimes struggle to break even in low season. However, in all these examples,

high level planning and forecasting tools are required and can only provide an

estimate. In the ride-sharing business model, for example, there is an impressive

ability to attract more drivers in rush hour or on rainy days in a fast and easy

way.

Lyft, for example, has a charging methodology that depends on the miles

and minutes that the ride lasts. However, when there are a higher number of ride

requests in comparison to the number of drivers working in a specific time, the

charging rate is inflated by an algorithm in order to motivate more drivers to

work at that time. When the user, who decides to get a ride to go to work due to

the rainy weather conditions, opens the app, he gets the warning that there is an

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extra charge of 25%. If the weather gets worse for example, and more and more

people are requesting rides, this extra fee goes up 50% (see Figure 4), 100% or

even 200%. On the other side of the business, drivers who work part time for

these applications have the option to set their smartphones in a way that they

receive a message when this surge price is in place. In that way, part time drivers

have the ability to choose to drive at that moment in order to make extra money.

At that point, they are automatically contributing to balancing the system in a

quick and efficient way.

Figure 4 - Ride Sharing Extra Fee and Driver’s Rating

Many cities, especially the ones that do not have an efficient

transportation system, struggle to find out what the optimal number of taxi

licenses they should distribute or sell. Even with optimization studies and their

experience dealing with this situation, it still results in occasions when

passengers struggle to find cabs or a specific area is underserved. Other times,

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there is an excess of cabs and few passengers in need of them, which causes loss

for the drivers. Moreover, these kinds of ride-sharing applications make it easier

for transportation during holidays or events in the city in the same way that

short-term residential rental companies provide alternative lodging.

Events that attract many visitors to cities, such as the SuperBowl, the

Soccer World Cup, the Boston Marathon, have always been a challenge to

organizers, mayors and governors. The number of hotels rooms, taxis and buses

are not solely calculated for special occasions such as these major events;

therefore, the short-term residential rentals and ride-sharing companies, and

their on-demand work and flexible nature, contributes substantially to the

expanded capacity of a city when needed. These kinds of events are usually on

holidays or weekends that facilitate the work hosts and part time drivers who

perform on these busy periods.

With this in mind we can note that the sharing economy is important in

contributing to cities hosting a variety of sports, professional and cultural events,

especially for small and medium-sized cities in which facilities and transportation

systems are either insufficient for large events or sit idle the rest of the time. This

attraction of tourists and professionals is a very interesting source of revenue for

towns and cities as well as a source of potential investments and cultural

exchange. Offering alternative options for lodging and transportation during

business events also has a cascade effect. Small companies, and the ones that are

more sensitive to price fluctuations during rush seasons, first have a better

capability of joining the event or conference, and second, tend to have cheaper

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expenses. These savings might be passed down their supply chain, which clearly

benefits consumers.

This elastic supply curve is not only a phenomenon in the ride-sharing

and house-sharing business models. Since the sharing economies have the

intrinsic characteristic of not owning inventory, its available level in the market

through the platforms might be adjusted. Cullen & Farronato (2014) use internal

data of TaskRabbit to draw some interesting conclusions in this regard. Task

Rabbit is an online marketplace that allows users to outsource small jobs and

tasks to others in their neighborhood such as shopping and delivery (24%),

moving help (12%) and cleaning (9%). According to the authors of this study,

“the existence of an elastic supply curve allows the market to efficiently

accommodate variable demand and to create 15 percent higher value from

aggregate matches”. When demand is high relative to supply, sellers increase the

number of offers they submit, and as a result everyone involved benefits from this

greater match percentage.

4.2 Flexible Work Force

In the perspective of the one performing the job, a freelance culture and

on-demand work provides people the flexibility to work when they want and as

much as they want aiding on their wellbeing. It is not a fact of replacing jobs that

are based on the traditional strict schedule, it is a fact of allowing people another

alternative way of working. Botsman (2015) comments that nine-to-five jobs are

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just not an option for some people like retirees, students, people with disabilities

and the SE also helps those ones who cannot find a traditional job in a tough

market. Another example are those jobs or careers which intrinsically have no

guarantee of demand, and therefore are capable of being “complemented” by the

income generated through the SE. Certain contractors fit in this category, as well

as musicians who primarily work with gigs. This is one of the reasons that some

authors use the term on-demand economy as a synonym for the SE.

Rather than working for large corporations, people start to become

suppliers of extremely valuable skills and assets in a variety of platforms

(Sundararajan, 2015). In the same way that companies benefit outsourcing tasks

that are not in their core business, sharing economies empower and facilitate the

individual to do the same with the labor of other individuals. In the ride sharing

business model, drivers have the ability to work not only whenever they want, but

also the amount of hours they want. On the other hand, markets may get

saturated and there might not be enough work demand for all the people willing

to work at a certain time.

This flexible work idea might be very valuable in many other examples.

Suppose the employee of a traditional company has the idea of opening his own

business. In order to start in the entrepreneurial world he knows he must spend

time developing his product or service and he finds himself in the tough decision

of quitting his actual job. In the financial perspective, he is skeptical about

quitting because, among other reasons, he will have fixed costs and bills to pay in

the end of the month. The fact that he knows, in case his own company doesn’t

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start to generate income at the speed that he has expected, he still has a plan B to

make some income by participating in the Sharing Economy, might be a key

factor in his decision. Ride-sharing researches have already indicated that most

drivers do not aim to follow a career as drivers in the long run, they are more

likely to use it as a bridge while seeking another position in the labor market

(Hall & Krueger, 2015). Similar research indicates that most Airbnb hosts do not

have plans on working as professional hosts or in the hospitality industry. One

of the main advantages claimed by the SE companies is that they provide

additional ways of income generation, which provides individuals freedom to

pursue their dreams.

