Data > Data Processing

Data Processing

In the lodging industry, the asymmetry of information deepens, bringing up the level of risks, while supply is continuously diversifying and demand is becoming more volatile. The best way to handle this growing level of uncertainties is to secure as many options as possible. Data is the most efficient tool to excavate options.
1. Value of Data
Lobin's comprehensive lodging database aims to help individual lodging establishments respond effectively to lodging market risks through data-driven decisions. In a dynamically changing lodging market, data holds the following significance:
  • In the lodging market, the asymmetry between inelastic supply and elastic demand amplifies the volatility of cash flow in both short and long terms. To respond effectively to such volatility, comprehensive data that provides a full visibility into the market is essential.
  • As lodging establishments diversify, competition is no longer limited to the type of lodging establishments. Increasingly, different types of establishments are competing for the same demand, making a comprehensive visibility into the entire market more critical.
2. Collecting Data
Lobin's comprehensive lodging database is based on actual data available. It is primarily composed of domestic and international statistical data that can be collected and utilized in relation to the lodging market of Korea. The database includes the following types of statistical data:
Category
Source
Supply
Property
Building Ledger, Building Permits Ledger, Property Records
Enterprise
Business Registration
Establishment
Lodging Business Ledger, Tourist Lodging Business Ledger, Rural Minbak Ledger, Urban Minbak Ledger
Demand
Domestic
Domestic Travel Survey (2005-), Hotel Operating Statistics (2005-)
International
International Travel Survey (2005-), Hotel Operating Statistics (2005-)
Financial
Revenue
Economic Cencus (MDIS, 2010, 2015), Service Industry Survey (MDIS, 2005), Hotel Operating Statistics (2005-)
Profitability
Economic Cencus (MDIS, 2010, 2015), Service Industry Survey (MDIS, 2005), Financial Statement Analysis (2005-)
Others
Service Industry Survey (MDIS, 2005), Financial Statement Analysis (2005-), Tourism Business Statistics (2005-2009)
3. Processing Data
However, due to the differences in scope and format among the various actual data collected, the data is processed according to consistent standards. The goal is to ensure that the data can be used for decision-making related to lodging business management and lodging property investment, based on the following principles:
  • Lobin aims to achieve complete visibility of the entire lodging market. While various types of lodging establishments are competing, visibility into the competitive environment is limited due to imbalanced availability of data. We work to fill in the blind spots of the lodging market.
  • Lobin aims to achieve visibility over long-term changes in the lodging market. The most reliable clue to addressing growing uncertainty is the pattern of past volatility. We work to reduce uncertainty by meticulously analyzing long-term time-series patterns.
  • Lobin's data covers the entire value chain of the lodging industry. To address the volatility of cash flow in the lodging industry, a seamless financial management system is essential. We work to provide comprehensive data, including revenues, expenses, and financial status.
  • Lobin's data is based on individual lodging establishments. Lodging market data can only be meaningful when it is actively utilized by individual establishments. We work to enhance data usability at the individual lodging establishment level.
4. Correcting and Estimating
In addition, Lobin corrects errors caused by discrepancies in items or figures among various actual data available, and estimates missing values. Particularly, we estimate missing values using an algorithm developed by ourselves to captures regional and type-specific cyclicality and seasonality.
Field
Details
Correction
Subject
Demand & financial data with different values for the same item
Method
1) Identify independent variables and derive relevant fonctions
2) Independent variable error: replace with the value in confirmed statistics
3) Relevant function error: correct through history & benchmarking analyses
Standard
Correct sellable unit values by establishment and reflect sales volume
Validation
Compare with the sum in the confirmed statistics (same sample, 95% confidence level)
Estimation
Subject
Detailed items of demand & financial data with values missed
Method
1) Identify independent variables for the data item
2) Derive functions for cyclicality and seasonality by region and type
3) Estimate missing value through history & benchmarking analyses
Standard
Correct sellable unit values by establishment and reflect sales volume
Validation
Compare with the sum in the confirmed statistics (same sample, 95% confidence level)
* The history analysis refers to a comparative analysis against previous indicators of the establishment itself, and the benchmarking analysis refers to a comparative analysis against recent indicators of competitive establishments.

