CONTENTS

BACKGROUND ............................................................................................................................................ 1 OBJECTIVES ............................................................................................................................................... 1 METHODOLOGY.......................................................................................................................................... 2 DISCUSSION ................................................................................................................................................ 3 FINDINGS .................................................................................................................................................... 3 DENSITY ..................................................................................................................................................... 4 ESTIMATING TOTAL LODGING UNITS ............................................................................................................. 4 APPENDIX A: INDIVIDUALLY ADVERTISED UNITS AND HOUSING UNITS ......................................... 6 APPENDIX B: INDIVIDUALLY ADVERTISED UNITS AND HOUSING UNITS MAPS ........................... 10 APPENDIX C: STUDY METHODS ............................................................................................................ 15 SELECTING WEBSITES ............................................................................................................................... 15 DEFINITIONS.............................................................................................................................................. 15 DATA ........................................................................................................................................................ 16 Data from Internet Listings .................................................................................................................. 16 Housing Data ...................................................................................................................................... 16 DATA COLLECTION .................................................................................................................................... 16 DATA PROCESSING.................................................................................................................................... 16 DATA CLEANING ........................................................................................................................................ 17 DUPLICATE LISTINGS ................................................................................................................................. 17

LIST OF TABLES TABLE 1: TOTAL NUMBER OF INDIVIDUALLY ADVERTISED UNITS IN 2014............................................................ 3 TABLE 2: DENSITY, RATIO OF INDIVIDUALLY ADVERTISED UNITS TO HOUSING STOCK IN HAWAI‘I, 2014 ............... 4 TABLE 3: NUMBER OF LODGING UNITS IN THE STATE OF HAWAI‘I BY TYPE, 2014 ............................................... 5 TABLE A-1: HAWAI‘I ISLAND: INDIVIDUALLY ADVERTISED UNITS BY ZIP CODE ..................................................... 6 TABLE A-2: KAUA‘I INDIVIDUALLY ADVERTISED UNITS BY ZIP CODE ................................................................... 7 TABLE A-3: MAUI INDIVIDUALLY ADVERTISED UNITS BY ZIP CODE ..................................................................... 8 TABLE A-4: MOLOKA‘I AND LĀNA‘I INDIVIDUALLY ADVERTISED UNITS BY ZIP CODE ............................................. 8 TABLE A-5: O‘AHU INDIVIDUALLY ADVERTISED UNITS BY ZIP CODE ................................................................... 9

LIST OF FIGURES

FIGURE B-1: FIGURE B-2: FIGURE B-3: FIGURE B-4: FIGURE B-5: FIGURE B-6: FIGURE B-7: FIGURE B-8:

HAWAI‘I ISLAND NUMBER OF INDIVIDUALLY ADVERTISED UNITS BY ZIP CODE ............................... 10 HAWAI‘I ISLAND: INDIVIDUALLY ADVERTISED UNITS DENSITY BY ZIP CODE ................................... 11 KAUA‘I NUMBER OF INDIVIDUALLY ADVERTISED UNITS BY ZIP CODE ............................................ 12 KAUA‘I INDIVIDUALLY ADVERTISED UNITS DENSITY BY ZIP CODE ................................................. 12 MAUI COUNTY NUMBER OF INDIVIDUALLY ADVERTISED UNITS BY ZIP CODE ................................. 13 MAUI COUNTY INDIVIDUALLY ADVERTISED UNITS DENSITY BY ZIP CODE ...................................... 13 O‘AHU NUMBER OF INDIVIDUALLY ADVERTISED UNITS BY ZIP CODE ............................................. 14 O‘AHU INDIVIDUALLY ADVERTISED UNITS DENSITY BY ZIP CODE.................................................. 14

BACKGROUND The individual vacation rental market has rapidly evolved in recent years and demand for accommodations beyond the traditional hotel, condominium-hotel, or timeshare rooms, has increased globally. •

Renting out individual condominiums or homes (also known as vacation rentals, individual vacation units, or IVU) has long existed in Hawai‘i.

•

These vacation rental units were a very small part of Hawai‘i’s total lodging supply. Units were marketed using traditional methods such as yellow pages, print advertising, and directories.