One interesting report made by a driver who works with different ride-

sharing companies summarizes some of the advantages: “There are a lot of things

I love about my job, but just to name a few: meeting interesting people, working

whenever I want and seeing a correlation between how hard I work and how

much money I make” (Holger, 2015). The SE is able to match these two concepts

in an efficient way. On one hand, there is the problem of balance of supply and

demand especially in major events in small and medium cities. On the other

hand, there is demand from the segments of the population who desire or require

flexibility and autonomy in their work schedule. Few traditional companies can

provide this schedule flexibility to its employees. Besides having this ability, SE

has the capacity of motivating workers or drivers in this manner, through setting

extra fees in place, such as the ones demonstrated before. In the short-term

residential rentals, the surge price is usually regulated directly by the market.

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Some researches claims that a portion of Airbnb hosts make their personal plans

based on dates that will generate good income throughout the rent of their

apartment, which tend to coincide with high season.

4.3 Mass Customization: The Uniqueness of the Sharing Economy

Some industries of the SE have the ability to scale business to a global

level with the singularity of keeping the personal touch and uniqueness as

important characteristics. This idea contrasts with traditional idea that global

brands have high levels of standardization, examples such as McDonald’s and

Best Western. While traveling, for example, some travelers might not be looking

for a hotel chain in which he knows exactly how the bedroom will look, how the

decorations will be, and style of service. In the SE, according to personal

experience and authors such as Owyang, Samuel and Grenville (2014), travelers

value authenticity and adventure. Staying in a residential neighborhood while

exploring a new country might be a completely singular and more personal

experience. Getting to know locals, the way they live, how their house looks

opens a new level of tourism, and according to Madden (2015) it builds a sense of

community among networks of users.

This is another value that is offered to the customer by companies such as

Airbnb and it is incredible how the concept of uniqueness goes along with global

level scale. This fits the concept of mass customization for services that are

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defined by Hart (1996) “using flexible processes and organizational structures to

produce varied and often individually customized products and services at the

price of standardized mass-produced alternatives”. The short-term residential

rentals business model is not only a conventional flexible system where a wide

variety is offered (Ahlstrom & Westbrook, 1999) but also provides personal

feedback after staying in someone’s house, providing the host with information

for changes. For both the specific customer who might come back, or the niche of

customer who might choose to stay in this accommodation in the future. Another

supportive argument is from Duray, Ward, Milligan and Berry (2000) who say

that one of the pre requisites of mass customization is that customers must be

involved in specifying the product/service. This is true in the sense that potential

customers of the house-sharing industry have the ability to communicate with the

accommodation owner prior to closing the deal, and then are able to negotiate for

specific items.

4.4 Environmental Concern and Social Aspects

Environmental concern is another important principle that the SE relies

on. In the ride-sharing business for example, in 2014, some companies like Uber

and Lyft started offering the option “carpooling” “in line”, which connects people

going in the same direction at the same time (Teodorović & Dell’Orco, 2008). To

explain better the dynamics of this tool, an illustration scenario will be presented.

Bob wants to go from Central Park NYC to Times Square on Friday night and

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chooses the “carpooling” mode. He requests a ride through the app and adds the

destination he wants to go. The app uses his GPS to know the exact location

where he wants to be picked up and sends this initial and final destination to

drivers who are working at that time. The first driver to accept the ride starts

moving towards Bob’s location. The app automatically calculates how long it will

take the driver to pick Bob up and shares this information with him. Meanwhile

another potential passenger requests a ride from Central Park as well and adds

the final destination, which is a certain address located on the way to Times

Square. In that way, Bob receives a message informing that John will be joining

him on the ride and costs will be split between passengers. It is important to

point out that the driver might be working on the commission mode and might

have accepted the ride because he also needed to go in that direction of the city.

To summarize, the ride-sharing business model with the “carpooling” option in

fact avoids two or more cars going in the same direction at the same time,

maximizing the use of the car and minimizing traffic jams and pollution. This

smart use of information is used in a variety of other applications within SE

companies and Big Data plays an important role since tons of data are captured

through the operations that are 100% traceable due to its tech nature.

Furthermore, there is also the social aspect advantage. Both passengers

get to know each other in a quick “speed date” style which can bring benefits to

businesses and even other kinds of relationships. The fact that the driver usually

has another job as well (61 % of drivers have full time or part-time careers outside

of Uber according to the own company) might increase the chance of creating

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interesting business contacts for the different parties of this “speed date”

meeting. They do not even need to talk about money, avoiding those awkward

moments and the potential feeling of being taken advantage of. The payment is

already figured out by the app and charged directly to the passenger’s credit card

that is associated with the account. Furthermore, a new person in the city no

longer needs to worry about being cheated by the driver who decided to take the

longer way to make more money or getting a driver who might not have a GPS

and gets lost. The application calculates the best route taking traffic into

consideration and automatically turns on the turn-by-turn guided instructions. If

anything went wrong, e.g. if the GPS did not work, both parties have the ride-

sharing company customer service to talk to and can ask for reimbursements.

In terms of social effect some argue that Smartphones and mobile

technology might make people less social. During in class breaks or in the metro,

for example, people used to hang out, chat and establish new connections.