Lobin Co. | Business Registration #: 493-86-02766 | Mail-order Reporting #: 2023-Seoul-Seongdong-0187 | Email: project@lobin.co | Phone: +82-2-6403-9423 | Fax: +82-2-6008-9420 | Address: #4121 SOL623 Bldg, 25 Sangwon 1-gil, Seongdong-gu, Seoul | Hosting: AWS | FTC Verification

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You can obtain a comprehensive view over the competitive environment of lodging markets in Korea.

2024 access fee is USD 528 USD 176(VAT included), for access to the Member Dashboard for a year until December 31, 2024.

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Data Source

  • GDP: GDP, Current $US (World Bank Open Data)
  • Establishments: Compendium of Tourism Statistics (UNWTO), Lodging Business Ledger (MOIS)
  • Rooms: Compendium of Tourism Statistics (UNWTO), Lodging Business Ledger (MOIS)
  • Lodging GDP: Value Added by Industry (BEA), National Accounts (Cabinet Office), GDP of Indonesia (BPS), GDP & GNI by Sector (BOK), Economic Census (KOSIS)
  • Period: 2017-2021

※ In Korea, general & residential accommodations are included while rural & urban minbaks are excluded. Comparable countries are selected based upon availability of lodging GDP statistics for all types of accommodations.

Data Source

  • Population: Population, Total (World Bank Open Data)
  • GDP: GDP, Current $US (World Bank Open Data)
  • Rooms: Compendium of Tourism Statistics (UNWTO), Lodging Business Ledger (MOIS)
  • Lodging GDP: Value Added by Industry (BEA), National Accounts (Cabinet Office), GDP of Indonesia (BPS), GDP & GNI by Sector (BOK), Economic Census (KOSIS)
  • Period: 2017-2021

※ In Korea, general & residential accommodations are included while rural & urban minbaks are excluded. Comparable countries are selected based upon availability of lodging GDP statistics for all types of accommodations.

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Data Source

  • Korea: Lodging Business Ledger (MOIS), Tourist Accommodation Ledger (MCST)
  • USA: Census Database (STR)
  • Period: As at the end of 2021

※ General & residential accommodations other than rural and urban minbaks asre included for Korea. Life cycle was calculated as of December 31, 2021 or actual closure date. If there exists a discrepancy between data sources for an establishment, the discrepancy was settled through an algorithm before use.

Data Source

  • Korea: Lodging Business Ledger (MOIS), Tourist Accommodation Ledger (MCST), Economic Census (KOSIS), Hotel Operating Statistics (KHA), DART (FSS), Trends Report (STR)
  • USA: Compendium of Tourism Statistics (UNWTO), Census Database (STR), Trends Report (STR)
  • Period: 2005-2021

※ General & residential accommodations other than rural and urban minbaks asre included for Korea. Visibility was calculated as the number of establishments for which revenue data is available divided by the total number of establishments. If there exists a discrepancy between data sources for an establishment, the discrepancy was settled through an algorithm before use.

Data Source

  • Guests(Korea): Domestic Traveler Survey (MCST), International Traveler Survey (MCST), Hotel Operating Statistics (KHA)
  • Rooms(Korea): Lodging Business Ledger (MOIS), Tourist Accommodation Ledger (MCST)
  • Guests(USA): Compendium of Tourism Statistics (UNWTO), Trends Report (STR)
  • Rooms(USA): Compendium of Tourism Statistics (UNWTO), Census Database (STR)
  • Period: 2005-2020

※ General & residential accommodations other than rural and urban minbaks asre included for Korea. If there exists a discrepancy between data sources for an establishment, the discrepancy was settled through an algorithm before use.