•

Internet marketing (individual websites) and internet-based distribution channels which specifically market and book individual vacation units have allowed the demand to rapidly increase.

•

Non-traditional units now represent approximately 25 percent of Hawai‘i’s total visitor unit count.

The Hawai‘i Tourism Authority (HTA) desired to have more information about vacation rentals. The study was conducted by SMS Research in phases at year-end 2013 and between August and September of 2014 to determine the estimated number of vacation rental units statewide being advertised individually on the internet. Information reported here includes estimates of the number of individually advertised units, number of bedrooms, unit capacity, and unit density (individually advertised units per 100 residential housing units). These are reported for the State as a whole, each of the six major islands, and for smaller areas called “cities” which are agglomerations of zip code areas. Finally, results are used to estimate the total number of all types of lodging units in Hawai’i in 2014.

OBJECTIVES Objectives of this study include the following: •

Research the evolving use of visitor accommodations in Hawai‘i;

•

Understand the impact of the internet distribution channels and the shared economy on the availability and use of visitor accommodations;

•

To determine the number of lodging units in Hawai‘i; and

•

Quantify how these changes affect the structure of Hawai‘i’s lodging supply.

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METHODOLOGY This study was designed to develop a reasonable estimate of independently marketed visitor units by examining selected internet marketing sites. A summary of the methods are described below. •

Sites were chosen for its large number of listing, uniqueness of listings between sites, availability of address or zip code data. Websites chosen for the project were: – vacation rentals by Owner (VRBO): http://www.vrbo.com/vacationrentals/usa/hawaii – Clearstay.com: http://www.clearstay.com/Vacation+Rentals/US-Hawaii – TripAdvisor: http://www.tripadvisor.com/hawaii-vacation-rentals.html – Airbnb: https://www.airbnb.com/s/Hawaii--United-States

•

The units identified by this search were referred to as Individually Advertised Units.

•

The number of bedrooms was taken directly from listings on the four websites. In rare cases the number of bedrooms was not given and was estimated from the unit capacity under an assumption of two persons per bedroom.

•

Most listings also included unit capacity, the maximum number of guests that can be accommodated by the unit. In some cases, the listing gave the capacity in terms of the number of persons who can sleep there. That figure could include the use of cots or rollaway beds. In cases where only the number of bedrooms was given, the assumption was made that each bedroom contained two beds and could sleep two people. Because of this latter assumption, the estimate given here may be a conservative one.

•

A preliminary study was conducted at the end of 2013 and a second wave of data collection occurred between mid-August and mid-September 2014. This report mainly focuses on the analysis of the 2014 data.

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DISCUSSION Findings 22,238 Individually Advertised Units in Hawai‘i were identified between mid-August and midSeptember 2014 1. Table 1 shows the number of Individually Advertised Units located on each island. Table 1: Total Number of Individually Advertised Units in 2014

Island Hawai‘i Kaua‘i Lāna‘i Maui Moloka‘i O‘ahu State of Hawai‘i

•

1

11,155 7,466 57 15,113 605 9,103

22,238

43,499

117,607

Total Estimated Number of Bedrooms

Unlike traditional hotel, condominium hotel, and timeshare units, the units identified in this study may be offered and withdrawn for visitor rental at the owners’ discretion and are typically not available for visitor rental on a year-round basis. –

•

4,986 3,614 22 8,840 365 4,411

Estimated Number of Visitors that Could Be Accommodated 28,106 19,481 133 43,877 1,676 24,334

Number of Individually Advertised Units

For example, individual owners may make their units available in response to seasonal demand or withdraw their units from the market during periods of personal use.

22,238 units is the best estimate of the number of Individually Advertised Units in Hawai‘i for 2014. Given the relative precision of the methods used, the estimate should be treated as 20,000 Individually Advertised Units in high season, plus or minus 2,500 units. –

The preliminary study done at end of 2013 yielded 18,998 units

–

The increase in identified units could have resulted from: •

Improved data collection techniques

•

Number of listings might increase during peak summer season

•

Increasing demand has increased listings

This period represents the second phase of fieldwork. Methodology and techniques were refined between phases.