Nowadays, in these occasions, it is not rare to see most people using their

Smartphones and not caring as much about the external environment. However,

the sharing economy is bringing it back through technology. Getaround, for

example, involves peer-to-peer sharing and motivates neighbors to use their car

when the owner goes on vacations. With the aid of rating systems, links to

Facebook, and background checks by the intermediate companies, people are

being able to interact more and trust strangers. Tanz (2014) claims that we are

entering a new era of Internet-enabled intimacy: this is not just an economic

breakthrough. It is a cultural one, enabled by a sophisticated series of

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mechanisms, algorithms, and finely calibrated systems of rewards and

punishments.

Delivery of goods is another perspective of the shared economy that has

started to impact many supply chains. Uber and Lyft have recently expanded

their services not only to transport people but to offer delivery for companies,

such as pizza and Chinese restaurants. Meanwhile, companies like Yerdle and

1000tools.com motivate the exchange of unused goods, and this increases the

complexity and importance of the planning of new products’ introduction to the

market. Managers might need to take into consideration how second and third

generation market segments are going to use their products (Ploos van Amstel &

Balm, 2014) because this product life extension requires a product liability

concern. Ploos van Amstel and Balm (2014) claims that the sharing economy will

also have an important impact in supply chains because of the peer to peer

transportation of goods.

4.5 Quality Control

Information transparency and the bidirectional rating system created by

EBay are key factors in maintaining quality in the SE services, and the quality

itself is a key factor for the success of the SE over traditional business models. It

is true that in some markets such as the hotel one, companies like Booking.com

have successfully created rating systems and made it available for users.

However, for the high level of trust required in the SE, the two-way rating system

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provides very valuable and reliable information. That means in the short-term

residential rentals for example, both the hosts and the guest are reviewed by each

other. In ride-sharing, not only is the driver rated by its passengers in regards to

his customer service, safety and overall conditions of the car. In this system the

passenger is rated as well and that is a new phenomenon in business (Frei as

cited in Carmichael, 2015). In that way, both sides have a motivation to act

professionally, be on time and be respectful to each other. This reflects the great

customer service usually experienced from most customers of this kind of service.

If a host or driver keeps receiving low ratings for a certain period of time, the

company has viable reasons and usually chooses to not to work with that host or

driver. The rating system eliminates the few players who act bad and who would

have made everyone else get uncomfortable or scared of dealing with strangers

(Stein, 2014). According to Lyft, 90% of times their drivers are rated 5 stars

(scale of 0 to 5).

Figure 4 (p. 27) shows a Lyft driver that has already given 810 rides and

has averaged 4.9 out of 5 in ratings. It is important to point out that rating the

driver is mandatory for passengers, otherwise they are not able to use the

application again. That means he has approximately 800 ratings, which averaged

almost the maximum possible. If numbers are not enough, there is also the

option to connect through social networks and check a variety of information.

With that said, there is strong evidence that ride-sharing provides more safety

than getting a random taxi on the street.

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Furthermore, since there is an online platform that intermediates

business among its users, everything is traceable, which adds safety to consumers

and micro-entrepreneurs. A study done by Feeney (2015) shows that Uber’s and

Lyft’s background check requirements are in fact stricter than the screening

requirements for many taxi drivers in the US. Housing sharing companies such

as Airbnb and Homeaway also have great satisfaction rates. This happens

because human beings feel more comfortable and act in a better manner when

working directly with other people who “run their business” rather than formal

agents who only represent corporations. Botsman (2013) links the term

humanness to this phenomenon. The human factor plays an important role in

this business model and it is connected by an efficient and sophisticated online

reputation system (Sundararajan, 2012). Sundararajan goes beyond supporting

that government should not regulate the SE because the online reputation

protects buyers and prevents market failure, and “profit is a much more powerful

driver for quality than regulatory compliance”. And this is true for the micro

environment within the platforms. However, as it will be described later with the

focus on the Short-Term Residential Rentals, these uses of Sharing platforms also

affect other stakeholders and then, in regards to that, some kind of regulation is

important. It is true that these safety mechanisms and quality control systems

cannot guarantee 100% results, and it still represents a challenge for the Sharing

Economy companies; however, a lot has been done in this regard and this is also

an interesting research question for other works.

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4.6 Economic Distribution

Across sectors, power is moving from big, centralized institutions to

distributed networks of individuals and communities disrupting who we trust

and how we can access goods and services (Botsman, 2013). Corporate brands

still have strong power in this new era of business but with the aid of the

confidence created by the real time reputation system and third party crowd

source reviews such as Yelp, small local business and the empowered individual

have new tools to succeed and compete against the corporate chains. The

individual who was just a consumer has also become a provider of valuable

skills and services and has the ability to be recognized and create a reputation

by the quality of his work. Economist Thomas Piketty says that main driver of

sustained economic inequality over the past decades has been the concentration

of wealth-producing “capital” in the hands of a few. “This seems less likely if

the economy is powered by millions of micro-entrepreneurs who own their

businesses, rather than a small number of giant corporations” (Sundararajan,

2015).

There is also another point that might have a role on decreasing

inequality. The entry barriers in this new business model for companies who

act as matching platforms and for the sellers is lower compared to tradition

business models (Einav, Farronato & Levin, 2015). Goodwin (2015) helps to

exemplify that when he states that the power of the Internet on a mobile phone

has unleashed a movement that is rapidly changing business models supported

40

by traditional supply chains, and moving power to new places. According to

Goodwin, the fastest-growing companies in history “are indescribably thin

layers that sit on top of vast supply systems (where the costs are)

and interface with a huge number of people (where the money is).” Since the

fundamental nature of SE is to have the effective ability to use inventory that is

already on the market, these companies do not need to own expensive assets in

order to become a player. The consequence is that more people and startups

have bigger chances of operating in this new economy. As it was argued, SE is a

new trend, so the real consequences might take a while until they become a fact.

5 MACRO CONCEPTUAL FRAMEWORK OF THE SHARING ECONOMY

Now one can introduce the Macro conceptual framework of the Sharing

Economy which outlines and groups many of its ideas and principles. These

ideas were found on the literature review as well as part of the contributions of

the author of this thesis. In the bottom there are the new technologies that

enabled the Sharing Economy to flourish such as Mobile Devices, Networking,

Big Data, Self-fed data systems and Effective micro transaction. Then we have

the three elements that constitute the foundation and the three key elements

that are results of the Sharing Economy. In conclusion, we have the four main

benefits of this economy that constitute a variety of business models in different

industries. Among the literature there are authors who debate what the SE is

41

and what companies are part of it. This framework groups the main

characteristics and indicates what makes the SE unique.

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Figure 5 - Macro Conceptual Framework of the Sharing Economy

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The framework (Figure 5) suggests that we are just in the beginning of a

new way of doing business. It helps to brainstorm new ideas of business models

within the Sharing Economy such as alternatives that enable businesses to unlock

and monetize their idling capacity or assets. There is still room for many new

businesses supported by these concepts, not only for new companies but also for

traditional companies, to take advantage of the SE and operate in a hybrid model,

such as the Amazon Case previously explained. An example of how this

framework can be applied to assist brainstorming would be a platform that

transforms a certain company, which is a passive consumer of a good or space,

into a provider. Some organizations are severely affected by seasonality and need

warehousing for a specific period of time. The SE principles such as the “Unlock

and Monetize Idle Capacity” could be used, for instance, to match companies

with opposite seasonality to share warehousing spaces.

To give an example, company “B” receives high quantity of raw material

from China a couple of times a year. The rest of the time their warehouse sits idle

and the managers could not figure out a use for the space. In the same

neighborhood there is a smaller company “C” which has inventory that it needs to

hold, but cannot afford to own a warehouse. Company C does not know about

the company B’s empty warehousing and is not able to take advantage of the

opportunity to buy cheap raw material and store it. This is a potential problem

that could be easily solved by an SE platform, which would use Right

Information at the Right Time as a foundation, which would result in a

Matching Ability of Providers with Wanters. This On Demand Access

instead of Ownership would Unlock and monetize idle capacity that

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clearly has potential benefits for both company B and C as well as the SE platform

that would intermediate the transaction. As a consequence of this win-win

example, all of these benefits are present in the framework. This exemplifies that

the SE is not only restricted to consumer-to-consumer as many people think, but

it also offers a business-to-business window of opportunity that occasionally has

similar desires and constraints and, based on the same foundations, results and

benefits outlined in the framework.

6 TEST BEST BED: APPLYING THE SE QUANTITATIVE MODEL TO THE STRR

The sharing economy is able to optimize everything around the consumer

and eliminates bureaucracy, waste, overhead costs, middleman and facilitating

the transportation of people and goods. The SE values and appreciates the

individual characteristics and uniqueness of humans, while the traditional

economy tends to standardize the worker and associates it with quality. It is true

that there is a lot to be discussed and researched about the SE, some suggestions

of future works have already been made. Prohibiting sharing economy

companies to operate is like prohibiting Smartphones to have GPS because

navigation equipment companies are losing their market share. The open market

is warning old fashioned companies that it is time to innovate and offer better

service. If these kinds of innovation were halted in the past, many of the good

ideas and products that we know and use would not be around today. However,

as the following research explains, some kind of regulation might be of

45

importance to SE industries since market flaws might cause some companies to

grow too much and cause problems to other stakeholders and society.

6.1 Stakeholder Threats and Opportunities Diagram

To explain better the problem originated by the misuse and abuse of

STRR and the conflict of different interests of different stakeholders as

introduced in chapter 2 and 3, a Stakeholder Diagram of Threats and

Opportunities (Figure 6) was created. It was based on the lower part of the

SWOT diagram that describes and divides the main stakeholders affected by the

STRR business model in 2 parts: the stakeholders who are being threatened and

the ones who have opportunities in this industry. The ones being threatened

consist of: Real Estate Brokers, Long Term Residents (such as college students

who usually have one year leases) and Hotels. The stakeholders who have

opportunities consist of landlords, short-term renters (such as tourists who don’t

stay long), and the platforms of STRR such as Airbnb which will be further

studied in this project.

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Figure 6 - Stakeholders Conflict Diagram

6.2 Test Bed Question and Objective

How to know if a particular city is being adversely affected by the misuse

and abuse of short-term residential rental platforms and what variables would

measure and provide insight into this undesirable practice?

The objective is to develop a single indicator to measure how much a

specific city is adhering to best practices of the short-term residential rentals in

the Sharing Economy.

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6.3 Steps of the Method

The following steps were taken in order to develop the analytical tool:

1) company selection;

2) cities selection;

3) variables analyzed per city;

4) validation of variables;

5) data collection and processing;

6) statistical analysis of the data.

6.3.1 Company Selection

Among the large short-term residential companies that operate in the US,

such as Airbnb, Homeaway and Tripadvisor, Airbnb is the being analyzed in this

test bed. This is due to the fact that it is the STRR company with the biggest

market share in the US and it has an open data policy. That means all the

information regarding the listings that are published in their website are

available. Due to the complexity of data extraction, this thesis used information

previously compiled by Insideairbnb.com.