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Density The density for Individually Advertised Units was based on existing residential housing stock rather than an area measure such as square miles. Table 2 presents the number of Individually Advertised Units identified, housing unit counts, and the ratio of identified units to total housing stock on each island. Table 2: Density, Ratio of Individually Advertised Units to Housing Stock in Hawai‘i, 2014

Island Hawai‘i Kaua‘i Lāna‘i Maui Moloka‘i O‘ahu State of Hawai‘i

Total Number of Individually Advertised Units 4,986 3,614 22 8,840 365 4,411 22,238

Total Number of Housing Units* 82,323 28,790 1,545 65,232 3,312 306,622 487,824

Ratio of Individually Advertised Units to Total Housing Units 2 6.1 12.6 1.4 13.6 11.0 1.4 4.6

•

Across the State, there were 4.6 Individually Advertised Units per 100 residential housing units.

•

Maui’s 13.6 units per 100 residential housing units was highest for the State. Kaua‘i’s ratio was 12.6 per 100. O‘ahu and Lāna‘i had the lowest ratio at 1.4 Individually Advertised Units per 100 residential housing units.

The ratio of Individually Advertised Units to housing units by zip code area is presented in Appendix A to this report.

Estimating Total Lodging Units Results of the data collection were compared to the data reported in the 2014 Visitor Plant Inventory Report. The objective was to estimate the total number of all types of lodging units in Hawai’i in 2014. •

Assessment of the data collected in this study indicated that many of the Individually Advertised Units were also included in the 2014 Visitor Plant Inventory as VR-Condo, VR-House, Bed & Breakfast, or as part of a traditional condominium hotel pool.

•

However, not all vacation rentals included in the Visitor Plant Inventory advertise on the selected Internet sites.

2

Ratio is expressed as the number of Individually Advertised Units per 100 residential housing units. Does not include housing units from geographic areas where there were no Individually Advertised Units found.

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•

The methods and definitions used in this study and the 2014 Visitor Plant Inventory are different; however the 22,238 Individually Advertised Units identified in this study were used as an estimate for Vacation Rentals and B&Bs reported in the Visitor Plant Inventory.

•

Table 3 combines these Individually Advertised Units with traditional visitor units reported in the 2014 Visitor Plant Inventory. If all of the identified units were available for visitor use at the same time, these units would account for up to 25 percent of Hawai‘i’s total lodging inventory. Table 3: Number of Lodging Units in the State of Hawai‘i by Type, 2014 Lodging Type Hotel Condo Hotel Timeshare Hostel Apartment Hotel Other Individually Advertised Units (Vacation Rentals) 3 Total

3

Units

% Mix

43,575 10,560 10,647 303 325 393

49.5% 12.0% 12.1% 0.3% 0.4% 0.4%

22,238

25.3%

88,041

100.0%

Assumes all identified units are available for visitor use at the same time.

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APPENDIX A: INDIVIDUALLY ADVERTISED UNITS AND HOUSING UNITS Table A-1: Hawai‘i Island: Individually Advertised Units by Zip Code

Hawai‘i Island City/Area Waikoloa Kailua-Kona

Zip Code

Individually Advertised Units

Housing Units

Individually Advertised Units per 100 Housing Units

96738

946

4421

21.4

96740 / 96739

2166

16843

12.9

Nīnole

96773

10

105

9.5

Volcano

96785

156

1776

8.8

Hōnaunau

96726

21

271

7.8

Kamuela

96743

417

5668

7.4

Hakalau

96710

20

275

7.3

Pāhala

96777

39

575

6.8

Hāwī

96719

43

655

6.6

Pāhoa

96778

397

6685

6.0

Captain Cook

96704

170

2938

5.8

Hōlualoa

96725

55

1469

3.7

Laupāhoehoe

96764

11

357

3.1

Nā‘ālehu

96772

29

1089

2.7

Papaaloa

96780

5

202

2.5

Honoka‘a

96727

43

1857

2.3

Pāpa‘ikou

96781

14

654

2.1

Honomu

96728

5

253

2.0

Kapa‘au

96755

25

1384

1.8

Kea‘au

96749

119

6645

1.8

Pepeekeo

96783

14

789

1.8

Kealakekua

96750

25

1466

1.7

Paauilo

96776

7

607

1.2

202

17770

1.1

Hilo

96720 / 96721

Ookala

96774

1

124

0.8

Ocean-View

96737

19

2450

0.8

Mountain View

96771

23

3660

0.6

Kurtistown

96760

4

1335

0.3

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Table A-2: Kaua‘i Individually Advertised Units by Zip Code