Airbnb was created in 2008 in San Francisco when a large design

conference was taking place. The city did not have enough hotel rooms to hold

the number of people that planned to attend this conference. Two roommates,

Brian and Joe, had the idea to buy airbeds, put them in their living room, and

rent them out to participants of the conference. They did not only provide bed

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and breakfast but also a unique networking opportunity for their guests who were

going to the same conference. The company was then named “Air Bed and

Breakfast” which was shortened and became later Airbnb. Brian described the

experience with the following words: “They booked a place to stay, but they

ended up with something more than just an airbed at a slightly messy apartment.

They learned our favorite places to grab coffee, ate the best tacos in the city, and

had friends to hang out whenever they wanted”. The founders originally focused

their business model on high-profile events where alternative lodging was scarce.

The Figures 7 and Figure 8 show the growth of Airbnb during the last

years.

Figure 7 –Nights Booked in Airbnb

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Figure 8 – Airbnb Listings Growth

6.3.2 Cities Selection

This case study involves thirteen North American cities selected on the

basis of their economic relevance and their use of short-term residential rental

platforms and the availability of public data that has been downloaded and

captured by a third party platform and available for use in this study

(InsideAirbnb). The cities are listed in Figure 9.

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Figure 9 –Cities Selected

6.3.3 Variables Analyzed per City

Utilizing knowledge based on reviews of the literature and interviews,

multiple variables were selected. Others variables of interest were derived

utilizing the interest of different stakeholders shown in the Stakeholder Conflict

Diagram in Figure 6. All variables were created in a way that the lower the

variable, the better it follows the SE principles. Therefore, the lower overall score

a city has (also known as ISEP), the better it follows the SE principles. The ISEP

component variables are listed and explained below.

1) Platform Density: Number of listings/population of the city.

Some cities have too many listings on Airbnb for its size. The ideal

scenario would be to divide the number of listings by the number of

bedrooms vacant in the city. However, this denominator is very hard to

determine. Different sources of data use different methods to estimate

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the number of rooms there are in a city and one could not find a reliable

source that had all the cities in this sample. Therefore, we use a proxy

variable for “number of bedrooms” which consist of “population” which is

a variable easier to be obtained. In appendix 1 there are more

explanations in regards to that.

2) Multiple Listings: Percentage of hosts who have more than one listing

The more listings a host has, the more likely he is renting places as a pure

commercial activity that goes against the rules of the SE. Out of all the

Airbnb hosts in a specific city, this variable shows the percentage of how

many people have more than one listing.

3) Listings per host: Average number of listings per host

As described in Variable 2, some cities are harmed by the STRR because

the hosts are taking advantage of the free lodging taxes in order to make

an easy profit. A high number in variable 3 indicates that some hosts

with many listings might be operating illegal hotels.

4) Top 20: % of total number of listings owned by top 20 hosts

Unfortunately, there are some hosts who are managing more than 30 or

40 listings which clearly goes against the principles of the SE. First this

variable classifies who the hosts with more listings are for each city.

Then, it sums up the number of listings managed by these people and

shows what percentage of the total number of listings of the city as in

appendix 2.

5) Hotel Price Ratio: Average hotel price per night/ Average Airbnb

price per night (for bedroom-only listings)

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As discussed before, one of the main competitors of STRR is the hotel

industry. Airbnb has traditionally captured part of the tourists and

traveler market due to their cheaper fare. This represents a typical

complaint by hotel managers that Airbnb prices are too low and that they

cannot compete. Therefore, this variable captures the relationship

between average hotel prices and average Airbnb prices for each city.

The higher this number is, the bigger the difference between hotel and

Airbnb and the harder it is for hotels to keep their customers. It is

important to keep in mind that the foundation of Index of Sharing

Economy Principles method (ISEP) is to keep balance between the

stakeholders, thus, no one has the ability to push hotels out of business.

Airbnb private bedrooms were used to measure this variable they are the

most comparable to a hotel room. It would be unfair to compare prices of

a whole apartment or house with a hotel bedroom, thus, comparing

bedrooms with bedrooms is the best solution. (For example, compare

Boston with San Diego – both have the same average for Airbnb prices.)

6) Resident Price Ratio: Average Airbnb price per night/Average Long

Term Resident per night

Similar to variable 5, this variable compares average Airbnb prices with

average long-term rental prices. In order to get the average price of long

term rental for one day, the average rent people pay per month was

divided by 30, in order to capture the average daily rent for each city

analyzed. The higher the outcome of this variable, the higher Airbnb

hosts are able to charge in that city. (Keeping in mind that hosts are the

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ones who choose how much they will charge for their space – the Airbnb

corporation keeps a fraction of the overall operation). If Airbnb hosts

are able to charge a high amount, landlords are more likely to shift from

renting long term to short-term, which causes the problems previously

described, such as the increase of rent.

7) Potential Host Income Per Month: Average host income made

through Airbnb in US$

As explained previously, although the Sharing Economy benefits users in

many ways, generating additional income for hosts is still a main driver

for the system to work and motivate more hosts to join. The problem is

when this additional income becomes their primary income and when the

hosts try to maximize it in as many ways as possible. This variable

expresses the average income for hosts who have a unit open for an entire

month in each city, also known as “full time hosts”. The higher this

value, the more interested landlords are to take units out of the

traditional long-term rental and allocate them to tourist rentals in the

short-term.

8) Availability_365: Number of nights available to rent in Airbnb.

Average of the bedrooms/apartments listed for each city.

Each host has the ability to open the list for rentals for a specific period of

time during the year. The principles of the Sharing Economy indicate

that bedrooms or apartments are rented occasionally (for example when

the host is travelling on vacation). Hosts who rent their place for the

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majority of the year are likely to be doing so for solely commercial

purposes.

9) Booked 365: Average number of nights booked per year per listing

Out of the Airbnb hosts, this variable counts the actual average number

of nights that units get rented.

10) Room Type: Number of entire homes (house or apartment)/total

number of listings.

In STRR platforms the host has the possibility to rent a bedroom or the

entire home. As it was explained earlier, based on the principles of the

Sharing Economy and a literary review, the renting of an entire home is

more likely to constitute solely commercial use. Therefore, this variable

expresses the number of entire homes in proportion to the total number

of listings.

6.3.4 Validation of Variables and Further Considerations

In order to validate these variables interviews were conducted with:

Greater Boston Real Estate Board, Bosleton Top Properties, Inside Airbnb, and

independent housing industry specialists. The structure of the interviews was

based on the Critical Incident Technique (1954). In this method, the interviewer

exposes a specific critical problem related to the subject of research in order to

stimulate the start of the interview and its development.

55

Interviews and literature review revealed problems caused by the abuse

and misuse of the STRR. One factor is in regards to neighbors for example.

Tourists have very different habits than residents, thereby provoking noise

complaints from neighbors. Another important factor relates to security,

specifically in buildings and closed condominiums. An Airbnb guest may easily

make and keep a copy of the main entrance key, which may cause security

concerns among neighbors. Figure 6 shows other stakeholders affected by the

misuse of the STRR, like hotels, long term residents and so on.

6.3.5 Data Collection and Processing

A variety of sources were used to collect Data. The main ones are listed

below:

Insideairbnb.com (independent organization which extracts data

directly from Airbnb.com);

academic journals;

newspapers;

Departmentofnumbers.com and other similar sites.

After collecting, the data was filtered, grouped and processed. All the

data for each city was grouped in an Excel Spreadsheet (Appendix 3) and then

formulas were implemented in order to capture the information for each variable

and for each city. Some variables were more straightforward, while others were

used to process the data and get the information that was required in the context

56

of this thesis. Some statistical methods were used such as arithmetic mean,

standard deviation and normalization of the variables which will be further

described.

All the data for each variable and each city is in Table 1. In order to get

some of these data an auxiliary variable table had to be used as shown in Table 2.

These auxiliary variables did not enter directly into the calculation of the ISEP

(Index of Sharing Economy Principles), but they are used to calculate each one of

the 10 variables used to determine the ISEP, as shown in table 1.

Table 1

Cities and Raw Data

57

Table 2

Auxiliary Variable

6.3.6 Analysis

All the data was compiled and statistical techniques were applied which

allowed for the construction of a single index involving the variables previously

presented. Each variable has a different scale with very different means and

different standard deviations. To obtain the arithmetic mean of these different

variables, it is necessary to normalize the data in order to bring them all to the

same context and scale (Montgomery & Runger, 2010).

The method of “Standard Score” (Vogt & Johnson, 2011) was chosen to

be used which consist of

y = 𝑥 − 𝜇

𝜎

y = Standardized value

58

x = value measured

µ = mean

σ = Standard deviation

Therefore, the statistical method generates table 3 (normalized data) with

the same format as the original one (Table 1 - Cities and Raw Data). Note that

now all the variables have means equals to “0” and standard deviations equal to

“1” as expected from the Standard Score method explained above. Only now,

using the normalized data of the Table 3, one can compute the different 10

variables of each city to get the single score ISEP for each city of the sample.

Table 3

Normalized Data

7 DATA ANALYSIS AND RESULTS OF THE TEST BED

7.1 ISEP: Index of Sharing Economy Principles

A couple of different ways were tested in order to obtain the ISEP score

and the method chosen to include all the ten variables was the arithmetic mean of

59

the standardized scores which in fact consist of the index ISEP. Table 4 shows

the results of the all the calculations of ISEP and whether each city has a green,

yellow or red flag associated with it.

A red flag indicates stakeholders are being economically harmed due to

the abuse and misuse of STRR platforms. Yellow indicates that some preventive

measures could be taken but the city and stakeholders are still not being

significantly harmed economically by the spread of STRR. A Green flag indicates

no problem and no significant risks for the stakeholders in regards to the spread

of STRR. The standard threshold factor used was set as half of the standard

deviation, which consists of 0.5 and the cities which got a red flag are Nashville,

New Orleans and Santa Cruz. Following, there is a graph for each city that

informs how it has performed in each one of the ten variables. These graphs

assist city officials to easily identify which variables they need to look into in

order to improve their ISEP score and consequently provide a better balance for

the stakeholders of their city. The lower the ISEP the better the conditions are.