Kaua‘i City/Area

Zip Code

Individually Advertised Units

Housing Units

Individually Advertised Units per 100 Housing Units

Kōloa

96756

1,286

3,247

39.6

Princeville

96722

947

2,464

38.4

Hanalei

96714

355

959

37.0

Anahola

96703

80

899

8.9

Kapa‘a

96746

635

8,134

7.8

Kīlauea

96754

103

1,706

6.0

Kekaha

96752

55

1,382

4.0

Waimea

96796

22

887

2.5

96766 / 96715

97

5,296

1.8

Makaweli

96769

3

185

1.6

Kealia

96751

1

69

1.4

Lāwa‘i

96765

3

210

1.4

Kalāheo

96741

26

2,370

1.1

Hanapēpē

96716

1

982

0.1

Līhu‘e

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Table A-3: Maui Individually Advertised Units by Zip Code

Maui City/Area

Individually Advertised Units

Zip Code

Housing Units

Individually Advertised Units per 100 Housing Units

Lahaina / Kapalua

96761

3,845

11,928

32.2

Kīhei

96753

4,183

18,059

23.2

Pā‘ia

96779

151

1,292

11.7

Hāna

96713

67

964

7.0

Haiku

96708

190

4,394

4.3

Wailuku

96793

332

10,564

3.1

96768 / 96788

47

6,729

0.7

Kula

96790

23

3,664

0.6

Kahului

96732

2

7,638

0.03

Makawao / Pukalani

Table A-4: Moloka‘i and Lāna‘i Individually Advertised Units by Zip Code

Moloka‘i and Lāna‘i City/Area

Zip Code

Individually Advertised Units

Housing Units

Individually Advertised Units per 100 Housing Units

Moloka‘i: Maunaloa

96770

147

757

19.4

Moloka‘i: Kaunakakai

96748

217

2,159

10.1

Moloka‘i: Hoolehua

96729

1

396

0.3

Lānai

96763

22

1,545

1.4

387

4,857

8.0

Moloka‘i and Lāna‘i Combined

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Table A-5: O‘ahu Individually Advertised Units by Zip Code

O‘ahu Zip Code

Individually Advertised Units

Housing Units

Individually Advertised Units per 100 Housing Units

Kahuku

96731

263

1,297

20.3

Hale‘iwa

96712

480

3,028

15.8

Hau‘ula

96717

195

1,826

10.7

Lā‘ie

96762

123

1,188

10.4

Honolulu: Waikīkī

96815

1,725

22,750

7.6

Ka‘a‘awa

96730

35

617

5.7

Waialua

96791

99

2,776

3.6

Kailua

96734

525

16,548

3.2

Kapolei

96707

372

12,461

3.0

Waimānalo

96795

73

2,494

2.9

Wai‘anae

96792

194

13,376

1.4

Honolulu: Hawaiʻi Kai

96825

67

11,592

0.6

Kāne‘ohe

96744

70

17,803

0.4

Honolulu: Kāhala & Kaimukī

96816

60

18,914

0.3

‘Ewa Beach

96706

48

18,319

0.3

Honolulu: Aina Haina & Niu Valley

96821

17

7,295

0.2

Honolulu: Ala Moana

96814

16

11,187

0.1

Honolulu: Downtown

96801 96812 96813

9

10,542

0.1

Honolulu: Mō‘ili‘ili

96826

8

15,948

0.05

Mililani

96789

8

18,650

0.04

Honolulu: Mānoa

96822

8

19,372

0.04

Aiea

96701

4

14,008

0.03

Honolulu: Nu‘uanu

96817

5

20,157

0.02

Honolulu: Moanalua

96819

3

12,399

0.02

Waipahu

96797

3

19,986

0.02

Pearl City

96782

1

12,089

0.01

City/Area

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APPENDIX B: INDIVIDUALLY ADVERTISED UNITS AND HOUSING UNITS MAPS Figure B-1: Hawai‘i Island Number of Individually Advertised Units by Zip Code

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Figure B-2: Hawai‘i Island: Individually Advertised Units Density by Zip Code

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Figure B-3: Kaua‘i Number of Individually Advertised Units by Zip Code

Figure B-4: Kaua‘i Individually Advertised Units Density by Zip Code

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Figure B-5: Maui County Number of Individually Advertised Units by Zip Code

Figure B-6: Maui County Individually Advertised Units Density by Zip Code

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Figure B-7: O‘ahu Number of Individually Advertised Units by Zip Code

Figure B-8: O‘ahu Individually Advertised Units Density by Zip Code

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APPENDIX C: STUDY METHODS Selecting Websites Internet websites that list vacation rentals were identified and evaluated for their effectiveness for this study. Websites chosen for the project were: • • • •

vacation rentals by Owner (VRBO): http://www.vrbo.com/vacationrentals/usa/hawaii Clearstay.com: http://www.clearstay.com/Vacation+Rent als/US-Hawaii TripAdvisor: http://www.tripadvisor.com/hawaiivacation-rentals.html Airbnb: https://www.airbnb.com/s/Hawaii-United-States

The VRBO site was the most comprehensive site in that it has a greater number of listings than any other site. VRBO is owned by the Homeaway and the two lists had a very large number of the same listings. We eliminated the Homeaway site from our study due to this duplication. TripAdvisor had about half the number of listings as VRBO. Clearstay.com and Airbnb each had about 1,000 listings for units in Hawai‘i. Clearstay.com and Airbnb were selected primarily because they did not have a high degree of overlap with VRBO, and because they included important data that was not available from other websites. Clearstay.com, for example, lists the address of most properties. Airbnb listings included zip codes for all listings in 2013. That information was not available in 2014, but we decided to use Airbnb to maintain comparable methods for the two phases of fieldwork.

Definitions Preparation for this report revealed that most of the studies of vacation rentals in

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Hawai‘i have used different definitions of terms. This stems from the fact that the authors had different objectives, used different methods, and applied their methods different data sources in each study 4. While this is understandable, it makes comparing results of different studies problematic. We note below the definitions used for this study with comments on how those definitions compare to the primary study available at this time, the VPI. •

Visitor Destination Area (VDA): A geographic area with significant visitor accommodations infrastructure, such as Waikīkī on O‘ahu. The term VDA is used on some islands as an official land use classification and those classifications were applied where available. Where they were not available, the district classifications for VPI were used.

•

Individually Advertised Unit. Any housing unit listed for short-term rental on the Internet sites selected for this study. Individually Advertised Units include individual condominium units, houses, villas, cottages, townhouses, apartments, bungalows, studios, B&B rentals, rooms in residential housing units, and non-standard units such as tents, converted lanais and garages. Definitions used by the four websites for vacation rentals and B&Bs vary from definitions used in the VPI. There were no units advertised on the four websites

4

Note in particular the last several years of the HTA Visitor Plant Inventory for definitions and changes in definitions. See also, The Kauaian Institute (2005) Transient vacation rentals on O‘ahu: A Comparative Analysis of the Geographic and Economic Footprint, September 2005; The Kauaian Institute (2005) Transient vacation rentals on Maui: A Comparative Analysis of the Geographic and Economic Footprint, August 2005; The Kauaian Institute (2004) Transient vacation rentals on Kaua‘i: A Comparative Analysis of the Geographic and Economic Footprint, January 2004.

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that were identified as apartment hotel, hostel, or “other” units as defined in VPI.