60

Table 4

Normalized Data per City and ISEP

61

Figure 10 - Normalized variables per city

-2,00

-1,00

0,00

1,00

2,00

3,00

1 2 3 4 5 6 7 8 9 10

Boston

-1,00

-0,50

0,00

0,50

1 2 3 4 5 6 7 8 9 10

DC

-2,00

-1,00

0,00

1,00

2,00

1 2 3 4 5 6 7 8 9 10

Seattle

-2,00

-1,00

0,00

1,00

2,00

1 2 3 4 5 6 7 8 9 10

LA

-1,00

0,00

1,00

2,00

1 2 3 4 5 6 7 8 9 10

New Orleans

-4,00

-2,00

0,00

2,00

1 2 3 4 5 6 7 8 9 10

Oakland

-1,00

0,00

1,00

2,00

1 2 3 4 5 6 7 8 9 10

Portland

-2,00

-1,00

0,00

1,00

1 2 3 4 5 6 7 8 9 10

San Diego

62

7.2 Sensitivity Analysis

As it can be observed, the classification of each city depends on the

threshold factor used. In Table 5, a sensitivity analysis is performed changing

three different values of the threshold factor related to the standard deviation

(0.5; 0.3; 0.2) and showing the consequent results.

-3,00

-2,00

-1,00

0,00

1,00

2,00

3,00

1 2 3 4 5 6 7 8 9 10

San Francisco

-1,00

0,00

1,00

2,00

3,00

1 2 3 4 5 6 7 8 9 10

Santa Cruz

-2,00

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

2,00

1 2 3 4 5 6 7 8 9 10

Austin

-1,50

-1,00

-0,50

0,00

0,50

1,00

1 2 3 4 5 6 7 8 9 10

Chicago

63

Table 5

Sensitivity Analysis of Threshold Factor

7.3 Demonstration of Calculations for Boston

We will detail the calculations for one of the cities and use Boston, MA, as

an example. Therefore, for the first variable we have the following calculations.

The change of scale was necessary because otherwise the number resulted would

be very small and it would be hard for anyone to read.

Platform Density:

Total number of listings in Boston = 2558;

Population of Boston = 655,884;

Total number of listings / Population = 0.0039001;

Change scale: 39 (per 10000 people).

64

Table 6

ISEP Boston Calculation Step 1

Table 7

ISEP Boston Calculation Step 2

Table 8

ISEP Boston Calculation Step 3

65

% of hosts with more than one listing = 47%;

No of listings per host;

Process data to get total number of hosts (Table 8);

% of total number of listings owned by top 20 hosts.

Table 9

ISEP Boston Calculation Step 4

66

Table 10

Boston Top 20 Hosts

Table 11

ISEP Boston Calculation Step 5

67

Table 12

ISEP Boston Calculation Step 6

Price Ratio (Airbnb x Hotel);

Hotel room rate = $205.00;

Airbnb price per night = $102;

Price Ratio (Hotel/Airbnb) = 205/102 =2.01;

Price Ratio (Airbnb x Long Term Residents);

Long term price rent per month = $1247;

Price Ratio (Airbnb/Long Term per night) = 102/41.57 = 2.45;

Host Potential income per month;

On average how much “full time hosts” make per listing = $ 1405.

Table 13

ISEP Boston Calculation Step 7

68

Nights Available per year;

Consist of average number of nights listings are available to rent

throughout Airbnb = 225;

% of year that is booked through Airbnb = 30%;

% of listings of entire home or apartment = 56.5%;

A rental on Airbnb might be solely for a room or for the entire home.

7.4 Considerations

The ISEP proves, as it was expected, that some cities have been affected

more than others in regards to the misuse of short-term residential rentals

platforms. This was predicted based on empirical evidence and on articles

published by prestigious newspapers such as The Wall Street Journal and the

New York Times. The main contribution of this method is to provide a way to

measure the impact considering the main stakeholders affected by this new

business model. This tool can be used by city officials, mayors or anyone

attempting to regulate the STRR. It can also be used by the Airbnb platform

since they have interest in continuing to rent in cities. As it will be exemplified a

restriction of parameters contribute to keep a better balance of the stakeholders

affected by STRR platforms.

69

7.5 Recommendations for Cities to Improve ISEP

The example of Nashville will be used to illustrate how city officials can

use this tool to regulate the STRR. Nashville was chosen for this demonstration

because it is one of the cities with one of the worst ISEP in the sample (0.711) and

therefore received a red flag in Table 14. Table 14 shows Nashville’s poor

performance, especially in two variables: % of year listings of entire homes or

apartments (variable 10) and % of total number of listings owned by top 20 hosts

(variable 4). After identifying the variables that could be regulated, one has to

look at Table 15 where there are raw numbers of the variables. Proposition 1 is to

limit variable 4 to 7%. That means the top 20 hosts in the number of listings

could no longer have more than 7% of all listings in this city. This measure would

result in an improvement in ISEP from 0.711 to 0.54. In conclusion, this would

improve the ISEP, however, the city of Nashville would still get a red flag. In

proposition 2 we will perform similar change but now using variable 10. If the

city official could regulate this variable and decrease from 71.8% to 56%, it would

result in an improvement of ISEP from 0.71 to 0.43. This measure by itself would

be enough to take Nashville out of the red flag zone.

Proposition 3 performs both propositions above at the same time. It

results in an improvement of Nashville’s ISEP to 0.27.

70

Table 14

Nashville Proposition Step 1

Table 15

Nashville Proposition Step 2

Table 16

Nashville Proposition Raw Data Step 3

71

7.6 “Number of Penalties” Criteria

The Arithmetic mean of normalized scores method has a drawback of not

requiring cities to perform at a minimum level in each of the variables.

Therefore, a specific city that performs very poor in a couple of variables might

not get a red flag because its overall arithmetic mean is higher than the threshold

factor. Therefore, an alternative criterion created that can be used to define if a

particular city does not have a balance among its stakeholders is the “Number of

Penalties”. On this method, any value above 1.5 would is “not accepted” or

counted as a penalty. It consists of 4 steps described below:

Step A) For each city, count number of variables > 1.5 standard

deviations and characterize it as a “penalty”.

Step B) If number of penalties > 2, city gets a Red Flag.

Step C) If number of penalties = 1, city gets a Yellow Flag.

Step D) If number of penalties = 0, city gets a Green Flag.

The meaning of each flag is still the same as the ISEP method. Green

means that stakeholders are not being economically harmed by the spread of the

STRR platforms. Yellow indicates imbalances among the stakeholders. Red

means that there is not balance among the stakeholders and some of them are

being significantly, economically harmed.

This criterion easily indicates city representative which variable to act or

regulate. Note that Boston is now a Red Flag and Santa Cruz Remains a Red

Flag.

72

Table 17

Number of Penalties per City

7.7 Analysis of ISEP methods

Three methods were developed to evaluate how the city follows the

Sharing Economy Principles:

1) Arithmetic Method: Arithmetic mean of normalized scores;

2) Penalty Method: Penalty selection criteria;

3) Third Method: Consists of a composition of both methods above. First

select cities that are not eliminated by the penalty criteria. The ones that

are eliminated, get a Red Flag immediately. The cities that “pass” the

73

criteria follow the Arithmetic mean of normalized scores and might or

might not get a red flag depending on the score. It consists of these 4

steps:

Step A) RED FLAG for the cities that don’t meet the penalty criteria;

Step B) RED FLAG for cities above the arithmetic mean more the

threshold factor (0.5) which consist of the same requirement as the first

method presented;

Step C) YELLOW FLAG for cities between: mean less threshold factor

and mean plus threshold factor;

Step D) GREEN FLAG for the remaining cities.

Table 18

Analysis of Isep Methods

74

8 Conclusions

Legislators are trying to keep updated with these new kinds of business

models and city officials usually lack a broad knowledge about the subject they do

not know which variables to consider. Uber has been recently banned in

countries such as Spain and Germany, and protests against this kind of service

have been made in a variety of countries. Meanwhile Airbnb has been facing

some challenges in New York legislation; they had to stop working with a variety

of apartment owners because apartments were being rented illegally according to

new regulations recently implemented.

The Sharing Economy has a promising future ahead; it could bring many

benefits for society. Stakeholders and the ISEP can be an alert signal of

imbalances of this system and new business models. ISEP is a valuable

instrument for all so that the SE performs to its full potential. However, the

misuse and abuse of STRR brings problems to cities and to stakeholders. The

Index of Sharing Economy Principles (ISEP) indicates that some cities have been

affected more than others in regards to the misuse or abuses of short-term

residential rental platforms according to predicted empirical evidence (media and

interviews)

Some parameters can be controlled by the platform in a kind of auto

regulation or regulated by city officials in order to keep a better balance of the

stakeholders affected by STRR platforms and to improve the ISEP score. The use

of ISEP serves as a parameter and a tool for governments, communities and

75

stakeholders. The lower it is, the better a particular city follows good practices of

the SE and the better the balance among stakeholders.

Since there were no studies in the literature of the impacts of STRR using

the balance among the stakeholders as a premise, this thesis can be used as a

starting point for further research. For example, in regards to the threshold

factor we set the standard value to 0.5; which consists of half a standard

deviation. However, as next steps, we recommend further research to determine

what the best threshold factor is indicating whether a particular city is a Red Flag,

Yellow or Green Flag especially if more cities are added to the analysis.

As it was explained in section 5.3.4, the variables were validated by

interviews with industry specialists but the whole method was not strictly

validated. Further research could attempt to do this through interviews with a

broader range of stakeholders and by testing this method in cities in other

countries, such as England, which already show evidence of similar problems.

We have also considered the possibilities of assigning weights to variables

and we have asked interviewees about it while performing the step “validation of

the variables”. However, we did not get enough causes to change it. So further

research should address the following questions: “Is it necessary to assign weight

to variables? How should it be done?”. Perhaps a high number of interviews in

regards to the importance of each variable would be a good start.

Is the arithmetic mean the best method to take into consideration the ten

variables analyzed? For example, tests have been done using harmonic mean to

calculate the ISEP. The harmonic mean has the advantage to give a better score

76

to the city that keeps “harmony” among the variables. Other statistical methods

or other kinds of relations among the variables could be tested as well.

A natural way to take this ISEP method further would be to expand

analysis to more cities. In that way cities could be classified according to size as

small/medium/large, and results could be compared for each group. The main

difficulty will be collecting the data from more cities, since it usually involves

complex data mining extraction from websites.

To conclude, a test bed shows the viability of the engineering

management typical concepts, such as balance of supply and demand, mass

production, quality control, and measuring techniques of complex systems,

contribute to the analysis of Sharing Economy challenges. It also created a

diagram of conflicts, a framework and an index (ISEP) as a parameter

methodology for governments, communities and stakeholders to evaluate the SE

and aims to reduce the impact of eventual economic and social problems.

77

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10 APPENDIXES

APPENDIX A - Apartments X Population

The coefficient of variation (CV) is defined as the ratio of the standard deviation

to the mean. As it is seen in the table below Coefficient of variation is <0.1 therefore

population can be used as a proxy variable of Total of Occupied Housing Units.

Table A1

Number of Apartments X Population

85

APPENDIX B - Top 20 Hosts

Table B1

Top 20 Hosts

86

APPENDIX C - Data Spreadsheet of each city

Table C1

San Diego Example