Data Data from Internet Listings For each unit listed in the four websites we reviewed, we attempted to gather the information listed below. Items that were required are marked with an asterisk. Listing that did not include the required items were not included. There were very few omissions. • Website Name* • Listing Identification Number* • Listing Name and/or Description* • Island Location* • Location Indicators: o Area or City* o Street Address (if available) o Zip Code (if available) • Listing Characteristics (when published): o Number of Bedrooms o Number of Bathrooms o Number of Guests that can be accommodated o Rates Most of the items listed above are selfexplanatory and suited to the task at hand. Much depends on the quality of the listing name or description. For some listings the content may be as clear as “Fairways at Maui, oceanfront, condominium hotel unit.” For others, the information may be powerful advertising, but less useful for our research. Housing Data The number of housing units in each area and for each island was obtained from the U.S. Census Bureau’s 2010 Census data.

Data Collection Unit data were obtained directly from listings on each of the four websites. Data were collected electronically using custom-written

programs 5 that mined or scraped html pages on each website, gathering unit characteristics data into subfiles. The data were gathered sequentially by website, island, and region within island (city, village, VDA, or zip code). The raw data were written to Excel worksheets for processing.

Data Processing Data gathered from websites were first arranged in a common format. Separate software was developed for each website to eliminate duplicate entries within the site data. A geographic locator in each file based on whichever data were available at that site -- city, street address, zip code or other information. A unit capacity measure (the maximum number of people that could be accommodated by a given unit) was also generated for each file. Shared assumptions for this process were that there was at least one bedroom per unit listed and that each bedroom had two beds or could accommodate at least two people unless otherwise specified. Regardless of the information supplied by each listing, these assumptions allowed us to calculate a comparable unit capacity for all units. Other programs were written for individual files as they were needed for cleaning, extracting information from larger fields, or other utilities. A sample of the listings obtained where checked manually by SMS professionals in order to confirm that the data obtained electronically matched the information published for the listings. This also served to validate the scripts developed to pull the data from the listing web pages. The manual check consists of a person using a web browser to navigate to the URL of a given listing and then confirming that the 5

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Most routines were written in PERL, some in SEQL.

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details found match the data that was obtained programmatically. A sample of listings identified as duplicates were also checked manually to confirm that they were in fact duplicates in order to validate the process of finding and eliminating duplicates. Finally, data from each of the four websites were merged and a sequential unit identification number was generated for each case. Initial identification numbers from the original files were maintained.

Data Cleaning In most cases, the exact location of the unit or property listed was unavailable. Some records included a ‘neighborhood’, local area, or community name and some listed a city or village name. Only one site, Clearstay.com, included an exact address for its listings. During data cleaning the geographic indicator was standardized across all sources using a list of cities recognized by the USPS and their assigned zip codes. If the listed city or area did not match a city with a zip code, then the closest city with an assigned zip code was assigned as the standardized location. When the location data extracted from the website was insufficient to identify a geographic location, the listing page was visited to determine the location based on descriptions, maps, or images. Listings on the Airbnb website included GPS coordinates, which were used to obtain the zip code which could be translated into island and city codes.

website, and enter the relevant data to the master data file.

Duplicate Listings Duplicate listings were identified by comparing case identifiers within a given geography. Within each of the four component files, it was possible to identify duplicates by their ID numbers. In each of the four files we compared ID numbers and “listing names” of two records. The listing name is a property name or short descriptive phrase or sentence describing the rental unit. For example, if in the VRBO site two listings had the same name and listing ID, then they were considered duplicates. Duplicate records were removed and saved for further analysis. A sample of duplicate listings was manually reviewed in a browser to confirm that they were in fact listings for the same unit. In the larger file, we compared the “listing names” and other information for two records. Across records from all four sources/websites, the identification of duplicate listings was conducted by island using the listing name. When needed additional details of the listings were used such as number of bedrooms and bathrooms. Using these methods, more than 1,500 duplicate listings were identified and removed from the database.

Similar processes were applied to important variables such as the capacity indicator and unit type (VR-condo, VR-house, or B&B). Raw data extracted from the websites is used to develop an indicator that works for all cases. In cases where the raw data are insufficient to generate a comparable value for the variable under consideration, staff members get the listing URL, visit the

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